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Pipeline run

6dcdb737-93c5-4641-b093-a2ce8ccbbe4b

Pipeline LLM cost (USD)
API 1: $0.0054 API 2: $0.0005 API 3: $0.0000 Total: $0.0058

Client output enrichment

v2 Skill cluster · Nature of work · AI index · Tech stack maturity · Evidence · KRA description
Nature of work · Cloud Data Platform Operations & Advisory
Day to day, you’ll run and improve cloud data platforms across streaming, lakes, warehouses, and analytics; troubleshoot, tune SQL/performance, automate repetitive work, and guide customers/engineers through delivery of Epics and technical best practices.
""Operate and manage modern data platforms - from streaming, to data lakes, to analytics""
Tech stack maturity
Modern Cloud Native
The skill set centers on cloud data platforms and services such as AWS, Azure, Amazon Redshift, and Snowflake, which are characteristic of modern cloud-native architectures.
AI index (0 = no AI use, 5 = totally AI-dependent · v2.1)
0.00 / 5
· Title match
· Has AI skill
· AI skill (primary)
· AI skill (secondary)
· On AI team
· Builds AI products
vocab breakdown (legacy)
Assistants (×1):
Frameworks (×2):
Models / concepts (×3):
Evidence — skills matched in JD (37)
SQL Snowflake Amazon Redshift AWS Azure Google Cloud Platform Hadoop Databricks Amazon S3 Azure Data Lake Storage HDFS Google Cloud Storage Kudu Elasticsearch Solr Cassandra Apache Spark Apache Kafka StreamSets Matillion Fivetran HVR Apache NiFi AWS Database Migration Service Azure Data Factory +12
Skill cluster (6 dimension groups, role-scoped)
Cloud Provider Platforms
AWS Azure Google Cloud Platform
Cloud Storage and Data Services
Amazon S3 Google Cloud Storage Cassandra
Infrastructure as Code
Terraform CloudFormation
Python Programming
Python
Search and Content Discovery
Elasticsearch
Cross-cutting / unaligned
SQL Snowflake Amazon Redshift Hadoop Databricks Azure Data Lake Storage HDFS Kudu Solr Apache Spark Apache Kafka StreamSets Matillion Fivetran HVR Apache NiFi AWS Database Migration Service Azure Data Factory Google Cloud Dataproc Apache Airflow AWS Managed Workflows for Apache Airflow Luigi CI/CD Bitbucket GitHub Flyway Liquibase
Show KRA description ↓
• Operate and manage modern data platforms - from streaming, to data lakes, to analytics, and beyond across a progressively evolving technical stack. • Demonstrate clear ownership of multiple simultaneous customer accounts across a variety of technical stacks and act as a technical leader. • Proactively identify and articulate best practices and improvements to customers on how to best leverage their Cloud Data Platform. • Skillfully navigate complex customer environments and build Epics, Stories, and Tasks in order to mature and improve our customer’s data platform. Delegate to and coach Engineers and ensure successful and timely delivery of these Epics. • Provide thought leadership by recommending the right technologies and approaches to maturing and solving problems to help ensure performance, security, scalability, and user satisfaction. • Continually hunt for ways to automate and optimize existing processes. • Strong communication skills in English • Strong working knowledge of SQL and the ability to write, debug, and optimize SQL queries • Extensive experience in providing architectural guidance and operational support across a large user base for a cloud-native data warehouse (Snowflake and/or Redshift) • Expertise with cloud-native data technologies in AWS or Azure • Production support experience in large enterprise environment supporting cloud data platforms and a variety of cloud data technologies (AWS Services/S3, Aurora Athena Redshift, Sagemaker, Azure DevOps/SQL, Azure Data Factory, etc..... • Proven experience learning new technology stacks and training team members • Professional track record of creating, challenging, and improving processes and procedures • Strong troubleshooting and performance tuning skills including database administration • Create and deliver detailed presentations • Client-facing written and verbal communication skills and experience • Production / database administration experience and certifications in core data platforms such as Snowflake, AWS, Azure, GCP, Hadoop, or Databricks • Production experience working with Cloud and Distributed Data Storage technologies such as S3, ADLS, HDFS, GCS, Kudu, ElasticSearch/Solr, Cassandra or other NoSQL storage systems • Production experience working with Data integration technologies such as Spark, Kafka, event/streaming, Streamsets, Matillion, Fivetran, HVR, NiFi, AWS Data Migration Services, Azure DataFactory Google DataProc or others • Production experience working with Workflow Management and Orchestration such as Airflow, AWS Managed Airflow, Luigi, NiFi • Working experience with infrastructure as code using Terraform or Cloud Formation • Strong expertise of scripting language to automate repetitive tasks (preferred Python) • Well versed with continuous integration and deployment frameworks with hands-on experience using CI/CD tools like Bitbucket, Github, Flyway, Liquibase • Experience as a technical team lead

Signals

Skill data-engineer
1.00
Alias cloud-architect
1.00
KRA data-engineer
0.62
Status: completed Created: 2026-05-27T15:18:28.382365Z Updated: 2026-06-12T16:40:28.623834Z API 3 duration: 125655 ms
Flow Current 3-step pipeline

1 POST /skills/extract-from-jd

2 POST /skills/extract-details

3 POST /skills/final-role-output

Role Chosen role & resolution

Cloud Architect

Cloud Solutions Architect

sub-role · 0.95 CASE A

slug: cloud-architect · id: 9 · source: db · sub-role slug: cloud-solutions-architect

Multi-alias tie (3 roles at 1.0) resolved by TIER_B_TITLE: Cloud Solutions Architect

Resolution: in_db — role exists in library; skill↔dim and role↔dim links saved when applicable.

0
New skills
0
Skill↔dim saved
0
Role↔dim saved
2
Skipped

Job description

Join phData, a dynamic and innovative leader in the modern data stack. We partner with major cloud data platforms like Snowflake, AWS, Azure, GCP, Fivetran, and dbt to deliver cutting-edge services and solutions. We're committed to helping global enterprises overcome their toughest data challenges. Even though we're growing extremely fast, we maintain a casual, exciting work environment. We hire top performers and allow you the autonomy to deliver results.


• 4x Snowflake Partner of the Year (2020, 2021, 2022, 2023)
• #1 Partner in Snowflake Advanced Certifications
• 600+ Expert Cloud Certifications (Fivetran, dbt, Sigma Award Winners)
• 7x Best Places to Work
• Inc 5000 Fastest Growing US Companies (2020-2023)


Fueled by talented Data Scientists, Engineers, Architects, and innovative thought leaders, phData is shaping the future of data. We're looking for Solutions Architects to join our Elastic Operations team to serve as hands-on technical leaders to help customers operate and manage their next generation cloud data platforms.


Responsibilities:
• Operate and manage modern data platforms - from streaming, to data lakes, to analytics, and beyond across a progressively evolving technical stack.
• Demonstrate clear ownership of multiple simultaneous customer accounts across a variety of technical stacks and act as a technical leader.
• Proactively identify and articulate best practices and improvements to customers on how to best leverage their Cloud Data Platform.
• Skillfully navigate complex customer environments and build Epics, Stories, and Tasks in order to mature and improve our customer’s data platform. Delegate to and coach Engineers and ensure successful and timely delivery of these Epics.
• Provide thought leadership by recommending the right technologies and approaches to maturing and solving problems to help ensure performance, security, scalability, and user satisfaction.
• Continually hunt for ways to automate and optimize existing processes.


Required Experience:
• Strong communication skills in English
• Strong working knowledge of SQL and the ability to write, debug, and optimize SQL queries
• Extensive experience in providing architectural guidance and operational support across a large user base for a cloud-native data warehouse (Snowflake and/or Redshift)
• Expertise with cloud-native data technologies in AWS or Azure
• Production support experience in large enterprise environment supporting cloud data platforms and a variety of cloud data technologies (AWS Services/S3, Aurora Athena Redshift, Sagemaker, Azure DevOps/SQL, Azure Data Factory, etc.....
• Proven experience learning new technology stacks and training team members
• Professional track record of creating, challenging, and improving processes and procedures
• Strong troubleshooting and performance tuning skills including database administration
• Create and deliver detailed presentations
• Client-facing written and verbal communication skills and experience


Preferred Experience: 
• Production / database administration experience and certifications in core data platforms such as Snowflake, AWS, Azure, GCP, Hadoop, or Databricks
• Production experience working with Cloud and Distributed Data Storage technologies such as S3, ADLS, HDFS, GCS, Kudu, ElasticSearch/Solr, Cassandra or other NoSQL storage systems
• Production experience working with Data integration technologies such as Spark, Kafka, event/streaming, Streamsets, Matillion, Fivetran, HVR, NiFi, AWS Data Migration Services, Azure DataFactory Google DataProc or others
• Production experience working with Workflow Management and Orchestration such as Airflow, AWS Managed Airflow, Luigi, NiFi
• Working experience with infrastructure as code using Terraform or Cloud Formation
• Strong expertise of scripting language to automate repetitive tasks (preferred Python)
• Well versed with continuous integration and deployment frameworks with hands-on experience using CI/CD tools like Bitbucket, Github, Flyway, Liquibase
• Experience as a technical team lead
• Bachelor's degree in Computer Science or a related field


 Why phData? We offer:
• Remote-First Workplace
• Medical Insurance for Self & Family
• Medical Insurance for Parents
• Term Life & Personal Accident
• Wellness Allowance
• Broadband Reimbursement
• 2-4 week bootcamp and provide continuous learning opportunities to enhance your skills and expertise
• Other perks include paid certifications, professional development allowance and additional compensation for creating company-approved content (dashboards, blogs, videos, whitepapers, etc.)

Skills from this JD

Each row merges API 1 extraction, API 2 library match / v3 orchestration (dimensions + locked dims), and API 3 persistence tags.

SQL Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: SQL id=101 · sql

Aliases — catalog

  • SQL (CANONICAL) primary

Context tags (catalog)

ACID CTE DDL DML ETL JOIN MySQL NoSQL OLAP ORM PostgreSQL SQL injection SQLite T-SQL data modeling data warehousing database normalization execution plan indexing joins normalization query optimization stored procedures subquery transaction isolation transaction management window functions

Stored enrichment (catalog DB)

Category
Language
Sub-category
Query Language
Vendor
ANSI
License
unknown
Year introduced
1974
Confidence
0.99
Version strategy
NOT_APPLICABLE

Maturity reasoning: SQL appears in a large share of data, backend, and analytics job descriptions and remains the default query language for PostgreSQL, MySQL, and cloud warehouses like Snowflake/BigQuery.

Skill profile (library / DB)

Skill nature
LANGUAGE
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
6
Sub-category id
97
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Pega Programming Languages & DSLs Catalog dimension db id 267

    Library dimension (catalog)

    Roles linked in library: Pega Developer

  • Programming Languages & DSLs Catalog dimension db id 475

    Library dimension (catalog)

    Roles linked in library: Engineering Manager

  • Programming Languages for Data Work Catalog dimension db id 21

    Library dimension (catalog)

    Roles linked in library: Data Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Pega Programming Languages & DSLs
pega-programming-languages-dsls
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Programming Languages & DSLs
programming-languages-dsls
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Programming Languages for Data Work
programming-languages-for-data-work
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Snowflake Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Snowflake id=105 · snowflake

Aliases — catalog

  • Snowflake (CANONICAL) primary

Context tags (catalog)

ELT ETL SQL Snowpark Snowpipe Streams Tasks Time Travel VARIANT data sharing data warehouse dbt semi-structured data virtual warehouse zero-copy cloning

Stored enrichment (catalog DB)

Category
Platform
Sub-category
Data Cloud Platform
Vendor
Snowflake Inc.
License
proprietary
Year introduced
2012
Confidence
0.98
Version strategy
NOT_APPLICABLE

Maturity reasoning: Snowflake appears frequently in data/analytics job postings and is a standard cloud data warehouse platform alongside BigQuery and Redshift.

Skill profile (library / DB)

Skill nature
PLATFORM
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
9
Sub-category id
113
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Cloud Data Warehouses Catalog dimension db id 22

    Library dimension (catalog)

    Roles linked in library: Data Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Cloud Data Warehouses
cloud-data-warehouses
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Amazon Redshift Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Amazon Redshift id=107 · amazon-redshift

Aliases — catalog

  • Amazon Redshift (CANONICAL) primary

Context tags (catalog)

AWS Glue Amazon S3 BI COPY command ELT ETL JDBC ODBC RA3 SQL Spectrum analytics data warehouse distribution key sort key

Stored enrichment (catalog DB)

Category
Service
Sub-category
Data Warehouse Service
Vendor
Amazon Web Services
License
proprietary
Year introduced
2012
Confidence
0.97
Version strategy
NOT_APPLICABLE

Maturity reasoning: Commonly listed in data/analytics job descriptions and widely used as AWS’s managed warehouse; strong vendor adoption and steady JD volume signal broad market demand.

Skill profile (library / DB)

Skill nature
CLOUD_SERVICE
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
11
Sub-category id
118
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Cloud Data Warehouses Catalog dimension db id 22

    Library dimension (catalog)

    Roles linked in library: Data Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Cloud Data Warehouses
cloud-data-warehouses
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
AWS Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: AWS id=187 · aws

Aliases — catalog

  • AWS (CANONICAL) primary

Context tags (catalog)

API Gateway AWS CLI Auto Scaling CloudFormation CloudFront CloudTrail CloudWatch Cognito DynamoDB EC2 ECS EKS Elastic Beanstalk Elastic Load Balancing IAM KMS Lambda RDS Route 53 S3 SNS SQS Serverless VPC

Stored enrichment (catalog DB)

Category
Platform
Sub-category
Cloud Platform
Vendor
Amazon
License
other_open
Year introduced
2006
Confidence
0.99
Version strategy
NOT_APPLICABLE

Maturity reasoning: AWS is a hiring-pipeline staple: it appears in a large share of cloud/DevOps job descriptions and dominates public cloud market share, with broad certification and vendor ecosystem support.

Skill profile (library / DB)

Skill nature
PLATFORM
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
9
Sub-category id
46
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Cloud Platforms Catalog dimension db id 20

    Library dimension (catalog)

    Roles linked in library: .NET Backend Developer, Backend Developer, Cyber Security Engineer, Data Engineer, DevOps Engineer, Fullstack Developer, Go Backend Developer, Java Backend Developer, Kotlin Backend Developer, ML Engineer, MLOps Engineer, Node.js Backend Developer, Python Backend Developer, Scala Backend Developer

  • Cloud Platforms for AI Deployment Catalog dimension db id 211

    Library dimension (catalog)

    Roles linked in library: AI Engineer

  • Cloud Provider Platforms Catalog dimension db id 131

    Library dimension (catalog)

    Roles linked in library: Cloud Architect, Cloud Security Engineer

  • Cloud Security Posture Tools Catalog dimension db id 64

    Library dimension (catalog)

    Roles linked in library: Cloud Security Engineer, Cyber Security Engineer

  • Vendor Product Families Catalog dimension db id 477

    Library dimension (catalog)

    Roles linked in library: Engineering Manager

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Cloud Platforms
cloud-platforms
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Cloud Platforms for AI Deployment
cloud-platforms-for-ai-deployment
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Cloud Provider Platforms
cloud-provider-platforms
Existing dimension (library) · Role↔dimension saved
Cloud Security Posture Tools
cloud-security-posture-tools
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Vendor Product Families
vendor-product-families
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Azure Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Azure id=188 · azure

Aliases — catalog

  • Azure (CANONICAL) primary

Context tags (catalog)

AKS ARM templates App Service Azure AD Azure Active Directory Azure App Service Azure Blob Azure Blob Storage Azure Cognitive Services Azure Cosmos DB Azure DevOps Azure DevTest Labs Azure Functions Azure Kubernetes Service Azure Logic Apps Azure Monitor Azure Networking Azure Resource Manager Azure SQL Azure SQL Database Azure Security Center Azure Storage Azure Storage Explorer Azure Virtual Machines Bicep Blob Storage Cloud Services Cosmos DB Entra ID Functions Infrastructure as Code Key Vault Log Analytics Logic Apps Resource Groups Serverless Computing Service Bus Storage Account Terraform Virtual Machines

Stored enrichment (catalog DB)

Category
Platform
Sub-category
Cloud Platform
Vendor
Microsoft
License
proprietary
Year introduced
2010
Confidence
0.99
Version strategy
NOT_APPLICABLE

Maturity reasoning: Azure is broadly adopted and frequently appears in cloud/platform job descriptions alongside AWS and GCP; Microsoft’s ongoing enterprise investment and Azure certification demand signal strong hiring-pipeline relevance.

Skill profile (library / DB)

Skill nature
PLATFORM
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
9
Sub-category id
46
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Cloud Platforms Catalog dimension db id 20

    Library dimension (catalog)

    Roles linked in library: .NET Backend Developer, Backend Developer, Cyber Security Engineer, Data Engineer, DevOps Engineer, Fullstack Developer, Go Backend Developer, Java Backend Developer, Kotlin Backend Developer, ML Engineer, MLOps Engineer, Node.js Backend Developer, Python Backend Developer, Scala Backend Developer

  • Cloud Platforms & Managed Services Catalog dimension db id 221

    Library dimension (catalog)

    Roles linked in library: Fullstack Developer, Go Backend Developer, Node.js Backend Developer

  • Cloud Platforms for AI Deployment Catalog dimension db id 211

    Library dimension (catalog)

    Roles linked in library: AI Engineer

  • Cloud Provider Platforms Catalog dimension db id 131

    Library dimension (catalog)

    Roles linked in library: Cloud Architect, Cloud Security Engineer

  • Cloud Security Posture Tools Catalog dimension db id 64

    Library dimension (catalog)

    Roles linked in library: Cloud Security Engineer, Cyber Security Engineer

  • Vendor Product Families Catalog dimension db id 477

    Library dimension (catalog)

    Roles linked in library: Engineering Manager

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Cloud Platforms
cloud-platforms
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Cloud Platforms & Managed Services
cloud-platforms-managed-services
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Cloud Platforms for AI Deployment
cloud-platforms-for-ai-deployment
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Cloud Provider Platforms
cloud-provider-platforms
Existing dimension (library) · Role↔dimension saved
Cloud Security Posture Tools
cloud-security-posture-tools
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Vendor Product Families
vendor-product-families
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Google Cloud Platform Secondary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Google Cloud Platform id=425 · google-cloud-platform

Aliases — catalog

  • Google Cloud Platform (CANONICAL) primary

Context tags (catalog)

Anthos App Engine Artifact Registry BigQuery Cloud Build Cloud Functions Cloud Monitoring Cloud Pub/Sub Cloud Run Cloud SQL Cloud Spanner Cloud Storage Compute Engine Dataflow Dataproc GCP GKE IAM Kubernetes Kubernetes Engine Pub/Sub Serverless Stackdriver Terraform VPC

Stored enrichment (catalog DB)

Category
Platform
Sub-category
Cloud Platform
Vendor
Google
License
other_open
Year introduced
2008
Confidence
0.99
Version strategy
NOT_APPLICABLE

Maturity reasoning: GCP appears in many cloud-engineering job descriptions alongside AWS/Azure, and Google continues expanding managed services and certifications, indicating broad hiring demand rather than niche use.

Skill profile (library / DB)

Skill nature
PLATFORM
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
9
Sub-category id
46
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Cloud & Hosting Providers Catalog dimension db id 414

    Library dimension (catalog)

    Roles linked in library: PHP Backend Developer

  • Cloud Provider Platforms Catalog dimension db id 131

    Library dimension (catalog)

    Roles linked in library: Cloud Architect, Cloud Security Engineer

  • Cloud Security Posture Tools Catalog dimension db id 64

    Library dimension (catalog)

    Roles linked in library: Cloud Security Engineer, Cyber Security Engineer

  • Vendor Product Families Catalog dimension db id 477

    Library dimension (catalog)

    Roles linked in library: Engineering Manager

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Cloud & Hosting Providers
cloud-hosting-providers
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Cloud Provider Platforms
cloud-provider-platforms
Existing dimension (library) · Role↔dimension saved
Cloud Security Posture Tools
cloud-security-posture-tools
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Vendor Product Families
vendor-product-families
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Hadoop Secondary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Hadoop id=1351 · hadoop

Aliases — catalog

  • Hadoop (CANONICAL)

Context tags (catalog)

Big Data Data Lake Distributed Computing ELT ETL Flume HDFS Hive Kafka MapReduce NoSQL Oozie Pig Spark Sqoop YARN

Stored enrichment (catalog DB)

Category
Framework
Sub-category
Data Processing Framework
Vendor
Apache Software Foundation
License
apache_2
Year introduced
2006
Confidence
0.90
Version strategy
NOT_APPLICABLE

Maturity reasoning: Job postings still mention Hadoop for legacy big-data stacks, but JD volume has fallen as Spark and cloud warehouses replaced MapReduce-era clusters.

Skill profile (library / DB)

Skill nature
FRAMEWORK
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
5
Sub-category id
91
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • ETL and ELT Tooling Catalog dimension db id 24

    Library dimension (catalog)

    Roles linked in library: Data Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
ETL and ELT Tooling
etl-and-elt-tooling
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Databricks Secondary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Databricks id=1202 · databricks

Aliases — catalog

  • Databricks (CANONICAL)

Context tags (catalog)

Apache Spark Databricks Runtime Delta Lake MLflow SQL Analytics Spark cloud integration collaborative workspace data engineering data lakes data pipelines data visualization job scheduling machine learning notebooks real-time analytics

Stored enrichment (catalog DB)

Category
Platform
Sub-category
Data Analytics Platform
Vendor
Databricks, Inc.
License
other_open
Year introduced
2013
Confidence
0.97
Version strategy
NOT_APPLICABLE

Maturity reasoning: Databricks appears frequently in data engineering and analytics job postings, especially alongside Spark, Delta Lake, and lakehouse stacks; strong vendor adoption and broad enterprise usage signal mainstream demand.

Skill profile (library / DB)

Skill nature
PLATFORM
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
9
Sub-category id
911
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • React Frontend Development Catalog dimension db id 96

    Library dimension (catalog)

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
React Frontend Development
d_init_01
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Amazon S3 Secondary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Amazon S3 id=170 · amazon-s3

Aliases — catalog

  • Amazon S3 (CANONICAL) primary

Context tags (catalog)

ACL Cross-Region Replication Glacier SSE-KMS SSE-S3 access control bucket bucket policy cross-region replication event notifications lifecycle policy multipart upload object storage pre-signed URL replication static website hosting storage class versioning

Stored enrichment (catalog DB)

Category
Service
Sub-category
Object Storage Service
Vendor
Amazon Web Services
License
proprietary
Year introduced
2006
Confidence
0.98
Version strategy
NOT_APPLICABLE

Maturity reasoning: Amazon S3 is a standard cloud storage service widely listed in job descriptions and core AWS certifications; it remains a default object-storage choice rather than a niche or sunset product.

Skill profile (library / DB)

Skill nature
CLOUD_SERVICE
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
11
Sub-category id
120
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Cloud Storage and Data Services Catalog dimension db id 144

    Library dimension (catalog)

    Roles linked in library: Cloud Architect

  • Cloud Storage and File Formats Catalog dimension db id 35

    Library dimension (catalog)

    Roles linked in library: Data Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Cloud Storage and Data Services
cloud-storage-and-data-services
Existing dimension (library) · Role↔dimension saved
Cloud Storage and File Formats
cloud-storage-and-file-formats
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Azure Data Lake Storage Secondary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Azure Blob Storage id=172 · azure-blob-storage

Aliases — catalog

  • Azure Blob Storage (CANONICAL) primary

Context tags (catalog)

AzCopy Azure Storage Explorer Azurite Managed Identity SAS token access tiers blob trigger blobs containers event grid hot/cool/archive immutable storage lifecycle management managed identity private endpoint replication shared access signature storage account

Stored enrichment (catalog DB)

Category
Service
Sub-category
Object Storage Service
Vendor
Microsoft
License
proprietary
Year introduced
2008
Confidence
0.98
Version strategy
NOT_APPLICABLE

Maturity reasoning: Broadly used object storage on Azure; appears frequently in cloud/data engineering JDs and Microsoft positions it as a core storage service, with no sunset or replacement signal.

Skill profile (library / DB)

Skill nature
CLOUD_SERVICE
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
11
Sub-category id
120
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Cloud Storage and Data Services Catalog dimension db id 144

    Library dimension (catalog)

    Roles linked in library: Cloud Architect

  • Cloud Storage and File Formats Catalog dimension db id 35

    Library dimension (catalog)

    Roles linked in library: Data Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Cloud Storage and Data Services
cloud-storage-and-data-services
Skipped — no persistable v3 meta for new skill
skill_not_in_db_v3_proposed
Cloud Storage and File Formats
cloud-storage-and-file-formats
Skipped — no persistable v3 meta for new skill
skill_not_in_db_v3_proposed
HDFS Secondary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).

Derived legacy fields
Category
Data Engineering Tools
Sub-category
general
Skill nature
TOOL
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Google Cloud Storage Secondary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Google Cloud Storage id=171 · google-cloud-storage

Aliases — catalog

  • Google Cloud Storage (CANONICAL) primary

Context tags (catalog)

CMEK CORS Cloud Storage FUSE Cloud Storage buckets GCS IAM IAM permissions Uniform bucket-level access bucket bucket policy event notifications gsutil multipart upload object lifecycle resumable uploads retention policy service account signed URL signed URLs storage class storage classes uniform bucket-level access versioning

Stored enrichment (catalog DB)

Category
Service
Sub-category
Object Storage Service
Vendor
Google
License
proprietary
Year introduced
2010
Confidence
0.98
Version strategy
NOT_APPLICABLE

Maturity reasoning: Broadly used object storage on GCP; appears frequently in cloud/data engineering JDs and is a standard managed service alongside S3/Azure Blob.

Skill profile (library / DB)

Skill nature
CLOUD_SERVICE
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
11
Sub-category id
120
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Cloud Storage and Data Services Catalog dimension db id 144

    Library dimension (catalog)

    Roles linked in library: Cloud Architect

  • Cloud Storage and File Formats Catalog dimension db id 35

    Library dimension (catalog)

    Roles linked in library: Data Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Cloud Storage and Data Services
cloud-storage-and-data-services
Existing dimension (library) · Role↔dimension saved
Cloud Storage and File Formats
cloud-storage-and-file-formats
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Kudu Secondary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).

Derived legacy fields
Category
Databases
Sub-category
general
Skill nature
TOOL
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Elasticsearch Secondary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Elasticsearch id=3171 · elasticsearch

Aliases — catalog

  • Elasticsearch (CANONICAL) primary
  • ES (VERSION)
  • ElasticSearch (VERSION)
  • Elasticsearch 6 (VERSION)
  • Elasticsearch 6.x (VERSION)
  • Elasticsearch 7 (VERSION)
  • Elasticsearch 7.x (VERSION)
  • Elasticsearch 8 (VERSION)
  • Elasticsearch 8.x (VERSION)
  • Elasticsearch v6 (VERSION)
  • Elasticsearch v7 (VERSION)
  • Elasticsearch v8 (VERSION)

Context tags (catalog)

ELK stack JSON Kibana Logstash Lucene REST API RESTful API aggregation cluster management data visualization document store full-text search indexing performance tuning query DSL real-time search replication scalability search analytics search relevance sharding

Stored enrichment (catalog DB)

Category
Datastore
Sub-category
Search Datastore
Vendor
Elastic NV
License
apache_2
Year introduced
2010
Confidence
0.93
Version strategy
SEPARATE_ENTITY
Version tag
8.x

Maturity reasoning: Commonly listed in job descriptions for search/log analytics roles and widely deployed in production; Elastic’s docs and ecosystem show sustained adoption rather than sunset or replacement.

Skill profile (library / DB)

Skill nature
TOOL
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
3
Sub-category id
2925
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Magento Search and Indexing Catalog dimension db id 403

    Library dimension (catalog)

    Roles linked in library: Magento Dev

  • Search and Content Discovery Catalog dimension db id 356

    Library dimension (catalog)

    Roles linked in library: Drupal Dev, Sitecore Dev

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Magento Search and Indexing
magento-search-and-indexing
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Search and Content Discovery
search-and-content-discovery
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Solr Secondary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Solr id=3170 · solr

Aliases — catalog

  • Solr (CANONICAL) primary

Context tags (catalog)

Apache Apache Solr JSON response Lucene Solr Admin UI Solr schema SolrCloud SolrJ data import handler distributed search facet fields faceting highlighting query parsing replication schema.xml search indexing sharding solrconfig.xml spell checking

Stored enrichment (catalog DB)

Category
Datastore
Sub-category
Search Engine Datastore
Vendor
Apache Software Foundation
License
apache_2
Year introduced
2006
Confidence
0.88
Version strategy
NOT_APPLICABLE

Maturity reasoning: Solr still appears in some enterprise JDs, but JD volume is far below Elasticsearch/OpenSearch and many search stacks have shifted to those successors; GitHub activity is steady but not broad-market.

Skill profile (library / DB)

Skill nature
TOOL
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
3
Sub-category id
3284
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Search and Content Discovery Catalog dimension db id 356

    Library dimension (catalog)

    Roles linked in library: Drupal Dev, Sitecore Dev

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Search and Content Discovery
search-and-content-discovery
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Cassandra Secondary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Cassandra id=1354 · cassandra

Aliases — catalog

  • Cassandra (CANONICAL)

Context tags (catalog)

CQL Cluster Consistency DSE Data Modeling DataStax Eventual Consistency NoSQL Partitioning Replication ScyllaDB Spark Thrift TinkerPop Wide Column

Stored enrichment (catalog DB)

Category
Datastore
Sub-category
Wide Column Store
Vendor
Apache Software Foundation
License
apache_2
Year introduced
2008
Confidence
0.99
Version strategy
NOT_APPLICABLE

Maturity reasoning: Apache Cassandra appears in many production data-platform JDs and is a common choice for high-write, distributed workloads; GitHub and vendor docs show sustained activity rather than sunset signals.

Skill profile (library / DB)

Skill nature
TOOL
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
3
Sub-category id
31
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Cloud Storage and Data Services Catalog dimension db id 144

    Library dimension (catalog)

    Roles linked in library: Cloud Architect

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Cloud Storage and Data Services
cloud-storage-and-data-services
Existing dimension (library) · Role↔dimension saved
Apache Spark Secondary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Apache Spark id=1350 · apache-spark

Aliases — catalog

  • Apache Spark (CANONICAL)
  • apache spark 3 (VERSION)
  • spark (VERSION)
  • spark 3 (VERSION)
  • spark 3.x (VERSION)
  • spark3 (VERSION)

Context tags (catalog)

Apache Kafka Cluster Manager DAGScheduler Data Lake DataFrame ETL Hadoop MLlib Machine Learning PySpark RDD Scala Spark SQL Spark Streaming SparkSession

Stored enrichment (catalog DB)

Category
Framework
Sub-category
Distributed Data Processing Framework
Vendor
Apache Software Foundation
License
apache_2
Year introduced
2010
Confidence
0.94
Version strategy
SEPARATE_ENTITY
Version tag
3.x

Maturity reasoning: Apache Spark appears in many data engineering JDs and remains a standard for distributed ETL/ELT; its GitHub and vendor ecosystem activity stay strong, with Databricks and cloud platforms still promoting it.

Skill profile (library / DB)

Skill nature
FRAMEWORK
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
5
Sub-category id
1021
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • ETL and ELT Tooling Catalog dimension db id 24

    Library dimension (catalog)

    Roles linked in library: Data Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
ETL and ELT Tooling
etl-and-elt-tooling
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Apache Kafka Secondary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Apache Kafka id=145 · apache-kafka

Aliases — catalog

  • Apache Kafka (CANONICAL) primary

Context tags (catalog)

Avro Kafka Streams Schema Registry ZooKeeper brokers consumer group event streaming exactly-once semantics ksqlDB message queue offsets partitioning pub/sub replication topics

Stored enrichment (catalog DB)

Category
Tool
Sub-category
Event Streaming Tool
Vendor
Apache Software Foundation
License
apache_2
Year introduced
2011
Confidence
0.90
Version strategy
NOT_APPLICABLE

Maturity reasoning: Apache Kafka is broadly adopted in production and appears frequently in job descriptions for event streaming, data pipelines, and microservices; it remains a common hiring-pipeline staple across backend and platform roles.

Skill profile (library / DB)

Skill nature
TOOL
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
13
Sub-category id
128
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Messaging and Event Streaming Catalog dimension db id 8

    Library dimension (catalog)

    Roles linked in library: Backend Developer, Data Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Messaging and Event Streaming
messaging-and-event-streaming
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
StreamSets Secondary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).

Derived legacy fields
Category
Data Engineering Tools
Sub-category
general
Skill nature
TOOL
Volatility
FAST
Typical lifespan
SHORT_LIVED
Version strategy
VERSIONED
Matillion Secondary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Matillion id=118 · matillion

Aliases — catalog

  • Matillion (CANONICAL) primary

Context tags (catalog)

API connectors Amazon Redshift Azure Synapse ELT ETL Google BigQuery JDBC S3 SQL Snowflake data warehouse dbt incremental loads orchestration staging tables

Stored enrichment (catalog DB)

Category
Platform
Sub-category
Data Integration Platform
Vendor
Matillion Ltd.
License
proprietary
Year introduced
2011
Confidence
0.90
Version strategy
NOT_APPLICABLE

Maturity reasoning: Matillion appears in cloud data-integration JDs, especially for Snowflake/Databricks stacks, but volume is far below ETL staples like Informatica/dbt, indicating growing but not universal adoption.

Skill profile (library / DB)

Skill nature
PLATFORM
Volatility
EMERGING
Typical lifespan
EVERGREEN
Category id
9
Sub-category id
114
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • ETL and ELT Tooling Catalog dimension db id 24

    Library dimension (catalog)

    Roles linked in library: Data Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
ETL and ELT Tooling
etl-and-elt-tooling
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Fivetran Secondary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Fivetran id=116 · fivetran

Aliases — catalog

  • Fivetran (CANONICAL) primary

Context tags (catalog)

API connector BigQuery CDC ELT Redshift SaaS sources Snowflake connector data ingestion data pipeline data warehouse dbt incremental sync replication schema drift

Stored enrichment (catalog DB)

Category
Platform
Sub-category
Data Integration Platform
Vendor
Fivetran, Inc.
License
proprietary
Year introduced
2012
Confidence
0.97
Version strategy
NOT_APPLICABLE

Maturity reasoning: Commonly listed in data engineering JDs and partner ecosystems; Fivetran’s broad connector catalog and frequent mentions alongside dbt/Snowflake signal mainstream adoption.

Skill profile (library / DB)

Skill nature
PLATFORM
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
9
Sub-category id
114
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • ETL and ELT Tooling Catalog dimension db id 24

    Library dimension (catalog)

    Roles linked in library: Data Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
ETL and ELT Tooling
etl-and-elt-tooling
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
HVR Secondary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).

Derived legacy fields
Category
Data Engineering Tools
Sub-category
general
Skill nature
TOOL
Volatility
FAST
Typical lifespan
SHORT_LIVED
Version strategy
VERSIONED
Apache NiFi Secondary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).

Derived legacy fields
Category
Data Engineering Tools
Sub-category
general
Skill nature
TOOL
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
AWS Database Migration Service Secondary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).

Derived legacy fields
Category
Cloud Platforms
Sub-category
general
Skill nature
PLATFORM
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Azure Data Factory Secondary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).

Derived legacy fields
Category
Cloud Platforms
Sub-category
general
Skill nature
PLATFORM
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Google Cloud Dataproc Secondary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).

Derived legacy fields
Category
Cloud Platforms
Sub-category
general
Skill nature
PLATFORM
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Apache Airflow Secondary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Apache Airflow id=110 · apache-airflow

Aliases — catalog

  • Apache Airflow (CANONICAL) primary

Context tags (catalog)

CeleryExecutor DAG ETL KubernetesExecutor Sensors XCom backfill catchup cron data pipelines executor hooks operators scheduler task dependencies

Stored enrichment (catalog DB)

Category
Tool
Sub-category
Workflow Orchestration Tool
Vendor
Apache Software Foundation
License
apache_2
Year introduced
2015
Confidence
0.98
Version strategy
NOT_APPLICABLE

Maturity reasoning: Frequently listed in data engineering JDs and widely adopted for workflow orchestration; strong GitHub activity and managed offerings from AWS/GCP/Azure signal broad market demand.

Skill profile (library / DB)

Skill nature
TOOL
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
13
Sub-category id
130
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Data Pipeline Orchestration Catalog dimension db id 23

    Library dimension (catalog)

    Roles linked in library: Data Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Data Pipeline Orchestration
data-pipeline-orchestration
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
AWS Managed Workflows for Apache Airflow Secondary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).

Derived legacy fields
Category
Cloud Platforms
Sub-category
general
Skill nature
PLATFORM
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Luigi Secondary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Luigi id=114 · luigi

Aliases — catalog

  • Luigi (CANONICAL) primary

Context tags (catalog)

Airflow DAG ETL Hadoop Prefect backfill batch processing data lake data pipeline idempotent retry logic scheduler task dependency task runner workflow orchestration

Stored enrichment (catalog DB)

Category
Framework
Sub-category
Workflow Orchestration Framework
Vendor
Spotify
License
apache_2
Year introduced
2012
Confidence
0.90
Version strategy
NOT_APPLICABLE

Maturity reasoning: Luigi appears in relatively few current job postings compared with Airflow/Prefect, and its GitHub activity is far lower than mainstream orchestration tools, indicating a specialized legacy niche.

Skill profile (library / DB)

Skill nature
FRAMEWORK
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
5
Sub-category id
95
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Data Pipeline Orchestration Catalog dimension db id 23

    Library dimension (catalog)

    Roles linked in library: Data Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Data Pipeline Orchestration
data-pipeline-orchestration
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Terraform Secondary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Terraform id=286 · terraform

Aliases — catalog

  • Terraform (CANONICAL) primary

Context tags (catalog)

AWS Azure GCP HCL IaC Terraform Cloud Terraform Enterprise Terraform Registry Terragrunt apply backend destroy infrastructure automation modules outputs plan providers provisioning remote backends remote state resource blocks resource management state file state management terraform apply terraform plan variables version control workspaces

Stored enrichment (catalog DB)

Category
Tool
Sub-category
Infrastructure As Code Tool
Vendor
HashiCorp
License
mpl
Year introduced
2014
Confidence
0.93
Version strategy
NOT_APPLICABLE

Maturity reasoning: Terraform is broadly listed in DevOps/SRE/cloud JDs and remains a standard IaC tool across AWS/Azure/GCP; HashiCorp’s ecosystem and widespread GitHub usage signal strong market adoption.

Skill profile (library / DB)

Skill nature
TOOL
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
13
Sub-category id
191
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Infrastructure & Security Automation Frameworks Catalog dimension db id 249

    Library dimension (catalog)

    Roles linked in library: Cloud Security Engineer

  • Infrastructure as Code Catalog dimension db id 132

    Library dimension (catalog)

    Roles linked in library: Cloud Architect, DevOps Engineer

  • Infrastructure as Code for ML Catalog dimension db id 57

    Library dimension (catalog)

    Roles linked in library: ML Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Infrastructure & Security Automation Frameworks
infrastructure-security-automation-frameworks
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Infrastructure as Code
infrastructure-as-code
Existing dimension (library) · Role↔dimension saved
Infrastructure as Code for ML
infrastructure-as-code-for-ml
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
CloudFormation Secondary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: CloudFormation id=837 · cloudformation

Aliases — catalog

  • CloudFormation (CANONICAL) primary

Context tags (catalog)

AWS AWS CLI CloudFormation Designer JSON YAML change set deployment drift detection infrastructure as code nested stacks outputs parameters resource stack template

Stored enrichment (catalog DB)

Category
Service
Sub-category
Infrastructure As Code Service
Vendor
Amazon Web Services
License
proprietary
Year introduced
2013
Confidence
0.90
Version strategy
NOT_APPLICABLE

Maturity reasoning: AWS CloudFormation appears in many cloud/IaC job descriptions and remains a standard AWS-native infrastructure-as-code option, alongside Terraform in hiring pipelines.

Skill profile (library / DB)

Skill nature
CLOUD_SERVICE
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
11
Sub-category id
181
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Infrastructure as Code Catalog dimension db id 132

    Library dimension (catalog)

    Roles linked in library: Cloud Architect, DevOps Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Infrastructure as Code
infrastructure-as-code
Existing dimension (library) · Role↔dimension saved
Python Secondary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Python id=5 · python

Aliases — catalog

  • Python (CANONICAL) primary
  • Python 2 (VERSION)
  • Python 2.x (VERSION)
  • Python 3 (VERSION)
  • Python 3.10 (VERSION)
  • Python 3.11 (VERSION)
  • Python 3.12 (VERSION)
  • Python 3.x (VERSION)
  • py (VERSION)
  • py2 (VERSION)
  • py3 (VERSION)
  • python 3 (VERSION)
  • python 3.x (VERSION)
  • python2 (VERSION)
  • python3 (VERSION)
  • python3.x (VERSION)

Context tags (catalog)

API Django FastAPI Flask Jupyter NumPy PEP 8 Pandas REST SQLAlchemy asyncio pandas pip pytest type hints venv virtualenv

Stored enrichment (catalog DB)

Category
Language
Sub-category
Programming Language
Vendor
PSF
License
mit
Year introduced
1991
Confidence
0.99
Version strategy
SEPARATE_ENTITY
Version tag
3

Maturity reasoning: Python appears in a very high volume of job descriptions across data, backend, automation, and ML roles, and remains a default hiring-pipeline language on major job boards and tech stacks.

Skill profile (library / DB)

Skill nature
LANGUAGE
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
6
Sub-category id
96
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Cloud Security Scripting & DSL Languages Catalog dimension db id 248

    Library dimension (catalog)

    Roles linked in library: Cloud Security Engineer

  • Programming Languages Catalog dimension db id 1

    Library dimension (catalog)

    Roles linked in library: Backend Developer, Fullstack Developer, Fullstack Developer

  • Programming Languages & DSLs Catalog dimension db id 475

    Library dimension (catalog)

    Roles linked in library: Engineering Manager

  • Programming Languages and Scripting Catalog dimension db id 59

    Library dimension (catalog)

    Roles linked in library: Cyber Security Engineer

  • Programming Languages for Data Work Catalog dimension db id 21

    Library dimension (catalog)

    Roles linked in library: Data Engineer

  • Programming Languages for ML Systems Catalog dimension db id 39

    Library dimension (catalog)

    Roles linked in library: ML Engineer, MLOps Engineer

  • Programming Languages for XR Catalog dimension db id 97

    Library dimension (catalog)

    Roles linked in library: AR/VR Engineer

  • Python Programming Catalog dimension db id 290

    Library dimension (catalog)

    Roles linked in library: Python Backend Developer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Cloud Security Scripting & DSL Languages
cloud-security-scripting-dsl-languages
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Programming Languages
programming-languages
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Programming Languages & DSLs
programming-languages-dsls
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Programming Languages and Scripting
programming-languages-and-scripting
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Programming Languages for Data Work
programming-languages-for-data-work
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Programming Languages for ML Systems
programming-languages-for-ml-systems
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Programming Languages for XR
programming-languages-for-xr
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Python Programming
python-programming
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
CI/CD Secondary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: CI/CD id=1190 · ci-cd

Aliases — catalog

  • CI/CD (CANONICAL)

Context tags (catalog)

Ansible CircleCI Docker GitLab CI Jenkins Kubernetes Terraform Travis CI automated testing build automation continuous deployment continuous integration deployment pipelines monitoring version control

Stored enrichment (catalog DB)

Category
Methodology
Sub-category
Ci Cd Process
Confidence
0.93
Version strategy
NOT_APPLICABLE

Maturity reasoning: CI/CD appears in a large share of software engineering JDs and is a standard requirement across DevOps, platform, and backend roles; major vendors like GitHub, GitLab, and AWS all center product roadmaps on CI/CD pipelines.

Skill profile (library / DB)

Skill nature
METHODOLOGY
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
8
Sub-category id
900
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • CI/CD Pipeline Platforms Catalog dimension db id 150

    Library dimension (catalog)

    Roles linked in library: DevOps Engineer

  • CI/CD for Machine Learning Catalog dimension db id 56

    Library dimension (catalog)

    Roles linked in library: ML Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
CI/CD Pipeline Platforms
ci-cd-pipeline-platforms
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
CI/CD for Machine Learning
ci-cd-for-machine-learning
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Bitbucket Secondary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).

Derived legacy fields
Category
Version Control Tools
Sub-category
general
Skill nature
TOOL
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
GitHub Secondary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: GitHub id=280 · github

Aliases — catalog

  • GitHub (CANONICAL) primary

Context tags (catalog)

CI/CD Git GitHub Actions GitHub Enterprise GitHub Pages SSH keys actions branch protection branches code review collaboration commit history fork forking issue tracker issues markdown merge conflicts merge request pull request pull requests release tags repositories repository version control webhooks

Stored enrichment (catalog DB)

Category
Platform
Sub-category
Devops Platform
Vendor
GitHub, Inc.
License
other_open
Year introduced
2008
Confidence
0.96
Version strategy
NOT_APPLICABLE

Maturity reasoning: GitHub appears in a very high volume of engineering JDs for source control, code review, and CI/CD; it’s a standard hiring-pipeline skill across teams.

Skill profile (library / DB)

Skill nature
PLATFORM
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
9
Sub-category id
170
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • CI/CD Pipeline Platforms Catalog dimension db id 150

    Library dimension (catalog)

    Roles linked in library: DevOps Engineer

  • CI/CD for Machine Learning Catalog dimension db id 56

    Library dimension (catalog)

    Roles linked in library: ML Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
CI/CD Pipeline Platforms
ci-cd-pipeline-platforms
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
CI/CD for Machine Learning
ci-cd-for-machine-learning
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Flyway Secondary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).

Derived legacy fields
Category
Data Engineering Tools
Sub-category
general
Skill nature
TOOL
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Liquibase Secondary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).

Derived legacy fields
Category
Data Engineering Tools
Sub-category
general
Skill nature
TOOL
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED

All API 3 persistence rows

Same grid as the skill-extractor “Persistence items” table: one row per (skill × dimension) work item.

Skill Tag Dimension Skill↔dim Role↔dim Outcome Notes
SQL in_db
Pega Programming Languages & DSLs
pega-programming-languages-dsls
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
SQL in_db
Programming Languages & DSLs
programming-languages-dsls
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
SQL in_db
Programming Languages for Data Work
programming-languages-for-data-work
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Snowflake in_db
Cloud Data Warehouses
cloud-data-warehouses
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Amazon Redshift in_db
Cloud Data Warehouses
cloud-data-warehouses
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
AWS in_db
Cloud Platforms
cloud-platforms
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
AWS in_db
Cloud Platforms for AI Deployment
cloud-platforms-for-ai-deployment
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
AWS in_db
Cloud Provider Platforms
cloud-provider-platforms
Existing dimension (library) · Role↔dimension saved
AWS in_db
Cloud Security Posture Tools
cloud-security-posture-tools
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
AWS in_db
Vendor Product Families
vendor-product-families
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Azure in_db
Cloud Platforms
cloud-platforms
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Azure in_db
Cloud Platforms & Managed Services
cloud-platforms-managed-services
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Azure in_db
Cloud Platforms for AI Deployment
cloud-platforms-for-ai-deployment
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Azure in_db
Cloud Provider Platforms
cloud-provider-platforms
Existing dimension (library) · Role↔dimension saved
Azure in_db
Cloud Security Posture Tools
cloud-security-posture-tools
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Azure in_db
Vendor Product Families
vendor-product-families
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Google Cloud Platform in_db
Cloud & Hosting Providers
cloud-hosting-providers
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Google Cloud Platform in_db
Cloud Provider Platforms
cloud-provider-platforms
Existing dimension (library) · Role↔dimension saved
Google Cloud Platform in_db
Cloud Security Posture Tools
cloud-security-posture-tools
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Google Cloud Platform in_db
Vendor Product Families
vendor-product-families
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Hadoop in_db
ETL and ELT Tooling
etl-and-elt-tooling
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Databricks in_db
React Frontend Development
d_init_01
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Amazon S3 in_db
Cloud Storage and Data Services
cloud-storage-and-data-services
Existing dimension (library) · Role↔dimension saved
Amazon S3 in_db
Cloud Storage and File Formats
cloud-storage-and-file-formats
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Azure Data Lake Storage new
Cloud Storage and Data Services
cloud-storage-and-data-services
Skipped — no persistable v3 meta for new skill skill_not_in_db_v3_proposed
Azure Data Lake Storage new
Cloud Storage and File Formats
cloud-storage-and-file-formats
Skipped — no persistable v3 meta for new skill skill_not_in_db_v3_proposed
Google Cloud Storage in_db
Cloud Storage and Data Services
cloud-storage-and-data-services
Existing dimension (library) · Role↔dimension saved
Google Cloud Storage in_db
Cloud Storage and File Formats
cloud-storage-and-file-formats
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Elasticsearch in_db
Magento Search and Indexing
magento-search-and-indexing
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Elasticsearch in_db
Search and Content Discovery
search-and-content-discovery
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Solr in_db
Search and Content Discovery
search-and-content-discovery
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Cassandra in_db
Cloud Storage and Data Services
cloud-storage-and-data-services
Existing dimension (library) · Role↔dimension saved
Apache Spark in_db
ETL and ELT Tooling
etl-and-elt-tooling
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Apache Kafka in_db
Messaging and Event Streaming
messaging-and-event-streaming
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Matillion in_db
ETL and ELT Tooling
etl-and-elt-tooling
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Fivetran in_db
ETL and ELT Tooling
etl-and-elt-tooling
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Apache Airflow in_db
Data Pipeline Orchestration
data-pipeline-orchestration
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Luigi in_db
Data Pipeline Orchestration
data-pipeline-orchestration
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Terraform in_db
Infrastructure & Security Automation Frameworks
infrastructure-security-automation-frameworks
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Terraform in_db
Infrastructure as Code
infrastructure-as-code
Existing dimension (library) · Role↔dimension saved
Terraform in_db
Infrastructure as Code for ML
infrastructure-as-code-for-ml
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
CloudFormation in_db
Infrastructure as Code
infrastructure-as-code
Existing dimension (library) · Role↔dimension saved
Python in_db
Cloud Security Scripting & DSL Languages
cloud-security-scripting-dsl-languages
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Python in_db
Programming Languages
programming-languages
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Python in_db
Programming Languages & DSLs
programming-languages-dsls
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Python in_db
Programming Languages and Scripting
programming-languages-and-scripting
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Python in_db
Programming Languages for Data Work
programming-languages-for-data-work
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Python in_db
Programming Languages for ML Systems
programming-languages-for-ml-systems
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Python in_db
Programming Languages for XR
programming-languages-for-xr
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Python in_db
Python Programming
python-programming
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
CI/CD in_db
CI/CD Pipeline Platforms
ci-cd-pipeline-platforms
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
CI/CD in_db
CI/CD for Machine Learning
ci-cd-for-machine-learning
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
GitHub in_db
CI/CD Pipeline Platforms
ci-cd-pipeline-platforms
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
GitHub in_db
CI/CD for Machine Learning
ci-cd-for-machine-learning
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)

Library artifacts (this run)

Kind Detail DB id
canonical_skill_proposed HDFS | type=Data Engineering Tools subtype=general nature=TOOL lifespan=MULTI_YEAR
canonical_skill_proposed Kudu | type=Databases subtype=general nature=TOOL lifespan=MULTI_YEAR
canonical_skill_proposed StreamSets | type=Data Engineering Tools subtype=general nature=TOOL lifespan=SHORT_LIVED
canonical_skill_proposed HVR | type=Data Engineering Tools subtype=general nature=TOOL lifespan=SHORT_LIVED
canonical_skill_proposed Apache NiFi | type=Data Engineering Tools subtype=general nature=TOOL lifespan=MULTI_YEAR
canonical_skill_proposed AWS Database Migration Service | type=Cloud Platforms subtype=general nature=PLATFORM lifespan=MULTI_YEAR
canonical_skill_proposed Azure Data Factory | type=Cloud Platforms subtype=general nature=PLATFORM lifespan=MULTI_YEAR
canonical_skill_proposed Google Cloud Dataproc | type=Cloud Platforms subtype=general nature=PLATFORM lifespan=MULTI_YEAR
canonical_skill_proposed AWS Managed Workflows for Apache Airflow | type=Cloud Platforms subtype=general nature=PLATFORM lifespan=MULTI_YEAR
canonical_skill_proposed Bitbucket | type=Version Control Tools subtype=general nature=TOOL lifespan=MULTI_YEAR
canonical_skill_proposed Flyway | type=Data Engineering Tools subtype=general nature=TOOL lifespan=MULTI_YEAR
canonical_skill_proposed Liquibase | type=Data Engineering Tools subtype=general nature=TOOL lifespan=MULTI_YEAR
dimension_skill_link_proposed Azure Data Lake Storage ↔ Cloud Storage and Data Services
role_dimension_link_proposed Cloud Architect ↔ Cloud Storage and Data Services
dimension_skill_link_proposed Azure Data Lake Storage ↔ Cloud Storage and File Formats
nano JD Parser — gpt-4.1-nano click to toggle
RoleSolutions Architect
CompanyphData
DomainIT Services & Consulting
Location(remote)
JD type pass
Show raw JSON
{
  "JD_type": "pass",
  "about_company": {
    "source_marker": {
      "first_5_words": "Join phData, a dynamic",
      "last_5_words": "you the autonomy to deliver results."
    },
    "text": "Join phData, a dynamic and innovative leader in the modern data stack. We partner with major cloud data platforms like Snowflake, AWS, Azure, GCP, Fivetran, and dbt to deliver cutting-edge services and solutions. We\u0027re committed to helping global enterprises overcome their toughest data challenges. Even though we\u0027re growing extremely fast, we maintain a casual, exciting work environment. We hire top performers and allow you the autonomy to deliver results.",
    "word_count": 64
  },
  "certifications": [],
  "company_name": "phData",
  "ctc": null,
  "domain": {
    "primary": {
      "aliases": [
        "Tech Consulting",
        "Cloud Services"
      ],
      "domain": "IT Services \u0026 Consulting"
    },
    "secondary": null
  },
  "education": [
    {
      "level": "Bachelor\u0027s",
      "qualification": "BTECH/BE - Computer Science (or related)",
      "raw": "Bachelor\u0027s degree in Computer Science or a related field",
      "requirement": "required"
    }
  ],
  "experience": {
    "max": null,
    "min": null,
    "raw": null
  },
  "job_locations": [
    {
      "aliases": [],
      "city": null,
      "country": null,
      "state": null,
      "work_mode": "remote"
    }
  ],
  "role": "Solutions Architect",
  "role_aliases": [
    "Cloud Solutions Architect",
    "Data Solutions Architect",
    "Technical Architect"
  ],
  "role_archetype": "Engineering",
  "roles_and_responsibilities": [
    {
      "bullet_count": 6,
      "heading": "Responsibilities",
      "heading_was_present": true,
      "source_marker": {
        "first_5_words": "\u2022 Operate and manage modern",
        "last_5_words": "automate and optimize existing processes."
      },
      "text": "\u2022 Operate and manage modern data platforms - from streaming, to data lakes, to analytics, and beyond across a progressively evolving technical stack.\n\u2022 Demonstrate clear ownership of multiple simultaneous customer accounts across a variety of technical stacks and act as a technical leader.\n\u2022 Proactively identify and articulate best practices and improvements to customers on how to best leverage their Cloud Data Platform.\n\u2022 Skillfully navigate complex customer environments and build Epics, Stories, and Tasks in order to mature and improve our customer\u2019s data platform. Delegate to and coach Engineers and ensure successful and timely delivery of these Epics.\n\u2022 Provide thought leadership by recommending the right technologies and approaches to maturing and solving problems to help ensure performance, security, scalability, and user satisfaction.\n\u2022 Continually hunt for ways to automate and optimize existing processes.",
      "word_count": 134
    },
    {
      "bullet_count": 10,
      "heading": "Required Experience",
      "heading_was_present": true,
      "source_marker": {
        "first_5_words": "\u2022 Strong communication skills in",
        "last_5_words": "and verbal communication skills and experience"
      },
      "text": "\u2022 Strong communication skills in English\n\u2022 Strong working knowledge of SQL and the ability to write, debug, and optimize SQL queries\n\u2022 Extensive experience in providing architectural guidance and operational support across a large user base for a cloud-native data warehouse (Snowflake and/or Redshift)\n\u2022 Expertise with cloud-native data technologies in AWS or Azure\n\u2022 Production support experience in large enterprise environment supporting cloud data platforms and a variety of cloud data technologies (AWS Services/S3, Aurora Athena Redshift, Sagemaker, Azure DevOps/SQL, Azure Data Factory, etc.....\n\u2022 Proven experience learning new technology stacks and training team members\n\u2022 Professional track record of creating, challenging, and improving processes and procedures\n\u2022 Strong troubleshooting and performance tuning skills including database administration\n\u2022 Create and deliver detailed presentations\n\u2022 Client-facing written and verbal communication skills and experience",
      "word_count": 139
    },
    {
      "bullet_count": 8,
      "heading": "Preferred Experience",
      "heading_was_present": true,
      "source_marker": {
        "first_5_words": "\u2022 Production / database administration experience",
        "last_5_words": "experience as a technical team lead"
      },
      "text": "\u2022 Production / database administration experience and certifications in core data platforms such as Snowflake, AWS, Azure, GCP, Hadoop, or Databricks\n\u2022 Production experience working with Cloud and Distributed Data Storage technologies such as S3, ADLS, HDFS, GCS, Kudu, ElasticSearch/Solr, Cassandra or other NoSQL storage systems\n\u2022 Production experience working with Data integration technologies such as Spark, Kafka, event/streaming, Streamsets, Matillion, Fivetran, HVR, NiFi, AWS Data Migration Services, Azure DataFactory Google DataProc or others\n\u2022 Production experience working with Workflow Management and Orchestration such as Airflow, AWS Managed Airflow, Luigi, NiFi\n\u2022 Working experience with infrastructure as code using Terraform or Cloud Formation\n\u2022 Strong expertise of scripting language to automate repetitive tasks (preferred Python)\n\u2022 Well versed with continuous integration and deployment frameworks with hands-on experience using CI/CD tools like Bitbucket, Github, Flyway, Liquibase\n\u2022 Experience as a technical team lead",
      "word_count": 139
    }
  ],
  "urls": []
}
API 1 — extract-from-jd click to toggle
{
  "final_skills": [
    {
      "is_primary": true,
      "skill_name": "SQL"
    },
    {
      "is_primary": true,
      "skill_name": "Snowflake"
    },
    {
      "is_primary": true,
      "skill_name": "Amazon Redshift"
    },
    {
      "is_primary": true,
      "skill_name": "AWS"
    },
    {
      "is_primary": true,
      "skill_name": "Azure"
    },
    {
      "is_primary": false,
      "skill_name": "Google Cloud Platform"
    },
    {
      "is_primary": false,
      "skill_name": "Hadoop"
    },
    {
      "is_primary": false,
      "skill_name": "Databricks"
    },
    {
      "is_primary": false,
      "skill_name": "Amazon S3"
    },
    {
      "is_primary": false,
      "skill_name": "Azure Data Lake Storage"
    },
    {
      "is_primary": false,
      "skill_name": "HDFS"
    },
    {
      "is_primary": false,
      "skill_name": "Google Cloud Storage"
    },
    {
      "is_primary": false,
      "skill_name": "Kudu"
    },
    {
      "is_primary": false,
      "skill_name": "Elasticsearch"
    },
    {
      "is_primary": false,
      "skill_name": "Solr"
    },
    {
      "is_primary": false,
      "skill_name": "Cassandra"
    },
    {
      "is_primary": false,
      "skill_name": "Apache Spark"
    },
    {
      "is_primary": false,
      "skill_name": "Apache Kafka"
    },
    {
      "is_primary": false,
      "skill_name": "StreamSets"
    },
    {
      "is_primary": false,
      "skill_name": "Matillion"
    },
    {
      "is_primary": false,
      "skill_name": "Fivetran"
    },
    {
      "is_primary": false,
      "skill_name": "HVR"
    },
    {
      "is_primary": false,
      "skill_name": "Apache NiFi"
    },
    {
      "is_primary": false,
      "skill_name": "AWS Database Migration Service"
    },
    {
      "is_primary": false,
      "skill_name": "Azure Data Factory"
    },
    {
      "is_primary": false,
      "skill_name": "Google Cloud Dataproc"
    },
    {
      "is_primary": false,
      "skill_name": "Apache Airflow"
    },
    {
      "is_primary": false,
      "skill_name": "AWS Managed Workflows for Apache Airflow"
    },
    {
      "is_primary": false,
      "skill_name": "Luigi"
    },
    {
      "is_primary": false,
      "skill_name": "Terraform"
    },
    {
      "is_primary": false,
      "skill_name": "CloudFormation"
    },
    {
      "is_primary": false,
      "skill_name": "Python"
    },
    {
      "is_primary": false,
      "skill_name": "CI/CD"
    },
    {
      "is_primary": false,
      "skill_name": "Bitbucket"
    },
    {
      "is_primary": false,
      "skill_name": "GitHub"
    },
    {
      "is_primary": false,
      "skill_name": "Flyway"
    },
    {
      "is_primary": false,
      "skill_name": "Liquibase"
    }
  ],
  "jd_role": {
    "display_name": "Solutions Architect",
    "rationale": null,
    "role_aliases": [
      "Cloud Solutions Architect",
      "Data Solutions Architect",
      "Technical Architect"
    ],
    "role_archetype": "Engineering",
    "slug": ""
  },
  "nano_parsed": {
    "JD_type": "pass",
    "about_company": {
      "source_marker": {
        "first_5_words": "Join phData, a dynamic",
        "last_5_words": "you the autonomy to deliver results."
      },
      "text": "Join phData, a dynamic and innovative leader in the modern data stack. We partner with major cloud data platforms like Snowflake, AWS, Azure, GCP, Fivetran, and dbt to deliver cutting-edge services and solutions. We\u0027re committed to helping global enterprises overcome their toughest data challenges. Even though we\u0027re growing extremely fast, we maintain a casual, exciting work environment. We hire top performers and allow you the autonomy to deliver results.",
      "word_count": 64
    },
    "certifications": [],
    "company_name": "phData",
    "ctc": null,
    "domain": {
      "primary": {
        "aliases": [
          "Tech Consulting",
          "Cloud Services"
        ],
        "domain": "IT Services \u0026 Consulting"
      },
      "secondary": null
    },
    "education": [
      {
        "level": "Bachelor\u0027s",
        "qualification": "BTECH/BE - Computer Science (or related)",
        "raw": "Bachelor\u0027s degree in Computer Science or a related field",
        "requirement": "required"
      }
    ],
    "experience": {
      "max": null,
      "min": null,
      "raw": null
    },
    "job_locations": [
      {
        "aliases": [],
        "city": null,
        "country": null,
        "state": null,
        "work_mode": "remote"
      }
    ],
    "role": "Solutions Architect",
    "role_aliases": [
      "Cloud Solutions Architect",
      "Data Solutions Architect",
      "Technical Architect"
    ],
    "role_archetype": "Engineering",
    "roles_and_responsibilities": [
      {
        "bullet_count": 6,
        "heading": "Responsibilities",
        "heading_was_present": true,
        "source_marker": {
          "first_5_words": "\u2022 Operate and manage modern",
          "last_5_words": "automate and optimize existing processes."
        },
        "text": "\u2022 Operate and manage modern data platforms - from streaming, to data lakes, to analytics, and beyond across a progressively evolving technical stack.\n\u2022 Demonstrate clear ownership of multiple simultaneous customer accounts across a variety of technical stacks and act as a technical leader.\n\u2022 Proactively identify and articulate best practices and improvements to customers on how to best leverage their Cloud Data Platform.\n\u2022 Skillfully navigate complex customer environments and build Epics, Stories, and Tasks in order to mature and improve our customer\u2019s data platform. Delegate to and coach Engineers and ensure successful and timely delivery of these Epics.\n\u2022 Provide thought leadership by recommending the right technologies and approaches to maturing and solving problems to help ensure performance, security, scalability, and user satisfaction.\n\u2022 Continually hunt for ways to automate and optimize existing processes.",
        "word_count": 134
      },
      {
        "bullet_count": 10,
        "heading": "Required Experience",
        "heading_was_present": true,
        "source_marker": {
          "first_5_words": "\u2022 Strong communication skills in",
          "last_5_words": "and verbal communication skills and experience"
        },
        "text": "\u2022 Strong communication skills in English\n\u2022 Strong working knowledge of SQL and the ability to write, debug, and optimize SQL queries\n\u2022 Extensive experience in providing architectural guidance and operational support across a large user base for a cloud-native data warehouse (Snowflake and/or Redshift)\n\u2022 Expertise with cloud-native data technologies in AWS or Azure\n\u2022 Production support experience in large enterprise environment supporting cloud data platforms and a variety of cloud data technologies (AWS Services/S3, Aurora Athena Redshift, Sagemaker, Azure DevOps/SQL, Azure Data Factory, etc.....\n\u2022 Proven experience learning new technology stacks and training team members\n\u2022 Professional track record of creating, challenging, and improving processes and procedures\n\u2022 Strong troubleshooting and performance tuning skills including database administration\n\u2022 Create and deliver detailed presentations\n\u2022 Client-facing written and verbal communication skills and experience",
        "word_count": 139
      },
      {
        "bullet_count": 8,
        "heading": "Preferred Experience",
        "heading_was_present": true,
        "source_marker": {
          "first_5_words": "\u2022 Production / database administration experience",
          "last_5_words": "experience as a technical team lead"
        },
        "text": "\u2022 Production / database administration experience and certifications in core data platforms such as Snowflake, AWS, Azure, GCP, Hadoop, or Databricks\n\u2022 Production experience working with Cloud and Distributed Data Storage technologies such as S3, ADLS, HDFS, GCS, Kudu, ElasticSearch/Solr, Cassandra or other NoSQL storage systems\n\u2022 Production experience working with Data integration technologies such as Spark, Kafka, event/streaming, Streamsets, Matillion, Fivetran, HVR, NiFi, AWS Data Migration Services, Azure DataFactory Google DataProc or others\n\u2022 Production experience working with Workflow Management and Orchestration such as Airflow, AWS Managed Airflow, Luigi, NiFi\n\u2022 Working experience with infrastructure as code using Terraform or Cloud Formation\n\u2022 Strong expertise of scripting language to automate repetitive tasks (preferred Python)\n\u2022 Well versed with continuous integration and deployment frameworks with hands-on experience using CI/CD tools like Bitbucket, Github, Flyway, Liquibase\n\u2022 Experience as a technical team lead",
        "word_count": 139
      }
    ],
    "urls": []
  },
  "rejected": false,
  "rejection_reason": null,
  "run_id": "6dcdb737-93c5-4641-b093-a2ce8ccbbe4b",
  "stage3_signals": {
    "alias_found": true,
    "alias_match_roles": [
      {
        "display_name": "Cloud Architect",
        "kra_matches": null,
        "matched_count": null,
        "matched_skills": null,
        "role_id": 9,
        "score": 1.0,
        "slug": "cloud-architect",
        "total_count": null
      },
      {
        "display_name": "Data Modeler",
        "kra_matches": null,
        "matched_count": null,
        "matched_skills": null,
        "role_id": 150,
        "score": 1.0,
        "slug": "data-modeler",
        "total_count": null
      },
      {
        "display_name": "Cloud Solutions Architect",
        "kra_matches": null,
        "matched_count": null,
        "matched_skills": null,
        "role_id": 335,
        "score": 1.0,
        "slug": "cloud-solutions-architect",
        "total_count": null
      }
    ],
    "kra_match_roles": [
      {
        "display_name": "Data Engineer",
        "kra_matches": [
          {
            "kra_text": "Develops batch and real-time streaming data pipelines using Apache Spark, Apache Kafka, Apache Flink, or Airflow for data movement and processing at scale.",
            "sentence": "Production experience working with Data integration technologies such as Spark, Kafka, event/streaming, Streamsets, Matillion, Fivetran, HVR, NiFi, AWS Data Migration Services, Azure DataFactory Google DataProc or others",
            "similarity": 0.667
          },
          {
            "kra_text": "Optimizes pipeline throughput, partitioning strategies, and query performance across cloud data warehouses like Snowflake, BigQuery, or Redshift.",
            "sentence": "Extensive experience in providing architectural guidance and operational support across a large user base for a cloud-native data warehouse (Snowflake and/or Redshift)",
            "similarity": 0.6099
          },
          {
            "kra_text": "Builds data ingestion pipelines to collect data from transactional databases, third-party APIs, event streams, and file sources into centralized data platforms.",
            "sentence": "Operate and manage modern data platforms - from streaming, to data lakes, to analytics, and beyond across a progressively evolving technical stack.",
            "similarity": 0.5707
          }
        ],
        "matched_count": null,
        "matched_skills": null,
        "role_id": 2,
        "score": 0.6159,
        "slug": "data-engineer",
        "total_count": null
      },
      {
        "display_name": "DevOps Engineer",
        "kra_matches": [
          {
            "kra_text": "Provisions and manages cloud infrastructure on AWS, Azure, or GCP using Terraform or CloudFormation to enforce infrastructure-as-code standards.",
            "sentence": "Working experience with infrastructure as code using Terraform or Cloud Formation",
            "similarity": 0.7016
          },
          {
            "kra_text": "Builds and maintains CI/CD pipelines using Jenkins, GitHub Actions, GitLab CI, or CircleCI to automate build, test, security scanning, and deployment workflows.",
            "sentence": "Well versed with continuous integration and deployment frameworks with hands-on experience using CI/CD tools like Bitbucket, Github, Flyway, Liquibase",
            "similarity": 0.565
          },
          {
            "kra_text": "Provisions and manages cloud infrastructure on AWS, Azure, or GCP using Terraform or CloudFormation to enforce infrastructure-as-code standards.",
            "sentence": "Expertise with cloud-native data technologies in AWS or Azure",
            "similarity": 0.5074
          }
        ],
        "matched_count": null,
        "matched_skills": null,
        "role_id": 10,
        "score": 0.5913,
        "slug": "devops-engineer",
        "total_count": null
      },
      {
        "display_name": "Cloud Architect",
        "kra_matches": [
          {
            "kra_text": "Defines cloud adoption roadmaps, lift-and-shift vs. refactor migration strategies, and landing zone architectures for workloads moving to AWS, Azure, or GCP.",
            "sentence": "Expertise with cloud-native data technologies in AWS or Azure",
            "similarity": 0.5647
          },
          {
            "kra_text": "Conducts architecture reviews, approves technical design documents, and guides engineering teams through cloud migration and modernization projects.",
            "sentence": "Provide thought leadership by recommending the right technologies and approaches to maturing and solving problems to help ensure performance, security, scalability, and user satisfaction.",
            "similarity": 0.5463
          },
          {
            "kra_text": "Defines cloud adoption roadmaps, lift-and-shift vs. refactor migration strategies, and landing zone architectures for workloads moving to AWS, Azure, or GCP.",
            "sentence": "Proactively identify and articulate best practices and improvements to customers on how to best leverage their Cloud Data Platform.",
            "similarity": 0.5114
          }
        ],
        "matched_count": null,
        "matched_skills": null,
        "role_id": 9,
        "score": 0.5408,
        "slug": "cloud-architect",
        "total_count": null
      },
      {
        "display_name": "Scala Backend Developer",
        "kra_matches": [
          {
            "kra_text": "performance and reliability tuning",
            "sentence": "Strong troubleshooting and performance tuning skills including database administration",
            "similarity": 0.6321
          },
          {
            "kra_text": "backend workflow orchestration",
            "sentence": "Production experience working with Workflow Management and Orchestration such as Airflow, AWS Managed Airflow, Luigi, NiFi",
            "similarity": 0.5435
          },
          {
            "kra_text": "service endpoint development",
            "sentence": "Client-facing written and verbal communication skills and experience",
            "similarity": 0.413
          }
        ],
        "matched_count": null,
        "matched_skills": null,
        "role_id": 87,
        "score": 0.5295,
        "slug": "scala-backend-developer",
        "total_count": null
      },
      {
        "display_name": "Cloud Security Engineer",
        "kra_matches": [
          {
            "kra_text": "Designs and implements cloud security controls including KMS encryption, secrets management, and data-at-rest protection for AWS, Azure, or GCP workloads.",
            "sentence": "Expertise with cloud-native data technologies in AWS or Azure",
            "similarity": 0.5635
          },
          {
            "kra_text": "Designs and implements cloud security controls including KMS encryption, secrets management, and data-at-rest protection for AWS, Azure, or GCP workloads.",
            "sentence": "Production support experience in large enterprise environment supporting cloud data platforms and a variety of cloud data technologies (AWS Services/S3, Aurora Athena Redshift, Sagemaker, Azure DevOps/SQL, Azure Data Factory, etc. ....",
            "similarity": 0.5312
          },
          {
            "kra_text": "Documents cloud security standards, approved architecture patterns, security exceptions, and remediation guidance for engineering teams.",
            "sentence": "Provide thought leadership by recommending the right technologies and approaches to maturing and solving problems to help ensure performance, security, scalability, and user satisfaction.",
            "similarity": 0.4915
          }
        ],
        "matched_count": null,
        "matched_skills": null,
        "role_id": 23,
        "score": 0.5288,
        "slug": "cloud-security-engineer",
        "total_count": null
      }
    ],
    "skill_match_roles": [
      {
        "display_name": "Data Engineer",
        "kra_matches": null,
        "matched_count": 5,
        "matched_skills": [
          "AWS",
          "Amazon Redshift",
          "Azure",
          "SQL",
          "Snowflake"
        ],
        "role_id": 2,
        "score": 1.0,
        "slug": "data-engineer",
        "total_count": 5
      },
      {
        "display_name": "Engineering Manager",
        "kra_matches": null,
        "matched_count": 3,
        "matched_skills": [
          "AWS",
          "Azure",
          "SQL"
        ],
        "role_id": 121,
        "score": 0.6,
        "slug": "engineering-manager",
        "total_count": 5
      },
      {
        "display_name": "ML Engineer",
        "kra_matches": null,
        "matched_count": 2,
        "matched_skills": [
          "AWS",
          "Azure"
        ],
        "role_id": 3,
        "score": 0.4,
        "slug": "ml-engineer",
        "total_count": 5
      },
      {
        "display_name": "Cloud Architect",
        "kra_matches": null,
        "matched_count": 2,
        "matched_skills": [
          "AWS",
          "Azure"
        ],
        "role_id": 9,
        "score": 0.4,
        "slug": "cloud-architect",
        "total_count": 5
      },
      {
        "display_name": "Cyber Security Engineer",
        "kra_matches": null,
        "matched_count": 2,
        "matched_skills": [
          "AWS",
          "Azure"
        ],
        "role_id": 5,
        "score": 0.4,
        "slug": "cybersecurity-engineer",
        "total_count": 5
      }
    ]
  },
  "stage4_decision": {
    "alias_collision_detected": true,
    "case": "A",
    "chosen_role": {
      "display_name": "Cloud Architect",
      "kra_matches": null,
      "matched_count": null,
      "matched_skills": null,
      "role_id": 9,
      "score": 1.0,
      "slug": "cloud-architect",
      "total_count": null
    },
    "confidence": 0.95,
    "is_new_role": false,
    "llm2_fired": false,
    "llm2_reasoning": null,
    "matched_dimensions": [],
    "matched_kras": [],
    "matched_skills": [],
    "new_role_display_name": null,
    "new_role_slug": null,
    "queued": false,
    "reasoning": "Multi-alias tie (3 roles at 1.0) resolved by TIER_B_TITLE: Cloud Solutions Architect",
    "sub_role": {
      "confidence": 0.95,
      "display_name": "Cloud Solutions Architect",
      "reasoning": "hoisted from chosen leaf (case=A)",
      "role_id": 335,
      "slug": "cloud-solutions-architect"
    }
  },
  "stage5_updates": null
}
API 2 — extract-details
{
  "alias_matches": [
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 271,
      "existing_alias_text": "SQL",
      "input_term": "SQL",
      "matched_canonical": {
        "category_id": 6,
        "display_name": "SQL",
        "id": 101,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "LANGUAGE",
        "slug": "sql",
        "sub_category_id": 97,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 299,
      "existing_alias_text": "Snowflake",
      "input_term": "Snowflake",
      "matched_canonical": {
        "category_id": 9,
        "display_name": "Snowflake",
        "id": 105,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "PLATFORM",
        "slug": "snowflake",
        "sub_category_id": 113,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 301,
      "existing_alias_text": "Amazon Redshift",
      "input_term": "Amazon Redshift",
      "matched_canonical": {
        "category_id": 11,
        "display_name": "Amazon Redshift",
        "id": 107,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "CLOUD_SERVICE",
        "slug": "amazon-redshift",
        "sub_category_id": 118,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 406,
      "existing_alias_text": "AWS",
      "input_term": "AWS",
      "matched_canonical": {
        "category_id": 9,
        "display_name": "AWS",
        "id": 187,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "PLATFORM",
        "slug": "aws",
        "sub_category_id": 46,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 407,
      "existing_alias_text": "Azure",
      "input_term": "Azure",
      "matched_canonical": {
        "category_id": 9,
        "display_name": "Azure",
        "id": 188,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "PLATFORM",
        "slug": "azure",
        "sub_category_id": 46,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 741,
      "existing_alias_text": "Google Cloud Platform",
      "input_term": "Google Cloud Platform",
      "matched_canonical": {
        "category_id": 9,
        "display_name": "Google Cloud Platform",
        "id": 425,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "PLATFORM",
        "slug": "google-cloud-platform",
        "sub_category_id": 46,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 2010,
      "existing_alias_text": "Hadoop",
      "input_term": "Hadoop",
      "matched_canonical": {
        "category_id": 5,
        "display_name": "Hadoop",
        "id": 1351,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "FRAMEWORK",
        "slug": "hadoop",
        "sub_category_id": 91,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 1838,
      "existing_alias_text": "Databricks",
      "input_term": "Databricks",
      "matched_canonical": {
        "category_id": 9,
        "display_name": "Databricks",
        "id": 1202,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "PLATFORM",
        "slug": "databricks",
        "sub_category_id": 911,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 379,
      "existing_alias_text": "Amazon S3",
      "input_term": "Amazon S3",
      "matched_canonical": {
        "category_id": 11,
        "display_name": "Amazon S3",
        "id": 170,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "CLOUD_SERVICE",
        "slug": "amazon-s3",
        "sub_category_id": 120,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "TODO: REMOVE AFTER TESTING \u2014 alias DB write disabled",
      "alias_persisted": false,
      "existing_alias_id": 381,
      "existing_alias_text": "Azure Blob Storage",
      "input_term": "Azure Data Lake Storage",
      "matched_canonical": {
        "category_id": 11,
        "display_name": "Azure Blob Storage",
        "id": 172,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "CLOUD_SERVICE",
        "slug": "azure-blob-storage",
        "sub_category_id": 120,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "matched_via": "embedding_alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 380,
      "existing_alias_text": "Google Cloud Storage",
      "input_term": "Google Cloud Storage",
      "matched_canonical": {
        "category_id": 11,
        "display_name": "Google Cloud Storage",
        "id": 171,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "CLOUD_SERVICE",
        "slug": "google-cloud-storage",
        "sub_category_id": 120,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 4648,
      "existing_alias_text": "Elasticsearch",
      "input_term": "Elasticsearch",
      "matched_canonical": {
        "category_id": 3,
        "display_name": "Elasticsearch",
        "id": 3171,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "TOOL",
        "slug": "elasticsearch",
        "sub_category_id": 2925,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 4647,
      "existing_alias_text": "Solr",
      "input_term": "Solr",
      "matched_canonical": {
        "category_id": 3,
        "display_name": "Solr",
        "id": 3170,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "TOOL",
        "slug": "solr",
        "sub_category_id": 3284,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 2013,
      "existing_alias_text": "Cassandra",
      "input_term": "Cassandra",
      "matched_canonical": {
        "category_id": 3,
        "display_name": "Cassandra",
        "id": 1354,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "TOOL",
        "slug": "cassandra",
        "sub_category_id": 31,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 2004,
      "existing_alias_text": "Apache Spark",
      "input_term": "Apache Spark",
      "matched_canonical": {
        "category_id": 5,
        "display_name": "Apache Spark",
        "id": 1350,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "FRAMEWORK",
        "slug": "apache-spark",
        "sub_category_id": 1021,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 349,
      "existing_alias_text": "Apache Kafka",
      "input_term": "Apache Kafka",
      "matched_canonical": {
        "category_id": 13,
        "display_name": "Apache Kafka",
        "id": 145,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "TOOL",
        "slug": "apache-kafka",
        "sub_category_id": 128,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 312,
      "existing_alias_text": "Matillion",
      "input_term": "Matillion",
      "matched_canonical": {
        "category_id": 9,
        "display_name": "Matillion",
        "id": 118,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "PLATFORM",
        "slug": "matillion",
        "sub_category_id": 114,
        "typical_lifespan": "EVERGREEN",
        "volatility": "EMERGING"
      },
      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 310,
      "existing_alias_text": "Fivetran",
      "input_term": "Fivetran",
      "matched_canonical": {
        "category_id": 9,
        "display_name": "Fivetran",
        "id": 116,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "PLATFORM",
        "slug": "fivetran",
        "sub_category_id": 114,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 304,
      "existing_alias_text": "Apache Airflow",
      "input_term": "Apache Airflow",
      "matched_canonical": {
        "category_id": 13,
        "display_name": "Apache Airflow",
        "id": 110,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "TOOL",
        "slug": "apache-airflow",
        "sub_category_id": 130,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 308,
      "existing_alias_text": "Luigi",
      "input_term": "Luigi",
      "matched_canonical": {
        "category_id": 5,
        "display_name": "Luigi",
        "id": 114,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "FRAMEWORK",
        "slug": "luigi",
        "sub_category_id": 95,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 547,
      "existing_alias_text": "Terraform",
      "input_term": "Terraform",
      "matched_canonical": {
        "category_id": 13,
        "display_name": "Terraform",
        "id": 286,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "TOOL",
        "slug": "terraform",
        "sub_category_id": 191,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 1382,
      "existing_alias_text": "CloudFormation",
      "input_term": "CloudFormation",
      "matched_canonical": {
        "category_id": 11,
        "display_name": "CloudFormation",
        "id": 837,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "CLOUD_SERVICE",
        "slug": "cloudformation",
        "sub_category_id": 181,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 67,
      "existing_alias_text": "Python",
      "input_term": "Python",
      "matched_canonical": {
        "category_id": 6,
        "display_name": "Python",
        "id": 5,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "LANGUAGE",
        "slug": "python",
        "sub_category_id": 96,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 1826,
      "existing_alias_text": "CI/CD",
      "input_term": "CI/CD",
      "matched_canonical": {
        "category_id": 8,
        "display_name": "CI/CD",
        "id": 1190,
        "is_also_category": false,
        "is_extractable": true,
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          "slug": "cloud-security-engineer",
          "source": "db"
        },
        {
          "display_name": "Cyber Security Engineer",
          "id": 5,
          "rationale": null,
          "role_archetype": null,
          "slug": "cybersecurity-engineer",
          "source": "db"
        }
      ]
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Vendor Product Families",
        "id": 477,
        "rationale": "Coordinate usage, licensing, and architecture decisions for major vendor software and cloud product families.",
        "slug": "vendor-product-families",
        "source": "db"
      },
      "input_skill": "Azure",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": "Engineering Manager",
          "id": 121,
          "rationale": null,
          "role_archetype": null,
          "slug": "engineering-manager",
          "source": "db"
        }
      ]
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Cloud \u0026 Hosting Providers",
        "id": 414,
        "rationale": "Knowledge of major cloud and hosting vendor platforms for deploying and managing PHP applications.",
        "slug": "cloud-hosting-providers",
        "source": "db"
      },
      "input_skill": "Google Cloud Platform",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": "PHP Backend Developer",
          "id": 86,
          "rationale": null,
          "role_archetype": "Engineering",
          "slug": "php-backend-developer",
          "source": "db"
        }
      ]
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Cloud Provider Platforms",
        "id": 131,
        "rationale": "Major cloud platforms and their core service ecosystems used to design target-state architectures, choose deployment boundaries, and evaluate managed capabilities. This is the primary substrate for cloud architecture decisions.",
        "slug": "cloud-provider-platforms",
        "source": "db"
      },
      "input_skill": "Google Cloud Platform",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": "Cloud Architect",
          "id": 9,
          "rationale": null,
          "role_archetype": null,
          "slug": "cloud-architect",
          "source": "db"
        },
        {
          "display_name": "Cloud Security Engineer",
          "id": 23,
          "rationale": null,
          "role_archetype": null,
          "slug": "cloud-security-engineer",
          "source": "db"
        }
      ]
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Cloud Security Posture Tools",
        "id": 64,
        "rationale": "Cloud-native security platforms used to assess misconfiguration, workload exposure, and cloud control coverage. This dimension includes the major CNAPP/CSPM/CWPP vendors and cloud security services the role reviews and tunes.",
        "slug": "cloud-security-posture-tools",
        "source": "db"
      },
      "input_skill": "Google Cloud Platform",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": "Cloud Security Engineer",
          "id": 23,
          "rationale": null,
          "role_archetype": null,
          "slug": "cloud-security-engineer",
          "source": "db"
        },
        {
          "display_name": "Cyber Security Engineer",
          "id": 5,
          "rationale": null,
          "role_archetype": null,
          "slug": "cybersecurity-engineer",
          "source": "db"
        }
      ]
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Vendor Product Families",
        "id": 477,
        "rationale": "Coordinate usage, licensing, and architecture decisions for major vendor software and cloud product families.",
        "slug": "vendor-product-families",
        "source": "db"
      },
      "input_skill": "Google Cloud Platform",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": "Engineering Manager",
          "id": 121,
          "rationale": null,
          "role_archetype": null,
          "slug": "engineering-manager",
          "source": "db"
        }
      ]
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "ETL and ELT Tooling",
        "id": 24,
        "rationale": "Packaged tools for extracting, loading, and transforming data across systems. This dimension covers connector-based ingestion, transformation frameworks, and managed integration products.",
        "slug": "etl-and-elt-tooling",
        "source": "db"
      },
      "input_skill": "Hadoop",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": "Data Engineer",
          "id": 2,
          "rationale": null,
          "role_archetype": null,
          "slug": "data-engineer",
          "source": "db"
        }
      ]
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "React Frontend Development",
        "id": 96,
        "rationale": "Building interactive web user interfaces with React.js, including component composition, state management, hooks, and rendering patterns. React.js belongs here because it is a core library for client-side UI development in modern web applications.",
        "slug": "d_init_01",
        "source": "db"
      },
      "input_skill": "Databricks",
      "llm_role": null,
      "roles_from_db": []
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Cloud Storage and Data Services",
        "id": 144,
        "rationale": "Cloud-native storage and managed data services used to place workloads, choose durability tiers, and define platform boundaries. This is a coherent cluster because architects evaluate storage fit, access patterns, and managed service tradeoffs.",
        "slug": "cloud-storage-and-data-services",
        "source": "db"
      },
      "input_skill": "Amazon S3",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": "Cloud Architect",
          "id": 9,
          "rationale": null,
          "role_archetype": null,
          "slug": "cloud-architect",
          "source": "db"
        }
      ]
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Cloud Storage and File Formats",
        "id": 35,
        "rationale": "Object storage and data file formats used as the physical substrate for data movement and lake-style analytics. Data engineers need these to manage landing zones, partitioned datasets, and efficient interchange.",
        "slug": "cloud-storage-and-file-formats",
        "source": "db"
      },
      "input_skill": "Amazon S3",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": "Data Engineer",
          "id": 2,
          "rationale": null,
          "role_archetype": null,
          "slug": "data-engineer",
          "source": "db"
        }
      ]
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Cloud Storage and Data Services",
        "id": 144,
        "rationale": "Cloud-native storage and managed data services used to place workloads, choose durability tiers, and define platform boundaries. This is a coherent cluster because architects evaluate storage fit, access patterns, and managed service tradeoffs.",
        "slug": "cloud-storage-and-data-services",
        "source": "db"
      },
      "input_skill": "Azure Data Lake Storage",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": "Cloud Architect",
          "id": 9,
          "rationale": null,
          "role_archetype": null,
          "slug": "cloud-architect",
          "source": "db"
        }
      ]
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Cloud Storage and File Formats",
        "id": 35,
        "rationale": "Object storage and data file formats used as the physical substrate for data movement and lake-style analytics. Data engineers need these to manage landing zones, partitioned datasets, and efficient interchange.",
        "slug": "cloud-storage-and-file-formats",
        "source": "db"
      },
      "input_skill": "Azure Data Lake Storage",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": "Data Engineer",
          "id": 2,
          "rationale": null,
          "role_archetype": null,
          "slug": "data-engineer",
          "source": "db"
        }
      ]
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Cloud Storage and Data Services",
        "id": 144,
        "rationale": "Cloud-native storage and managed data services used to place workloads, choose durability tiers, and define platform boundaries. This is a coherent cluster because architects evaluate storage fit, access patterns, and managed service tradeoffs.",
        "slug": "cloud-storage-and-data-services",
        "source": "db"
      },
      "input_skill": "Google Cloud Storage",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": "Cloud Architect",
          "id": 9,
          "rationale": null,
          "role_archetype": null,
          "slug": "cloud-architect",
          "source": "db"
        }
      ]
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Cloud Storage and File Formats",
        "id": 35,
        "rationale": "Object storage and data file formats used as the physical substrate for data movement and lake-style analytics. Data engineers need these to manage landing zones, partitioned datasets, and efficient interchange.",
        "slug": "cloud-storage-and-file-formats",
        "source": "db"
      },
      "input_skill": "Google Cloud Storage",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": "Data Engineer",
          "id": 2,
          "rationale": null,
          "role_archetype": null,
          "slug": "data-engineer",
          "source": "db"
        }
      ]
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Magento Search and Indexing",
        "id": 403,
        "rationale": "Index-driven storefront data freshness and search behavior. This cluster is coherent because Magento commerce sites depend on indexing to keep catalog, price, and search results aligned with changing data.",
        "slug": "magento-search-and-indexing",
        "source": "db"
      },
      "input_skill": "Elasticsearch",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": "Magento Dev",
          "id": 231,
          "rationale": null,
          "role_archetype": "Engineering",
          "slug": "magento-dev",
          "source": "db"
        }
      ]
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Search and Content Discovery",
        "id": 356,
        "rationale": "Implementing site search, indexing, and content discovery features in Drupal. This cluster is coherent because many Drupal sites need structured search experiences beyond basic navigation.",
        "slug": "search-and-content-discovery",
        "source": "db"
      },
      "input_skill": "Elasticsearch",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": "Drupal Dev",
          "id": 228,
          "rationale": null,
          "role_archetype": "Engineering",
          "slug": "drupal-dev",
          "source": "db"
        },
        {
          "display_name": "Sitecore Dev",
          "id": 233,
          "rationale": null,
          "role_archetype": "Engineering",
          "slug": "sitecore-dev",
          "source": "db"
        }
      ]
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Search and Content Discovery",
        "id": 356,
        "rationale": "Implementing site search, indexing, and content discovery features in Drupal. This cluster is coherent because many Drupal sites need structured search experiences beyond basic navigation.",
        "slug": "search-and-content-discovery",
        "source": "db"
      },
      "input_skill": "Solr",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": "Drupal Dev",
          "id": 228,
          "rationale": null,
          "role_archetype": "Engineering",
          "slug": "drupal-dev",
          "source": "db"
        },
        {
          "display_name": "Sitecore Dev",
          "id": 233,
          "rationale": null,
          "role_archetype": "Engineering",
          "slug": "sitecore-dev",
          "source": "db"
        }
      ]
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Cloud Storage and Data Services",
        "id": 144,
        "rationale": "Cloud-native storage and managed data services used to place workloads, choose durability tiers, and define platform boundaries. This is a coherent cluster because architects evaluate storage fit, access patterns, and managed service tradeoffs.",
        "slug": "cloud-storage-and-data-services",
        "source": "db"
      },
      "input_skill": "Cassandra",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": "Cloud Architect",
          "id": 9,
          "rationale": null,
          "role_archetype": null,
          "slug": "cloud-architect",
          "source": "db"
        }
      ]
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "ETL and ELT Tooling",
        "id": 24,
        "rationale": "Packaged tools for extracting, loading, and transforming data across systems. This dimension covers connector-based ingestion, transformation frameworks, and managed integration products.",
        "slug": "etl-and-elt-tooling",
        "source": "db"
      },
      "input_skill": "Apache Spark",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": "Data Engineer",
          "id": 2,
          "rationale": null,
          "role_archetype": null,
          "slug": "data-engineer",
          "source": "db"
        }
      ]
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Messaging and Event Streaming",
        "id": 8,
        "rationale": "Transport-layer systems used to move events and decouple producers from consumers. Data engineers use these systems to ingest, buffer, and distribute event data before downstream processing.",
        "slug": "messaging-and-event-streaming",
        "source": "db"
      },
      "input_skill": "Apache Kafka",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": "Backend Developer",
          "id": 1,
          "rationale": null,
          "role_archetype": "A Backend Engineer designs, builds, and maintains the server-side logic and data handling that power applications and services. They focus on implementing reliable business functionality, integrating with other systems, and ensuring the backend is scalable, maintainable, and observable.",
          "slug": "backend-engineer",
          "source": "db"
        },
        {
          "display_name": "Data Engineer",
          "id": 2,
          "rationale": null,
          "role_archetype": null,
          "slug": "data-engineer",
          "source": "db"
        }
      ]
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "ETL and ELT Tooling",
        "id": 24,
        "rationale": "Packaged tools for extracting, loading, and transforming data across systems. This dimension covers connector-based ingestion, transformation frameworks, and managed integration products.",
        "slug": "etl-and-elt-tooling",
        "source": "db"
      },
      "input_skill": "Matillion",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": "Data Engineer",
          "id": 2,
          "rationale": null,
          "role_archetype": null,
          "slug": "data-engineer",
          "source": "db"
        }
      ]
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "ETL and ELT Tooling",
        "id": 24,
        "rationale": "Packaged tools for extracting, loading, and transforming data across systems. This dimension covers connector-based ingestion, transformation frameworks, and managed integration products.",
        "slug": "etl-and-elt-tooling",
        "source": "db"
      },
      "input_skill": "Fivetran",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": "Data Engineer",
          "id": 2,
          "rationale": null,
          "role_archetype": null,
          "slug": "data-engineer",
          "source": "db"
        }
      ]
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Data Pipeline Orchestration",
        "id": 23,
        "rationale": "Workflow engines that schedule, coordinate, and recover batch data jobs. This cluster covers dependency management, retries, backfills, sensors, and operational control of pipeline DAGs.",
        "slug": "data-pipeline-orchestration",
        "source": "db"
      },
      "input_skill": "Apache Airflow",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": "Data Engineer",
          "id": 2,
          "rationale": null,
          "role_archetype": null,
          "slug": "data-engineer",
          "source": "db"
        }
      ]
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Data Pipeline Orchestration",
        "id": 23,
        "rationale": "Workflow engines that schedule, coordinate, and recover batch data jobs. This cluster covers dependency management, retries, backfills, sensors, and operational control of pipeline DAGs.",
        "slug": "data-pipeline-orchestration",
        "source": "db"
      },
      "input_skill": "Luigi",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": "Data Engineer",
          "id": 2,
          "rationale": null,
          "role_archetype": null,
          "slug": "data-engineer",
          "source": "db"
        }
      ]
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Infrastructure \u0026 Security Automation Frameworks",
        "id": 249,
        "rationale": "Frameworks and libraries for provisioning, configuring, and automating cloud security infrastructure.",
        "slug": "infrastructure-security-automation-frameworks",
        "source": "db"
      },
      "input_skill": "Terraform",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": "Cloud Security Engineer",
          "id": 23,
          "rationale": null,
          "role_archetype": null,
          "slug": "cloud-security-engineer",
          "source": "db"
        }
      ]
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Infrastructure as Code",
        "id": 132,
        "rationale": "Declarative provisioning and environment definition tools used to codify cloud infrastructure, repeatable environments, and platform standards. Cloud Architects use these to express reference architectures and guardrails.",
        "slug": "infrastructure-as-code",
        "source": "db"
      },
      "input_skill": "Terraform",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": "Cloud Architect",
          "id": 9,
          "rationale": null,
          "role_archetype": null,
          "slug": "cloud-architect",
          "source": "db"
        },
        {
          "display_name": "DevOps Engineer",
          "id": 10,
          "rationale": null,
          "role_archetype": null,
          "slug": "devops-engineer",
          "source": "db"
        }
      ]
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Infrastructure as Code for ML",
        "id": 57,
        "rationale": "Tools for provisioning and managing ML infrastructure resources through code.",
        "slug": "infrastructure-as-code-for-ml",
        "source": "db"
      },
      "input_skill": "Terraform",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": "ML Engineer",
          "id": 3,
          "rationale": null,
          "role_archetype": null,
          "slug": "ml-engineer",
          "source": "db"
        }
      ]
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Infrastructure as Code",
        "id": 132,
        "rationale": "Declarative provisioning and environment definition tools used to codify cloud infrastructure, repeatable environments, and platform standards. Cloud Architects use these to express reference architectures and guardrails.",
        "slug": "infrastructure-as-code",
        "source": "db"
      },
      "input_skill": "CloudFormation",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": "Cloud Architect",
          "id": 9,
          "rationale": null,
          "role_archetype": null,
          "slug": "cloud-architect",
          "source": "db"
        },
        {
          "display_name": "DevOps Engineer",
          "id": 10,
          "rationale": null,
          "role_archetype": null,
          "slug": "devops-engineer",
          "source": "db"
        }
      ]
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Cloud Security Scripting \u0026 DSL Languages",
        "id": 248,
        "rationale": "Proficiency in programming and domain-specific languages used to automate and script cloud security controls.",
        "slug": "cloud-security-scripting-dsl-languages",
        "source": "db"
      },
      "input_skill": "Python",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": "Cloud Security Engineer",
          "id": 23,
          "rationale": null,
          "role_archetype": null,
          "slug": "cloud-security-engineer",
          "source": "db"
        }
      ]
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Programming Languages",
        "id": 1,
        "rationale": "Primary implementation languages used to build client and server feature code. Full stack engineers need enough fluency to move across layers and implement product behavior end to end.",
        "slug": "programming-languages",
        "source": "db"
      },
      "input_skill": "Python",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": "Backend Developer",
          "id": 1,
          "rationale": null,
          "role_archetype": "A Backend Engineer designs, builds, and maintains the server-side logic and data handling that power applications and services. They focus on implementing reliable business functionality, integrating with other systems, and ensuring the backend is scalable, maintainable, and observable.",
          "slug": "backend-engineer",
          "source": "db"
        },
        {
          "display_name": "Fullstack Developer",
          "id": 435,
          "rationale": null,
          "role_archetype": "Engineering",
          "slug": "fullstack-developer",
          "source": "db"
        },
        {
          "display_name": "Fullstack Developer",
          "id": 15,
          "rationale": null,
          "role_archetype": null,
          "slug": "full-stack-engineer",
          "source": "db"
        }
      ]
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Programming Languages \u0026 DSLs",
        "id": 475,
        "rationale": "Oversee and guide the selection and effective use of programming and domain\u2010specific languages in software projects.",
        "slug": "programming-languages-dsls",
        "source": "db"
      },
      "input_skill": "Python",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": "Engineering Manager",
          "id": 121,
          "rationale": null,
          "role_archetype": null,
          "slug": "engineering-manager",
          "source": "db"
        }
      ]
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Programming Languages and Scripting",
        "id": 59,
        "rationale": "Languages used to write security automation, analysis scripts, detection logic, and remediation helpers. This is the primary implementation surface for a cybersecurity engineer across tooling and response workflows.",
        "slug": "programming-languages-and-scripting",
        "source": "db"
      },
      "input_skill": "Python",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": "Cyber Security Engineer",
          "id": 5,
          "rationale": null,
          "role_archetype": null,
          "slug": "cybersecurity-engineer",
          "source": "db"
        }
      ]
    },
    {
      "dimension": {
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            "rationale": "Workflow engines that schedule, coordinate, and recover batch data jobs. This cluster covers dependency management, retries, backfills, sensors, and operational control of pipeline DAGs.",
            "slug": "data-pipeline-orchestration",
            "source": "db"
          },
          "input_skill": "Luigi",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "Data Engineer",
              "id": 2,
              "rationale": null,
              "role_archetype": null,
              "slug": "data-engineer",
              "source": "db"
            }
          ]
        }
      ],
      "input_skill": "Luigi",
      "matched_via": "alias",
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": null,
      "source_tag": "db",
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    },
    {
      "aliases_in_db": [
        {
          "alias_text": "Terraform",
          "alias_type": "CANONICAL",
          "id": 547,
          "is_primary": true,
          "match_strategy": "CASE_INSENSITIVE"
        }
      ],
      "canonical": {
        "category_id": 13,
        "display_name": "Terraform",
        "id": 286,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "TOOL",
        "slug": "terraform",
        "sub_category_id": 191,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "dimensions": [
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Infrastructure \u0026 Security Automation Frameworks",
            "id": 249,
            "rationale": "Frameworks and libraries for provisioning, configuring, and automating cloud security infrastructure.",
            "slug": "infrastructure-security-automation-frameworks",
            "source": "db"
          },
          "input_skill": "Terraform",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "Cloud Security Engineer",
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              "rationale": null,
              "role_archetype": null,
              "slug": "cloud-security-engineer",
              "source": "db"
            }
          ]
        },
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Infrastructure as Code",
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            "rationale": "Declarative provisioning and environment definition tools used to codify cloud infrastructure, repeatable environments, and platform standards. Cloud Architects use these to express reference architectures and guardrails.",
            "slug": "infrastructure-as-code",
            "source": "db"
          },
          "input_skill": "Terraform",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "Cloud Architect",
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              "rationale": null,
              "role_archetype": null,
              "slug": "cloud-architect",
              "source": "db"
            },
            {
              "display_name": "DevOps Engineer",
              "id": 10,
              "rationale": null,
              "role_archetype": null,
              "slug": "devops-engineer",
              "source": "db"
            }
          ]
        },
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Infrastructure as Code for ML",
            "id": 57,
            "rationale": "Tools for provisioning and managing ML infrastructure resources through code.",
            "slug": "infrastructure-as-code-for-ml",
            "source": "db"
          },
          "input_skill": "Terraform",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "ML Engineer",
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              "rationale": null,
              "role_archetype": null,
              "slug": "ml-engineer",
              "source": "db"
            }
          ]
        }
      ],
      "input_skill": "Terraform",
      "matched_via": "alias",
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": null,
      "source_tag": "db",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [
        {
          "alias_text": "CloudFormation",
          "alias_type": "CANONICAL",
          "id": 1382,
          "is_primary": true,
          "match_strategy": "CASE_INSENSITIVE"
        }
      ],
      "canonical": {
        "category_id": 11,
        "display_name": "CloudFormation",
        "id": 837,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "CLOUD_SERVICE",
        "slug": "cloudformation",
        "sub_category_id": 181,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "dimensions": [
        {
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            "slug": "infrastructure-as-code",
            "source": "db"
          },
          "input_skill": "CloudFormation",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "Cloud Architect",
              "id": 9,
              "rationale": null,
              "role_archetype": null,
              "slug": "cloud-architect",
              "source": "db"
            },
            {
              "display_name": "DevOps Engineer",
              "id": 10,
              "rationale": null,
              "role_archetype": null,
              "slug": "devops-engineer",
              "source": "db"
            }
          ]
        }
      ],
      "input_skill": "CloudFormation",
      "matched_via": "alias",
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": null,
      "source_tag": "db",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [
        {
          "alias_text": "Python",
          "alias_type": "CANONICAL",
          "id": 67,
          "is_primary": true,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "Python 2",
          "alias_type": "VERSION",
          "id": 72,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "Python 2.x",
          "alias_type": "VERSION",
          "id": 74,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "Python 3",
          "alias_type": "VERSION",
          "id": 73,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "Python 3.10",
          "alias_type": "VERSION",
          "id": 76,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "Python 3.11",
          "alias_type": "VERSION",
          "id": 77,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "Python 3.12",
          "alias_type": "VERSION",
          "id": 78,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "Python 3.x",
          "alias_type": "VERSION",
          "id": 75,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "py",
          "alias_type": "VERSION",
          "id": 2183,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "py2",
          "alias_type": "VERSION",
          "id": 68,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "py3",
          "alias_type": "VERSION",
          "id": 69,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "python 3",
          "alias_type": "VERSION",
          "id": 2186,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "python 3.x",
          "alias_type": "VERSION",
          "id": 2849,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "python2",
          "alias_type": "VERSION",
          "id": 70,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "python3",
          "alias_type": "VERSION",
          "id": 71,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "python3.x",
          "alias_type": "VERSION",
          "id": 2848,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        }
      ],
      "canonical": {
        "category_id": 6,
        "display_name": "Python",
        "id": 5,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "LANGUAGE",
        "slug": "python",
        "sub_category_id": 96,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "dimensions": [
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Cloud Security Scripting \u0026 DSL Languages",
            "id": 248,
            "rationale": "Proficiency in programming and domain-specific languages used to automate and script cloud security controls.",
            "slug": "cloud-security-scripting-dsl-languages",
            "source": "db"
          },
          "input_skill": "Python",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "Cloud Security Engineer",
              "id": 23,
              "rationale": null,
              "role_archetype": null,
              "slug": "cloud-security-engineer",
              "source": "db"
            }
          ]
        },
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Programming Languages",
            "id": 1,
            "rationale": "Primary implementation languages used to build client and server feature code. Full stack engineers need enough fluency to move across layers and implement product behavior end to end.",
            "slug": "programming-languages",
            "source": "db"
          },
          "input_skill": "Python",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "Backend Developer",
              "id": 1,
              "rationale": null,
              "role_archetype": "A Backend Engineer designs, builds, and maintains the server-side logic and data handling that power applications and services. They focus on implementing reliable business functionality, integrating with other systems, and ensuring the backend is scalable, maintainable, and observable.",
              "slug": "backend-engineer",
              "source": "db"
            },
            {
              "display_name": "Fullstack Developer",
              "id": 435,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "fullstack-developer",
              "source": "db"
            },
            {
              "display_name": "Fullstack Developer",
              "id": 15,
              "rationale": null,
              "role_archetype": null,
              "slug": "full-stack-engineer",
              "source": "db"
            }
          ]
        },
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Programming Languages \u0026 DSLs",
            "id": 475,
            "rationale": "Oversee and guide the selection and effective use of programming and domain\u2010specific languages in software projects.",
            "slug": "programming-languages-dsls",
            "source": "db"
          },
          "input_skill": "Python",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "Engineering Manager",
              "id": 121,
              "rationale": null,
              "role_archetype": null,
              "slug": "engineering-manager",
              "source": "db"
            }
          ]
        },
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Programming Languages and Scripting",
            "id": 59,
            "rationale": "Languages used to write security automation, analysis scripts, detection logic, and remediation helpers. This is the primary implementation surface for a cybersecurity engineer across tooling and response workflows.",
            "slug": "programming-languages-and-scripting",
            "source": "db"
          },
          "input_skill": "Python",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "Cyber Security Engineer",
              "id": 5,
              "rationale": null,
              "role_archetype": null,
              "slug": "cybersecurity-engineer",
              "source": "db"
            }
          ]
        },
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Programming Languages for Data Work",
            "id": 21,
            "rationale": "Languages used to implement data pipelines, transformations, and operational glue. This is the primary coding surface for building ingestion, enrichment, and automation logic in data engineering.",
            "slug": "programming-languages-for-data-work",
            "source": "db"
          },
          "input_skill": "Python",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "Data Engineer",
              "id": 2,
              "rationale": null,
              "role_archetype": null,
              "slug": "data-engineer",
              "source": "db"
            }
          ]
        },
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Programming Languages for ML Systems",
            "id": 39,
            "rationale": "Languages used to build training code, inference services, evaluation jobs, and ML glue code. This is the primary implementation surface for ML engineers across experimentation and productionization.",
            "slug": "programming-languages-for-ml-systems",
            "source": "db"
          },
          "input_skill": "Python",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "ML Engineer",
              "id": 3,
              "rationale": null,
              "role_archetype": null,
              "slug": "ml-engineer",
              "source": "db"
            },
            {
              "display_name": "MLOps Engineer",
              "id": 16,
              "rationale": null,
              "role_archetype": null,
              "slug": "ml-ops-engineer",
              "source": "db"
            }
          ]
        },
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Programming Languages for XR",
            "id": 97,
            "rationale": "Primary implementation languages used to build immersive client features, interaction logic, and device-specific runtime behavior. This is the core coding surface for AR/VR experiences.",
            "slug": "programming-languages-for-xr",
            "source": "db"
          },
          "input_skill": "Python",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "AR/VR Engineer",
              "id": 8,
              "rationale": null,
              "role_archetype": null,
              "slug": "ar-vr-engineer",
              "source": "db"
            }
          ]
        },
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Python Programming",
            "id": 290,
            "rationale": "Core Python language skills used to implement backend business logic, request handlers, integrations, and service internals. This is the primary coding surface for the role.",
            "slug": "python-programming",
            "source": "db"
          },
          "input_skill": "Python",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "Python Backend Developer",
              "id": 80,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "python-backend-developer",
              "source": "db"
            }
          ]
        }
      ],
      "input_skill": "Python",
      "matched_via": "alias",
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": null,
      "source_tag": "db",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [
        {
          "alias_text": "CI/CD",
          "alias_type": "CANONICAL",
          "id": 1826,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        }
      ],
      "canonical": {
        "category_id": 8,
        "display_name": "CI/CD",
        "id": 1190,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "METHODOLOGY",
        "slug": "ci-cd",
        "sub_category_id": 900,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "dimensions": [
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "CI/CD Pipeline Platforms",
            "id": 150,
            "rationale": "Systems used to define, run, and maintain automated build and deployment workflows. This cluster is coherent because the role owns delivery automation end to end, including pipeline reliability and promotion logic.",
            "slug": "ci-cd-pipeline-platforms",
            "source": "db"
          },
          "input_skill": "CI/CD",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "DevOps Engineer",
              "id": 10,
              "rationale": null,
              "role_archetype": null,
              "slug": "devops-engineer",
              "source": "db"
            }
          ]
        },
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "CI/CD for Machine Learning",
            "id": 56,
            "rationale": "Tools and platforms for automating ML model integration, testing, and deployment pipelines.",
            "slug": "ci-cd-for-machine-learning",
            "source": "db"
          },
          "input_skill": "CI/CD",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "ML Engineer",
              "id": 3,
              "rationale": null,
              "role_archetype": null,
              "slug": "ml-engineer",
              "source": "db"
            }
          ]
        }
      ],
      "input_skill": "CI/CD",
      "matched_via": "alias",
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": null,
      "source_tag": "db",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "Bitbucket",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Version Control Tools",
          "skill_nature": "TOOL",
          "sub_category": "general",
          "typical_lifespan": "MULTI_YEAR",
          "version_strategy": "UNVERSIONED",
          "volatility": "MEDIUM"
        },
        "enrichment": null,
        "keep_log": [],
        "locked_dimensions": [],
        "merge_log": [],
        "placed": null,
        "relationships": null,
        "skill_id": "bitbucket",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [
        {
          "alias_text": "GitHub",
          "alias_type": "CANONICAL",
          "id": 541,
          "is_primary": true,
          "match_strategy": "CASE_INSENSITIVE"
        }
      ],
      "canonical": {
        "category_id": 9,
        "display_name": "GitHub",
        "id": 280,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "PLATFORM",
        "slug": "github",
        "sub_category_id": 170,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "dimensions": [
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "CI/CD Pipeline Platforms",
            "id": 150,
            "rationale": "Systems used to define, run, and maintain automated build and deployment workflows. This cluster is coherent because the role owns delivery automation end to end, including pipeline reliability and promotion logic.",
            "slug": "ci-cd-pipeline-platforms",
            "source": "db"
          },
          "input_skill": "GitHub",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "DevOps Engineer",
              "id": 10,
              "rationale": null,
              "role_archetype": null,
              "slug": "devops-engineer",
              "source": "db"
            }
          ]
        },
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "CI/CD for Machine Learning",
            "id": 56,
            "rationale": "Tools and platforms for automating ML model integration, testing, and deployment pipelines.",
            "slug": "ci-cd-for-machine-learning",
            "source": "db"
          },
          "input_skill": "GitHub",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "ML Engineer",
              "id": 3,
              "rationale": null,
              "role_archetype": null,
              "slug": "ml-engineer",
              "source": "db"
            }
          ]
        }
      ],
      "input_skill": "GitHub",
      "matched_via": "alias",
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": null,
      "source_tag": "db",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "Flyway",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Data Engineering Tools",
          "skill_nature": "TOOL",
          "sub_category": "general",
          "typical_lifespan": "MULTI_YEAR",
          "version_strategy": "UNVERSIONED",
          "volatility": "MEDIUM"
        },
        "enrichment": null,
        "keep_log": [],
        "locked_dimensions": [],
        "merge_log": [],
        "placed": null,
        "relationships": null,
        "skill_id": "flyway",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "Liquibase",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Data Engineering Tools",
          "skill_nature": "TOOL",
          "sub_category": "general",
          "typical_lifespan": "MULTI_YEAR",
          "version_strategy": "UNVERSIONED",
          "volatility": "MEDIUM"
        },
        "enrichment": null,
        "keep_log": [],
        "locked_dimensions": [],
        "merge_log": [],
        "placed": null,
        "relationships": null,
        "skill_id": "liquibase",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    }
  ],
  "unmatched_skills": [
    "HDFS",
    "Kudu",
    "StreamSets",
    "HVR",
    "Apache NiFi",
    "AWS Database Migration Service",
    "Azure Data Factory",
    "Google Cloud Dataproc",
    "AWS Managed Workflows for Apache Airflow",
    "Bitbucket",
    "Flyway",
    "Liquibase"
  ]
}
API 3 — final-role-output
{
  "chosen_role": {
    "display_name": "Cloud Architect",
    "id": 9,
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        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "AI Engineer",
            "id": 13,
            "rationale": null,
            "role_archetype": null,
            "slug": "ai-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 188,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 9,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Cloud Provider Platforms",
          "id": 131,
          "rationale": "Major cloud platforms and their core service ecosystems used to design target-state architectures, choose deployment boundaries, and evaluate managed capabilities. This is the primary substrate for cloud architecture decisions.",
          "slug": "cloud-provider-platforms",
          "source": "db"
        },
        "dimension_id": 131,
        "input_skill": "Azure",
        "llm_role": null,
        "matched_chosen_role": true,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension saved",
        "role_dimension_saved": true,
        "roles_from_db": [
          {
            "display_name": "Cloud Architect",
            "id": 9,
            "rationale": null,
            "role_archetype": null,
            "slug": "cloud-architect",
            "source": "db"
          },
          {
            "display_name": "Cloud Security Engineer",
            "id": 23,
            "rationale": null,
            "role_archetype": null,
            "slug": "cloud-security-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 188,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 9,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Cloud Security Posture Tools",
          "id": 64,
          "rationale": "Cloud-native security platforms used to assess misconfiguration, workload exposure, and cloud control coverage. This dimension includes the major CNAPP/CSPM/CWPP vendors and cloud security services the role reviews and tunes.",
          "slug": "cloud-security-posture-tools",
          "source": "db"
        },
        "dimension_id": 64,
        "input_skill": "Azure",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "Cloud Security Engineer",
            "id": 23,
            "rationale": null,
            "role_archetype": null,
            "slug": "cloud-security-engineer",
            "source": "db"
          },
          {
            "display_name": "Cyber Security Engineer",
            "id": 5,
            "rationale": null,
            "role_archetype": null,
            "slug": "cybersecurity-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 188,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 9,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Vendor Product Families",
          "id": 477,
          "rationale": "Coordinate usage, licensing, and architecture decisions for major vendor software and cloud product families.",
          "slug": "vendor-product-families",
          "source": "db"
        },
        "dimension_id": 477,
        "input_skill": "Azure",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "Engineering Manager",
            "id": 121,
            "rationale": null,
            "role_archetype": null,
            "slug": "engineering-manager",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 188,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 9,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Cloud \u0026 Hosting Providers",
          "id": 414,
          "rationale": "Knowledge of major cloud and hosting vendor platforms for deploying and managing PHP applications.",
          "slug": "cloud-hosting-providers",
          "source": "db"
        },
        "dimension_id": 414,
        "input_skill": "Google Cloud Platform",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "PHP Backend Developer",
            "id": 86,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "php-backend-developer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 425,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 9,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Cloud Provider Platforms",
          "id": 131,
          "rationale": "Major cloud platforms and their core service ecosystems used to design target-state architectures, choose deployment boundaries, and evaluate managed capabilities. This is the primary substrate for cloud architecture decisions.",
          "slug": "cloud-provider-platforms",
          "source": "db"
        },
        "dimension_id": 131,
        "input_skill": "Google Cloud Platform",
        "llm_role": null,
        "matched_chosen_role": true,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension saved",
        "role_dimension_saved": true,
        "roles_from_db": [
          {
            "display_name": "Cloud Architect",
            "id": 9,
            "rationale": null,
            "role_archetype": null,
            "slug": "cloud-architect",
            "source": "db"
          },
          {
            "display_name": "Cloud Security Engineer",
            "id": 23,
            "rationale": null,
            "role_archetype": null,
            "slug": "cloud-security-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 425,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 9,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Cloud Security Posture Tools",
          "id": 64,
          "rationale": "Cloud-native security platforms used to assess misconfiguration, workload exposure, and cloud control coverage. This dimension includes the major CNAPP/CSPM/CWPP vendors and cloud security services the role reviews and tunes.",
          "slug": "cloud-security-posture-tools",
          "source": "db"
        },
        "dimension_id": 64,
        "input_skill": "Google Cloud Platform",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "Cloud Security Engineer",
            "id": 23,
            "rationale": null,
            "role_archetype": null,
            "slug": "cloud-security-engineer",
            "source": "db"
          },
          {
            "display_name": "Cyber Security Engineer",
            "id": 5,
            "rationale": null,
            "role_archetype": null,
            "slug": "cybersecurity-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 425,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 9,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Vendor Product Families",
          "id": 477,
          "rationale": "Coordinate usage, licensing, and architecture decisions for major vendor software and cloud product families.",
          "slug": "vendor-product-families",
          "source": "db"
        },
        "dimension_id": 477,
        "input_skill": "Google Cloud Platform",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "Engineering Manager",
            "id": 121,
            "rationale": null,
            "role_archetype": null,
            "slug": "engineering-manager",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 425,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 9,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "ETL and ELT Tooling",
          "id": 24,
          "rationale": "Packaged tools for extracting, loading, and transforming data across systems. This dimension covers connector-based ingestion, transformation frameworks, and managed integration products.",
          "slug": "etl-and-elt-tooling",
          "source": "db"
        },
        "dimension_id": 24,
        "input_skill": "Hadoop",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "Data Engineer",
            "id": 2,
            "rationale": null,
            "role_archetype": null,
            "slug": "data-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 1351,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 9,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "React Frontend Development",
          "id": 96,
          "rationale": "Building interactive web user interfaces with React.js, including component composition, state management, hooks, and rendering patterns. React.js belongs here because it is a core library for client-side UI development in modern web applications.",
          "slug": "d_init_01",
          "source": "db"
        },
        "dimension_id": 96,
        "input_skill": "Databricks",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [],
        "skill_dimension_saved": true,
        "skill_id": 1202,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 9,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Cloud Storage and Data Services",
          "id": 144,
          "rationale": "Cloud-native storage and managed data services used to place workloads, choose durability tiers, and define platform boundaries. This is a coherent cluster because architects evaluate storage fit, access patterns, and managed service tradeoffs.",
          "slug": "cloud-storage-and-data-services",
          "source": "db"
        },
        "dimension_id": 144,
        "input_skill": "Amazon S3",
        "llm_role": null,
        "matched_chosen_role": true,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension saved",
        "role_dimension_saved": true,
        "roles_from_db": [
          {
            "display_name": "Cloud Architect",
            "id": 9,
            "rationale": null,
            "role_archetype": null,
            "slug": "cloud-architect",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 170,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 9,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Cloud Storage and File Formats",
          "id": 35,
          "rationale": "Object storage and data file formats used as the physical substrate for data movement and lake-style analytics. Data engineers need these to manage landing zones, partitioned datasets, and efficient interchange.",
          "slug": "cloud-storage-and-file-formats",
          "source": "db"
        },
        "dimension_id": 35,
        "input_skill": "Amazon S3",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "Data Engineer",
            "id": 2,
            "rationale": null,
            "role_archetype": null,
            "slug": "data-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 170,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 9,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Cloud Storage and Data Services",
          "id": 144,
          "rationale": "Cloud-native storage and managed data services used to place workloads, choose durability tiers, and define platform boundaries. This is a coherent cluster because architects evaluate storage fit, access patterns, and managed service tradeoffs.",
          "slug": "cloud-storage-and-data-services",
          "source": "db"
        },
        "dimension_id": 144,
        "input_skill": "Azure Data Lake Storage",
        "llm_role": null,
        "matched_chosen_role": true,
        "outcome_line": "Skipped \u2014 no persistable v3 meta for new skill",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "Cloud Architect",
            "id": 9,
            "rationale": null,
            "role_archetype": null,
            "slug": "cloud-architect",
            "source": "db"
          }
        ],
        "skill_dimension_saved": false,
        "skill_id": null,
        "skill_tag": "new",
        "skipped_reason": "skill_not_in_db_v3_proposed"
      },
      {
        "chosen_role_id": 9,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Cloud Storage and File Formats",
          "id": 35,
          "rationale": "Object storage and data file formats used as the physical substrate for data movement and lake-style analytics. Data engineers need these to manage landing zones, partitioned datasets, and efficient interchange.",
          "slug": "cloud-storage-and-file-formats",
          "source": "db"
        },
        "dimension_id": 35,
        "input_skill": "Azure Data Lake Storage",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Skipped \u2014 no persistable v3 meta for new skill",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "Data Engineer",
            "id": 2,
            "rationale": null,
            "role_archetype": null,
            "slug": "data-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": false,
        "skill_id": null,
        "skill_tag": "new",
        "skipped_reason": "skill_not_in_db_v3_proposed"
      },
      {
        "chosen_role_id": 9,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Cloud Storage and Data Services",
          "id": 144,
          "rationale": "Cloud-native storage and managed data services used to place workloads, choose durability tiers, and define platform boundaries. This is a coherent cluster because architects evaluate storage fit, access patterns, and managed service tradeoffs.",
          "slug": "cloud-storage-and-data-services",
          "source": "db"
        },
        "dimension_id": 144,
        "input_skill": "Google Cloud Storage",
        "llm_role": null,
        "matched_chosen_role": true,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension saved",
        "role_dimension_saved": true,
        "roles_from_db": [
          {
            "display_name": "Cloud Architect",
            "id": 9,
            "rationale": null,
            "role_archetype": null,
            "slug": "cloud-architect",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 171,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 9,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Cloud Storage and File Formats",
          "id": 35,
          "rationale": "Object storage and data file formats used as the physical substrate for data movement and lake-style analytics. Data engineers need these to manage landing zones, partitioned datasets, and efficient interchange.",
          "slug": "cloud-storage-and-file-formats",
          "source": "db"
        },
        "dimension_id": 35,
        "input_skill": "Google Cloud Storage",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "Data Engineer",
            "id": 2,
            "rationale": null,
            "role_archetype": null,
            "slug": "data-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 171,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 9,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Magento Search and Indexing",
          "id": 403,
          "rationale": "Index-driven storefront data freshness and search behavior. This cluster is coherent because Magento commerce sites depend on indexing to keep catalog, price, and search results aligned with changing data.",
          "slug": "magento-search-and-indexing",
          "source": "db"
        },
        "dimension_id": 403,
        "input_skill": "Elasticsearch",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "Magento Dev",
            "id": 231,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "magento-dev",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 3171,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 9,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Search and Content Discovery",
          "id": 356,
          "rationale": "Implementing site search, indexing, and content discovery features in Drupal. This cluster is coherent because many Drupal sites need structured search experiences beyond basic navigation.",
          "slug": "search-and-content-discovery",
          "source": "db"
        },
        "dimension_id": 356,
        "input_skill": "Elasticsearch",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "Drupal Dev",
            "id": 228,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "drupal-dev",
            "source": "db"
          },
          {
            "display_name": "Sitecore Dev",
            "id": 233,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "sitecore-dev",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 3171,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 9,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Search and Content Discovery",
          "id": 356,
          "rationale": "Implementing site search, indexing, and content discovery features in Drupal. This cluster is coherent because many Drupal sites need structured search experiences beyond basic navigation.",
          "slug": "search-and-content-discovery",
          "source": "db"
        },
        "dimension_id": 356,
        "input_skill": "Solr",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "Drupal Dev",
            "id": 228,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "drupal-dev",
            "source": "db"
          },
          {
            "display_name": "Sitecore Dev",
            "id": 233,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "sitecore-dev",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 3170,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 9,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Cloud Storage and Data Services",
          "id": 144,
          "rationale": "Cloud-native storage and managed data services used to place workloads, choose durability tiers, and define platform boundaries. This is a coherent cluster because architects evaluate storage fit, access patterns, and managed service tradeoffs.",
          "slug": "cloud-storage-and-data-services",
          "source": "db"
        },
        "dimension_id": 144,
        "input_skill": "Cassandra",
        "llm_role": null,
        "matched_chosen_role": true,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension saved",
        "role_dimension_saved": true,
        "roles_from_db": [
          {
            "display_name": "Cloud Architect",
            "id": 9,
            "rationale": null,
            "role_archetype": null,
            "slug": "cloud-architect",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 1354,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 9,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "ETL and ELT Tooling",
          "id": 24,
          "rationale": "Packaged tools for extracting, loading, and transforming data across systems. This dimension covers connector-based ingestion, transformation frameworks, and managed integration products.",
          "slug": "etl-and-elt-tooling",
          "source": "db"
        },
        "dimension_id": 24,
        "input_skill": "Apache Spark",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "Data Engineer",
            "id": 2,
            "rationale": null,
            "role_archetype": null,
            "slug": "data-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 1350,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 9,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Messaging and Event Streaming",
          "id": 8,
          "rationale": "Transport-layer systems used to move events and decouple producers from consumers. Data engineers use these systems to ingest, buffer, and distribute event data before downstream processing.",
          "slug": "messaging-and-event-streaming",
          "source": "db"
        },
        "dimension_id": 8,
        "input_skill": "Apache Kafka",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
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          },
          {
            "display_name": "Data Engineer",
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            "rationale": null,
            "role_archetype": null,
            "slug": "data-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 145,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 9,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "ETL and ELT Tooling",
          "id": 24,
          "rationale": "Packaged tools for extracting, loading, and transforming data across systems. This dimension covers connector-based ingestion, transformation frameworks, and managed integration products.",
          "slug": "etl-and-elt-tooling",
          "source": "db"
        },
        "dimension_id": 24,
        "input_skill": "Matillion",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "Data Engineer",
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            "rationale": null,
            "role_archetype": null,
            "slug": "data-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 118,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 9,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "ETL and ELT Tooling",
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          "rationale": "Packaged tools for extracting, loading, and transforming data across systems. This dimension covers connector-based ingestion, transformation frameworks, and managed integration products.",
          "slug": "etl-and-elt-tooling",
          "source": "db"
        },
        "dimension_id": 24,
        "input_skill": "Fivetran",
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        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "Data Engineer",
            "id": 2,
            "rationale": null,
            "role_archetype": null,
            "slug": "data-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 116,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 9,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Data Pipeline Orchestration",
          "id": 23,
          "rationale": "Workflow engines that schedule, coordinate, and recover batch data jobs. This cluster covers dependency management, retries, backfills, sensors, and operational control of pipeline DAGs.",
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          "source": "db"
        },
        "dimension_id": 23,
        "input_skill": "Apache Airflow",
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        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "Data Engineer",
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            "rationale": null,
            "role_archetype": null,
            "slug": "data-engineer",
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          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 110,
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        "skipped_reason": null
      },
      {
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          "source": "db"
        },
        "dimension_id": 23,
        "input_skill": "Luigi",
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        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "Data Engineer",
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            "role_archetype": null,
            "slug": "data-engineer",
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        ],
        "skill_dimension_saved": true,
        "skill_id": 114,
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        "skipped_reason": null
      },
      {
        "chosen_role_id": 9,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Infrastructure \u0026 Security Automation Frameworks",
          "id": 249,
          "rationale": "Frameworks and libraries for provisioning, configuring, and automating cloud security infrastructure.",
          "slug": "infrastructure-security-automation-frameworks",
          "source": "db"
        },
        "dimension_id": 249,
        "input_skill": "Terraform",
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        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "Cloud Security Engineer",
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            "rationale": null,
            "role_archetype": null,
            "slug": "cloud-security-engineer",
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        ],
        "skill_dimension_saved": true,
        "skill_id": 286,
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        "skipped_reason": null
      },
      {
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        "dimension": {
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          "display_name": "Infrastructure as Code",
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        },
        "dimension_id": 132,
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        "role_dimension_saved": true,
        "roles_from_db": [
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            "slug": "cloud-architect",
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          },
          {
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          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 286,
        "skill_tag": "in_db",
        "skipped_reason": null
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      {
        "chosen_role_id": 9,
        "dimension": {
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        },
        "dimension_id": 57,
        "input_skill": "Terraform",
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        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
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        ],
        "skill_dimension_saved": true,
        "skill_id": 286,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 9,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Infrastructure as Code",
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          "source": "db"
        },
        "dimension_id": 132,
        "input_skill": "CloudFormation",
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        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension saved",
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        "roles_from_db": [
          {
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          {
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          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 837,
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        "skipped_reason": null
      },
      {
        "chosen_role_id": 9,
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          "source": "db"
        },
        "dimension_id": 248,
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        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "Cloud Security Engineer",
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        ],
        "skill_dimension_saved": true,
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        "skipped_reason": null
      },
      {
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          "source": "db"
        },
        "dimension_id": 1,
        "input_skill": "Python",
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        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
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        "roles_from_db": [
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          },
          {
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            "rationale": null,
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          },
          {
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            "role_archetype": null,
            "slug": "full-stack-engineer",
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        ],
        "skill_dimension_saved": true,
        "skill_id": 5,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
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          "source": "db"
        },
        "dimension_id": 475,
        "input_skill": "Python",
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        "roles_from_db": [
          {
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        ],
        "skill_dimension_saved": true,
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        "dimension_id": 59,
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        ],
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      {
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          "rationale": "Languages used to implement data pipelines, transformations, and operational glue. This is the primary coding surface for building ingestion, enrichment, and automation logic in data engineering.",
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          "source": "db"
        },
        "dimension_id": 21,
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        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "Data Engineer",
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        "skill_dimension_saved": true,
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        "skipped_reason": null
      },
      {
        "chosen_role_id": 9,
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          "display_name": "Programming Languages for ML Systems",
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          "rationale": "Languages used to build training code, inference services, evaluation jobs, and ML glue code. This is the primary implementation surface for ML engineers across experimentation and productionization.",
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          "source": "db"
        },
        "dimension_id": 39,
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        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "ML Engineer",
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            "slug": "ml-engineer",
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          },
          {
            "display_name": "MLOps Engineer",
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            "role_archetype": null,
            "slug": "ml-ops-engineer",
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        ],
        "skill_dimension_saved": true,
        "skill_id": 5,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
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          "difficulty_hint": "well_known",
          "display_name": "Programming Languages for XR",
          "id": 97,
          "rationale": "Primary implementation languages used to build immersive client features, interaction logic, and device-specific runtime behavior. This is the core coding surface for AR/VR experiences.",
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          "source": "db"
        },
        "dimension_id": 97,
        "input_skill": "Python",
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        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "AR/VR Engineer",
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            "rationale": null,
            "role_archetype": null,
            "slug": "ar-vr-engineer",
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        ],
        "skill_dimension_saved": true,
        "skill_id": 5,
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        "skipped_reason": null
      },
      {
        "chosen_role_id": 9,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Python Programming",
          "id": 290,
          "rationale": "Core Python language skills used to implement backend business logic, request handlers, integrations, and service internals. This is the primary coding surface for the role.",
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          "source": "db"
        },
        "dimension_id": 290,
        "input_skill": "Python",
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        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "Python Backend Developer",
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            "rationale": null,
            "role_archetype": "Engineering",
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          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 5,
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        "skipped_reason": null
      },
      {
        "chosen_role_id": 9,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "CI/CD Pipeline Platforms",
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          "rationale": "Systems used to define, run, and maintain automated build and deployment workflows. This cluster is coherent because the role owns delivery automation end to end, including pipeline reliability and promotion logic.",
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        },
        "dimension_id": 150,
        "input_skill": "CI/CD",
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        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "DevOps Engineer",
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            "role_archetype": null,
            "slug": "devops-engineer",
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        ],
        "skill_dimension_saved": true,
        "skill_id": 1190,
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        "skipped_reason": null
      },
      {
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        "roles_from_db": [
          {
            "display_name": "ML Engineer",
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        ],
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        "skill_id": 1190,
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        "skipped_reason": null
      },
      {
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        },
        "dimension_id": 150,
        "input_skill": "GitHub",
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        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "DevOps Engineer",
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            "rationale": null,
            "role_archetype": null,
            "slug": "devops-engineer",
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        ],
        "skill_dimension_saved": true,
        "skill_id": 280,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 9,
        "dimension": {
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          "display_name": "CI/CD for Machine Learning",
          "id": 56,
          "rationale": "Tools and platforms for automating ML model integration, testing, and deployment pipelines.",
          "slug": "ci-cd-for-machine-learning",
          "source": "db"
        },
        "dimension_id": 56,
        "input_skill": "GitHub",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "ML Engineer",
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            "rationale": null,
            "role_archetype": null,
            "slug": "ml-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 280,
        "skill_tag": "in_db",
        "skipped_reason": null
      }
    ],
    "new_skills_created": 0,
    "role_dimension_saved": 0,
    "skill_dimension_saved": 0,
    "skipped": 2
  },
  "planner_output": null,
  "run_id": "6dcdb737-93c5-4641-b093-a2ce8ccbbe4b"
}

LLM Calls

Every model call made for this run, in pipeline order. Click a card to see the model's response.

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