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Pipeline run

f0ab237f-4c64-4ff1-802c-0c58a67096de

Pipeline LLM cost (USD)
API 1: $0.0046 API 2: $0.0003 API 3: $0.0000 Total: $0.0049

Client output enrichment

v2 Skill cluster · Nature of work · AI index · Tech stack maturity · Evidence · KRA description
role baseline loaded sources · ai_index: jd · nature_of_work: jd · tech_stack_maturity: jd
Nature of work · Data transformation and modeling
Build and run cloud-based data platforms in Fabric/Databricks: design databases and warehouses, create ETL/ELT pipelines and lakehouse models, integrate source APIs, and partner with stakeholders to turn business needs into technical solutions.
"“Expertise in creating and managing data warehouses, including ETL and ELT processes”"
Tech stack maturity
Modern Cloud Native
The skill set centers on cloud data platforms and modern lakehouse tooling such as AWS, Azure, Databricks, Delta Lake, Unity Catalog, and Spark, which aligns with a modern cloud-native data engineering stack.
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 (31)
AWS Azure Google Cloud SQL NoSQL Microsoft Fabric Lakehouse Data Pipelines Notebooks Databricks PySpark Delta Lake Unity Catalog Workflow Orchestration Data Warehouses ETL ELT Hadoop Spark Data Lakes APIs GDPR HIPAA Agile DevOps +6
Skill cluster (8 dimension groups, role-scoped)
BI and Visualization Tools
Tableau Power BI Looker
Programming Languages for Data Work
SQL Python Java
Cloud Platforms
AWS Azure
Compliance and Security Frameworks
GDPR HIPAA
ETL and ELT Tooling
Hadoop Spark
CI/CD Pipeline Platforms
DevOps
Data Lineage and Metadata
Unity Catalog
Cross-cutting / unaligned
Google Cloud NoSQL Microsoft Fabric Lakehouse Data Pipelines Notebooks Databricks PySpark Delta Lake Workflow Orchestration Data Warehouses ETL ELT Data Lakes APIs Agile R
Show KRA description ↓
• Proficiency in using cloud services such as AWS, Azure, or Google Cloud for data storage and architecture. • Ability to design and manage SQL and NoSQL databases, create data models, and work with database management systems (DBMS). • Experience working with Microsoft Fabric, including Lakehouses, Data Pipelines, Notebooks, and semantic models, with a strong understanding of Fabric’s end‑to‑end data architecture. • Hands-on expertise in Databricks for building scalable data pipelines using PySpark, Delta Lake, Unity Catalog, and workflow orchestration. • Expertise in creating and managing data warehouses, including ETL and ELT processes to move data efficiently. • Knowledge of big data frameworks like Hadoop and Spark, and experience with data lakes, Lakehouse and pipelines for handling large volumes of data. • Exposure of BI tools such as Tableau, Power BI, or Looker to visualize and interpret data. • Knowledge of integrating with source systems using APIs to enable seamless data exchange and interoperability • Understanding of data encryption, masking, and compliance with standards like GDPR and HIPAA to ensure data protection. • Ability to understand the business requirements and translate them into functional and technical requirements • Strong analytical and problem-solving skills as well as excellent oral and written communication skills. • Work closely with various stakeholders to understand their data needs and deliver solutions that meet their requirement • Experience with Agile and DevOps best practices. • 5+ years of experience in data engineering, or a related field • Familiarity with programming languages like Python, R, or Java for data manipulation and analysis. • Experience in managing data-related projects and familiarity with Agile methodologies to ensure timely delivery. • Willingness to stay updated with the latest trends and technologies in data architecture to continuously improve skills.

Signals

Skill data-engineer
0.27
Alias data-engineer
1.00
KRA data-engineer
0.63

Post-classification

Centroidupdated · n=486
Alias collision log
New-role queue
New skills captured9
New KRA captured

Captured for admin review

Google Cloud primary Data Engineer pending
Microsoft Fabric primary Data Engineer pending
Data Pipelines primary Data Engineer pending
Notebooks primary Data Engineer pending
PySpark primary Data Engineer pending
Workflow Orchestration primary Data Engineer pending
Data Warehouses primary Data Engineer pending
ETL primary Data Engineer pending
ELT primary Data Engineer pending
Status: completed Created: 2026-05-27T16:57:00.728266Z Updated: 2026-05-27T16:58:45.637536Z API 3 duration: 44453 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

Data Engineer

CASE A

slug: data-engineer · id: 2 · source: db

Exact alias hit on data-engineer (1.0) — no other alias at this confidence; skill_top data-engineer 0.27 does not contradict

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
5
Skipped

Job description

At Jacobs, we're challenging today to reinvent tomorrow by solving the world's most critical problems for thriving cities, resilient environments, mission-critical outcomes, operational advancement, scientific discovery and cutting-edge manufacturing, turning abstract ideas into realities that transform the world for good.

Your impact

• Proficiency in using cloud services such as AWS, Azure, or Google Cloud for data storage and architecture.
• Ability to design and manage SQL and NoSQL databases, create data models, and work with database management systems (DBMS).
• Experience working with Microsoft Fabric, including Lakehouses, Data Pipelines, Notebooks, and semantic models, with a strong understanding of Fabric’s end‑to‑end data architecture.
• Hands-on expertise in Databricks for building scalable data pipelines using PySpark, Delta Lake, Unity Catalog, and workflow orchestration.
• Expertise in creating and managing data warehouses, including ETL and ELT processes to move data efficiently.
• Knowledge of big data frameworks like Hadoop and Spark, and experience with data lakes, Lakehouse and pipelines for handling large volumes of data.
• Exposure of BI tools such as Tableau, Power BI, or Looker to visualize and interpret data.
• Knowledge of integrating with source systems using APIs to enable seamless data exchange and interoperability
• Understanding of data encryption, masking, and compliance with standards like GDPR and HIPAA to ensure data protection.
• Ability to understand the business requirements and translate them into functional and technical requirements
• Strong analytical and problem-solving skills as well as excellent oral and written communication skills.
• Work closely with various stakeholders to understand their data needs and deliver solutions that meet their requirement
• Experience with Agile and DevOps best practices.



Here's what you'll need

• 5+ years of experience in data engineering, or a related field
• Familiarity with programming languages like Python, R, or Java for data manipulation and analysis.
• Experience in managing data-related projects and familiarity with Agile methodologies to ensure timely delivery.
• Willingness to stay updated with the latest trends and technologies in data architecture to continuously improve skills. Graduate degree educated in computer science or a relevant subject



At Jacobs we value people. Having the right balance of belonging, career and lifestyle enables us to consistently deliver and exceed clients’ expectations.

Working alongside industry leaders, you will have the opportunity to develop your career working on key projects in an environment which encourages collaboration, knowledge sharing and innovation. To support your professional growth, Jacobs flexible working arrangements, extended leave options and a host of social, health and wellbeing events and initiatives will underpin our commitment to you.

At Jacobs it’s all about what you do, not where you are, which counts!

We value collaboration and believe that in-person interactions are crucial for both our culture and client delivery. We empower employees with our hybrid working policy, allowing them to split their work week between Jacobs offices/projects and remote locations enabling them to deliver their best work.

Your application experience is important to us, and we’re keen to adapt to make every interaction even better. If you require further support or reasonable adjustments with regards to the recruitment process (for example, you require the application form in a different format), please contact the team via Careers Support.

Skills from this JD

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

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 saved
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 skipped (dimension not under chosen role)
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 saved
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 skipped (dimension not under chosen role)
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 Primary 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
Skipped — no persistable v3 meta for new skill
skill_not_in_db_v3_proposed
Cloud Provider Platforms
cloud-provider-platforms
Skipped — no persistable v3 meta for new skill
skill_not_in_db_v3_proposed
Cloud Security Posture Tools
cloud-security-posture-tools
Skipped — no persistable v3 meta for new skill
skill_not_in_db_v3_proposed
Vendor Product Families
vendor-product-families
Skipped — no persistable v3 meta for new skill
skill_not_in_db_v3_proposed
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 saved
NoSQL Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: NoSQL id=1346 · nosql

Aliases — catalog

  • NoSQL (CANONICAL)

Context tags (catalog)

CAP theorem Cassandra DynamoDB MongoDB Redis column-family data modeling document store eventual consistency graph database horizontal scaling key-value store query language schema-less sharding

Stored enrichment (catalog DB)

Category
Concept
Sub-category
Database Paradigm
Confidence
0.93
Version strategy
NOT_APPLICABLE

Maturity reasoning: NoSQL is broadly listed in job descriptions across backend/data roles, with MongoDB, DynamoDB, and Cassandra appearing as common market signals; it remains a hiring-pipeline staple rather than a niche or sunset tech.

Skill profile (library / DB)

Skill nature
CONCEPT
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
2
Sub-category id
1019
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • NoSQL Databases Catalog dimension db id 19

    Library dimension (catalog)

    Roles linked in library: Backend Developer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
NoSQL Databases
nosql-databases
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Microsoft Fabric Primary 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
Lakehouse Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Lakehouse id=1359 · lakehouse

Aliases — catalog

  • Lakehouse (CANONICAL)

Context tags (catalog)

Apache Spark Delta Lake ETL SQL analytics cloud storage data governance data integration data lake data modeling data pipeline data warehouse metadata management real-time processing streaming analytics

Stored enrichment (catalog DB)

Category
Architecture
Sub-category
Data Platform Architecture
Confidence
0.90
Version strategy
NOT_APPLICABLE

Maturity reasoning: Lakehouse is increasingly listed in data-platform JDs and vendor docs (Databricks, Snowflake, Microsoft Fabric), but it is not yet as universal as core warehouse or lake skills.

Skill profile (library / DB)

Skill nature
PATTERN
Volatility
EMERGING
Typical lifespan
EVERGREEN
Category id
1
Sub-category id
1026
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)
Data Pipelines Primary 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
CONCEPT
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Notebooks Primary 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
Databricks Primary 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)
PySpark Primary 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
Skipped — no persistable v3 meta for new skill
skill_not_in_db_v3_proposed
Delta Lake Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Delta Lake id=237 · delta-lake

Aliases — catalog

  • Delta Lake (CANONICAL) primary

Context tags (catalog)

ACID transactions Apache Spark CDC Databricks Delta Engine ETL Lakehouse architecture MERGE INTO OPTIMIZE Parquet Unity Catalog Z-Order batch processing cloud storage data governance data lake data lakehouse data pipeline data reliability partition pruning schema enforcement schema evolution streaming data streaming ingestion time travel

Stored enrichment (catalog DB)

Category
Tool
Sub-category
Table Format Tool
Vendor
Databricks
License
apache_2
Year introduced
2017
Confidence
0.72
Version strategy
NOT_APPLICABLE

Maturity reasoning: Delta Lake appears frequently in data engineering JDs and cloud vendor docs, especially alongside Databricks/Spark for lakehouse stacks; it’s a common hiring-pipeline skill rather than a niche tool.

Skill profile (library / DB)

Skill nature
TOOL
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
13
Sub-category id
1170
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Model and Data Versioning Catalog dimension db id 48

    Library dimension (catalog)

    Roles linked in library: ML Engineer, MLOps Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Model and Data Versioning
model-and-data-versioning
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Unity Catalog Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Unity Catalog id=1203 · unity-catalog

Aliases — catalog

  • Unity Catalog (CANONICAL)

Context tags (catalog)

access control audit logs collaboration compliance data catalog data classification data discovery data governance data lineage data privacy data quality data sharing metadata management role-based access schema enforcement

Stored enrichment (catalog DB)

Category
Platform
Sub-category
Data Governance Platform
Vendor
Databricks
License
unknown
Year introduced
2021
Confidence
0.90
Version strategy
NOT_APPLICABLE

Maturity reasoning: Appears increasingly in Databricks and data-governance job postings, but JD volume is still far below core platforms like AWS or PostgreSQL; adoption is growing with Unity Catalog positioned as Databricks’ unified governance layer.

Skill profile (library / DB)

Skill nature
PLATFORM
Volatility
EMERGING
Typical lifespan
EVERGREEN
Category id
9
Sub-category id
250
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Data Lineage and Metadata Catalog dimension db id 28

    Library dimension (catalog)

    Roles linked in library: Data Engineer

  • React Frontend Development Catalog dimension db id 96

    Library dimension (catalog)

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Data Lineage and Metadata
data-lineage-and-metadata
Existing dimension (library) · Role↔dimension saved
React Frontend Development
d_init_01
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Workflow Orchestration Primary 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
CONCEPT
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Data Warehouses Primary 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
CONCEPT
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
ETL Primary 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
PRACTICE
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
ELT Primary 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
PRACTICE
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Hadoop Primary 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 saved
Spark Primary 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 saved
Data Lakes Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Data Lakes id=1358 · data-lakes

Aliases — catalog

  • Data Lakes (CANONICAL)

Context tags (catalog)

AWS Lake Formation Azure Data Lake ETL big data data catalog data governance data ingestion data lakes vs data warehouses data modeling data pipelines data warehousing partitioning real-time analytics schema evolution serverless architecture

Stored enrichment (catalog DB)

Category
Architecture
Sub-category
Data Lake Architecture
Confidence
0.90
Version strategy
NOT_APPLICABLE

Maturity reasoning: Data lakes are widely listed in cloud/data platform job descriptions and are a standard architecture in AWS, Azure, and GCP ecosystems; they’re a common hiring-pipeline staple rather than a niche pattern.

Skill profile (library / DB)

Skill nature
PATTERN
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
1
Sub-category id
1025
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

  • React Frontend Development Catalog dimension db id 96

    Library dimension (catalog)

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 skipped (dimension not under chosen role)
React Frontend Development
d_init_01
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Tableau Secondary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Tableau id=150 · tableau

Aliases — catalog

  • Tableau (CANONICAL) primary

Context tags (catalog)

LOD expressions Tableau Cloud Tableau Desktop Tableau Prep Tableau Server actions calculated fields dashboards data blending data visualization extracts filters parameters published data sources workbooks

Stored enrichment (catalog DB)

Category
Platform
Sub-category
Bi Analytics Platform
Vendor
Tableau Software
License
proprietary
Year introduced
2003
Confidence
0.96
Version strategy
NOT_APPLICABLE

Maturity reasoning: Tableau appears frequently in BI/data analyst job descriptions and remains a standard enterprise analytics platform with strong vendor support and broad adoption.

Skill profile (library / DB)

Skill nature
PLATFORM
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
9
Sub-category id
111
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • BI and Visualization Tools Catalog dimension db id 31

    Library dimension (catalog)

    Roles linked in library: Data Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
BI and Visualization Tools
bi-and-visualization-tools
Existing dimension (library) · Role↔dimension saved
Power BI Secondary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Power BI id=151 · power-bi

Aliases — catalog

  • Power BI (CANONICAL) primary

Context tags (catalog)

Azure Synapse DAX DirectQuery Import mode M language Power Query RLS SQL Server SSAS dashboard data modeling data warehouse gateway reporting star schema

Stored enrichment (catalog DB)

Category
Platform
Sub-category
Bi Analytics Platform
Vendor
Microsoft
License
proprietary
Year introduced
2015
Confidence
0.96
Version strategy
NOT_APPLICABLE

Maturity reasoning: Power BI appears frequently in BI/data analyst job descriptions and is a standard Microsoft analytics platform in enterprise stacks, with strong vendor support and broad adoption.

Skill profile (library / DB)

Skill nature
PLATFORM
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
9
Sub-category id
111
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • BI and Visualization Tools Catalog dimension db id 31

    Library dimension (catalog)

    Roles linked in library: Data Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
BI and Visualization Tools
bi-and-visualization-tools
Existing dimension (library) · Role↔dimension saved
Looker Secondary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Looker id=152 · looker

Aliases — catalog

  • Looker (CANONICAL) primary

Context tags (catalog)

BigQuery Dimensions Explores LookML Measures PDT SQL Runner Snowflake dashboards data modeling derived table drill-down embedded analytics scheduled delivery tiles

Stored enrichment (catalog DB)

Category
Platform
Sub-category
Bi Analytics Platform
Vendor
Google Cloud
License
proprietary
Year introduced
2012
Confidence
0.95
Version strategy
NOT_APPLICABLE

Maturity reasoning: Looker appears frequently in BI/analytics job descriptions and is a standard enterprise analytics platform, especially after Google Cloud’s acquisition expanded market visibility.

Skill profile (library / DB)

Skill nature
PLATFORM
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
9
Sub-category id
111
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • BI and Visualization Tools Catalog dimension db id 31

    Library dimension (catalog)

    Roles linked in library: Data Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
BI and Visualization Tools
bi-and-visualization-tools
Existing dimension (library) · Role↔dimension saved
APIs Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: APIs id=1192 · apis

Aliases — catalog

  • APIs (CANONICAL)

Context tags (catalog)

API Gateway Endpoint GraphQL JSON JWT Microservices OAuth Postman REST Rate Limiting SOAP Swagger Throttling Webhooks XML

Stored enrichment (catalog DB)

Category
Protocol
Sub-category
Application Programming Interfaces
Confidence
0.93
Version strategy
NOT_APPLICABLE

Maturity reasoning: APIs are a hiring-pipeline staple across backend, mobile, and platform JDs; REST/GraphQL/API design appears in large volumes of job postings and vendor docs, indicating broad adoption.

Skill profile (library / DB)

Skill nature
PROTOCOL
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
10
Sub-category id
902
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)
GDPR Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: GDPR id=402 · gdpr

Aliases — catalog

  • GDPR (CANONICAL) primary

Context tags (catalog)

DPA DPIA DPO RoPA SCCs accountability audit trail compliance consent consent management controller cross-border transfers data breach data breach notification data minimization data portability data privacy data processing data processing agreement data protection data protection officer data subject data subject rights encryption lawful basis personal data privacy by design privacy impact assessment privacy policy processor records of processing regulatory framework right to access right to erasure third-party vendors

Stored enrichment (catalog DB)

Category
Standard
Sub-category
Privacy Regulation Standard
Vendor
European Union
Year introduced
2016
Confidence
0.99
Version strategy
NOT_APPLICABLE

Maturity reasoning: GDPR is a widely cited compliance requirement in job postings for product, legal, security, and data roles across EU-facing companies; it remains an active regulatory standard rather than a niche tool.

Skill profile (library / DB)

Skill nature
STANDARD
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
12
Sub-category id
3215
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Compliance and Security Frameworks Catalog dimension db id 73

    Library dimension (catalog)

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

  • Standards, Protocols & Compliance Catalog dimension db id 452

    Library dimension (catalog)

    Roles linked in library: Engineering Manager, Sitecore Dev

  • Web Standards & Compliance Catalog dimension db id 343

    Library dimension (catalog)

    Roles linked in library: WordPress Dev

  • Web Standards, Protocols & Compliance Catalog dimension db id 436

    Library dimension (catalog)

    Roles linked in library: Shopify Dev

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Compliance and Security Frameworks
compliance-and-security-frameworks
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Standards, Protocols & Compliance
standards-protocols-compliance
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Web Standards & Compliance
web-standards-compliance
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Web Standards, Protocols & Compliance
web-standards-protocols-compliance
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
HIPAA Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: HIPAA id=397 · hipaa

Aliases — catalog

  • HIPAA (CANONICAL) primary

Context tags (catalog)

BAA HIPAA training HITECH PHI administrative safeguards audit controls breach notification business associate business associates compliance covered entities covered entity data encryption data sharing ePHI electronic health information health information healthcare regulations medical records minimum necessary patient confidentiality patient consent physical safeguards privacy rule regulatory framework risk assessment security rule technical safeguards training requirements

Stored enrichment (catalog DB)

Category
Standard
Sub-category
Healthcare Privacy Standard
Year introduced
1996
Confidence
0.99
Version strategy
NOT_APPLICABLE

Maturity reasoning: HIPAA is a baseline compliance requirement in U.S. healthcare JDs and vendor security questionnaires; it remains widely referenced rather than replaced by a successor standard.

Skill profile (library / DB)

Skill nature
STANDARD
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
12
Sub-category id
274
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Compliance and Security Frameworks Catalog dimension db id 73

    Library dimension (catalog)

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

  • Standards, Protocols & Compliance Catalog dimension db id 452

    Library dimension (catalog)

    Roles linked in library: Engineering Manager, Sitecore Dev

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Compliance and Security Frameworks
compliance-and-security-frameworks
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Standards, Protocols & Compliance
standards-protocols-compliance
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Agile Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Agile id=520 · agile

Aliases — catalog

  • Agile (CANONICAL) primary

Context tags (catalog)

Kanban SAFe Scrum backlog backlog grooming burndown burndown chart continuous delivery continuous improvement cross-functional daily standup epics incremental development iteration iteration planning lean product backlog product owner retrospective sprint sprint planning stand-up story points user stories velocity

Stored enrichment (catalog DB)

Category
Methodology
Sub-category
Agile
Confidence
0.99
Version strategy
NOT_APPLICABLE

Maturity reasoning: Agile appears in a large share of software job descriptions and is a standard hiring-pipeline requirement; Scrum/Kanban are commonly listed alongside it, showing broad market adoption.

Skill profile (library / DB)

Skill nature
METHODOLOGY
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
8
Sub-category id
3594
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • React Frontend Development Catalog dimension db id 96

    Library dimension (catalog)

  • Software Concepts, Patterns & Practices Catalog dimension db id 478

    Library dimension (catalog)

    Roles linked in library: Engineering Manager

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)
Software Concepts, Patterns & Practices
software-concepts-patterns-practices
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
DevOps Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: DevOps id=1216 · devops

Aliases — catalog

  • DevOps (CANONICAL)

Context tags (catalog)

Agile Ansible Automation CI/CD Cloud-native Continuous Deployment Continuous Integration Docker GitOps Infrastructure as Code Jenkins Kubernetes Microservices Monitoring SRE Terraform

Stored enrichment (catalog DB)

Category
Methodology
Sub-category
Devops Methodology
Confidence
0.97
Version strategy
NOT_APPLICABLE

Maturity reasoning: DevOps appears in a large share of software and platform engineering job descriptions, often alongside CI/CD, Kubernetes, and cloud tooling; it is a standard hiring-pipeline keyword rather than a niche specialty.

Skill profile (library / DB)

Skill nature
METHODOLOGY
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
8
Sub-category id
922
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

  • Deployment and Release Patterns Catalog dimension db id 140

    Library dimension (catalog)

    Roles linked in library: Cloud Architect

  • 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
CI/CD Pipeline Platforms
ci-cd-pipeline-platforms
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Deployment and Release Patterns
deployment-and-release-patterns
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Infrastructure as Code
infrastructure-as-code
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Python Primary 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 saved
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)
R Secondary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: R id=194 · r

Aliases — catalog

  • R (VERSION)
  • R 3 (VERSION)
  • R 3.x (VERSION)
  • R 4 (VERSION)
  • R 4.0 (VERSION)
  • R 4.1 (VERSION)
  • R 4.2 (VERSION)
  • R 4.3 (VERSION)
  • R 4.4 (VERSION)
  • R 4.x (VERSION)

Context tags (catalog)

Bioconductor CRAN R Markdown Shiny Tidyverse caret data.table dplyr ggplot2 glm lme4 lubridate rstan tidyr tidyverse

Stored enrichment (catalog DB)

Category
Language
Sub-category
Programming Language
Vendor
R Core Team
License
gpl_v2
Year introduced
1993
Confidence
0.99
Version strategy
SEPARATE_ENTITY
Version tag
R 4.x

Maturity reasoning: R appears in many data science, statistics, and analytics job postings, and CRAN remains active with broad package usage across academia and industry.

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)

  • Programming Languages for ML Systems Catalog dimension db id 39

    Library dimension (catalog)

    Roles linked in library: ML Engineer, MLOps Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Programming Languages for ML Systems
programming-languages-for-ml-systems
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Java Secondary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Java id=1 · java

Aliases — catalog

  • Java (CANONICAL) primary
  • JDK (VERSION)
  • JDK 10 (VERSION)
  • JDK 11 (VERSION)
  • JDK 12 (VERSION)
  • JDK 13 (VERSION)
  • JDK 14 (VERSION)
  • JDK 15 (VERSION)
  • JDK 16 (VERSION)
  • JDK 17 (VERSION)
  • JDK 18 (VERSION)
  • JDK 19 (VERSION)
  • JDK 20 (VERSION)
  • JDK 21 (VERSION)
  • JDK 5 (VERSION)
  • JDK 6 (VERSION)
  • JDK 7 (VERSION)
  • JDK 8 (VERSION)
  • JDK 9 (VERSION)
  • Java 1.0 (VERSION)
  • Java 1.1 (VERSION)
  • Java 1.2 (VERSION)
  • Java 1.3 (VERSION)
  • Java 1.4 (VERSION)
  • Java 1.5 (VERSION)
  • Java 1.6 (VERSION)
  • Java 1.7 (VERSION)
  • Java 1.8 (VERSION)
  • Java 10 (VERSION)
  • Java 11 (VERSION)
  • Java 12 (VERSION)
  • Java 13 (VERSION)
  • Java 14 (VERSION)
  • Java 15 (VERSION)
  • Java 16 (VERSION)
  • Java 17 (VERSION)
  • Java 18 (VERSION)
  • Java 19 (VERSION)
  • Java 20 (VERSION)
  • Java 21 (VERSION)
  • Java 5 (VERSION)
  • Java 6 (VERSION)
  • Java 7 (VERSION)
  • Java 8 (VERSION)
  • Java 9 (VERSION)
  • Java11 (VERSION)
  • Java17 (VERSION)
  • Java21 (VERSION)
  • Java8 (VERSION)
  • OpenJDK 11 (VERSION)
  • OpenJDK 17 (VERSION)
  • OpenJDK 21 (VERSION)
  • OpenJDK 8 (VERSION)
  • java 11 (VERSION)
  • java 17 (VERSION)
  • java 21 (VERSION)
  • java 4 (VERSION)
  • java 5 (VERSION)
  • java 6 (VERSION)
  • java 7 (VERSION)
  • java 8 (VERSION)
  • java lts (VERSION)
  • java-11 (VERSION)
  • java-17 (VERSION)
  • java-21 (VERSION)
  • java-4 (VERSION)
  • java-5 (VERSION)
  • java-6 (VERSION)
  • java-7 (VERSION)
  • java-8 (VERSION)
  • java11 (VERSION)
  • java17 (VERSION)
  • java21 (VERSION)
  • java4 (VERSION)
  • java5 (VERSION)
  • java6 (VERSION)
  • java7 (VERSION)
  • java8 (VERSION)
  • jdk 11 (VERSION)
  • jdk 17 (VERSION)
  • jdk 21 (VERSION)
  • jdk 4 (VERSION)
  • jdk 5 (VERSION)
  • jdk 6 (VERSION)
  • jdk 7 (VERSION)
  • jdk 8 (VERSION)
  • jdk11 (VERSION)
  • jdk17 (VERSION)
  • jdk21 (VERSION)
  • jdk4 (VERSION)
  • jdk5 (VERSION)
  • jdk6 (VERSION)
  • jdk7 (VERSION)
  • jdk8 (VERSION)
  • jvm21 (VERSION)

Context tags (catalog)

APIs Apache Tomcat Concurrency Design patterns Garbage collection GraalVM Gradle Hibernate JDBC JDK JPA JUnit JVM Java 8 Java EE JavaFX Kafka Lambda expressions Maven Microservices Mockito Object-oriented REST RESTful SOAP Servlets Spring Spring Boot Tomcat microservices

Stored enrichment (catalog DB)

Category
Language
Sub-category
Programming Language
Vendor
Oracle
License
other_open
Year introduced
1995
Confidence
0.99
Version strategy
SEPARATE_ENTITY
Version tag
21

Maturity reasoning: Java is a hiring-pipeline staple with very high JD volume across enterprise backend, Android, and cloud roles; it remains widely supported by major vendors and frameworks like Spring.

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)

  • Java Language and JVM Catalog dimension db id 279

    Library dimension (catalog)

    Roles linked in library: Java Backend Developer, Kotlin Backend Developer, Scala Backend Developer

  • Kotlin and Java Catalog dimension db id 161

    Library dimension (catalog)

    Roles linked in library: Android Developer

  • Native Mobile Languages Catalog dimension db id 274

    Library dimension (catalog)

    Roles linked in library: Native Mobile Developer

  • Pega Programming Languages & DSLs Catalog dimension db id 267

    Library dimension (catalog)

    Roles linked in library: Pega Developer

  • 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 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
Java Language and JVM
java-language-and-jvm
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Kotlin and Java
kotlin-and-java
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Native Mobile Languages
native-mobile-languages
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Pega Programming Languages & DSLs
pega-programming-languages-dsls
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 for Data Work
programming-languages-for-data-work
Existing dimension (library) · Role↔dimension saved

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
AWS in_db
Cloud Platforms
cloud-platforms
Existing dimension (library) · Role↔dimension saved
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 skipped (dimension not under chosen role)
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 saved
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 skipped (dimension not under chosen role)
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 new
Cloud & Hosting Providers
cloud-hosting-providers
Skipped — no persistable v3 meta for new skill skill_not_in_db_v3_proposed
Google Cloud new
Cloud Provider Platforms
cloud-provider-platforms
Skipped — no persistable v3 meta for new skill skill_not_in_db_v3_proposed
Google Cloud new
Cloud Security Posture Tools
cloud-security-posture-tools
Skipped — no persistable v3 meta for new skill skill_not_in_db_v3_proposed
Google Cloud new
Vendor Product Families
vendor-product-families
Skipped — no persistable v3 meta for new skill skill_not_in_db_v3_proposed
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 saved
NoSQL in_db
NoSQL Databases
nosql-databases
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Lakehouse in_db
React Frontend Development
d_init_01
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)
PySpark new
ETL and ELT Tooling
etl-and-elt-tooling
Skipped — no persistable v3 meta for new skill skill_not_in_db_v3_proposed
Delta Lake in_db
Model and Data Versioning
model-and-data-versioning
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Unity Catalog in_db
Data Lineage and Metadata
data-lineage-and-metadata
Existing dimension (library) · Role↔dimension saved
Unity Catalog in_db
React Frontend Development
d_init_01
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 saved
Spark in_db
ETL and ELT Tooling
etl-and-elt-tooling
Existing dimension (library) · Role↔dimension saved
Data Lakes in_db
Cloud Storage and Data Services
cloud-storage-and-data-services
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Data Lakes in_db
React Frontend Development
d_init_01
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Tableau in_db
BI and Visualization Tools
bi-and-visualization-tools
Existing dimension (library) · Role↔dimension saved
Power BI in_db
BI and Visualization Tools
bi-and-visualization-tools
Existing dimension (library) · Role↔dimension saved
Looker in_db
BI and Visualization Tools
bi-and-visualization-tools
Existing dimension (library) · Role↔dimension saved
APIs in_db
React Frontend Development
d_init_01
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
GDPR in_db
Compliance and Security Frameworks
compliance-and-security-frameworks
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
GDPR in_db
Standards, Protocols & Compliance
standards-protocols-compliance
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
GDPR in_db
Web Standards & Compliance
web-standards-compliance
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
GDPR in_db
Web Standards, Protocols & Compliance
web-standards-protocols-compliance
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
HIPAA in_db
Compliance and Security Frameworks
compliance-and-security-frameworks
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
HIPAA in_db
Standards, Protocols & Compliance
standards-protocols-compliance
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Agile in_db
React Frontend Development
d_init_01
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Agile in_db
Software Concepts, Patterns & Practices
software-concepts-patterns-practices
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
DevOps in_db
CI/CD Pipeline Platforms
ci-cd-pipeline-platforms
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
DevOps in_db
Deployment and Release Patterns
deployment-and-release-patterns
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
DevOps in_db
Infrastructure as Code
infrastructure-as-code
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
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 saved
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)
R in_db
Programming Languages for ML Systems
programming-languages-for-ml-systems
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Java in_db
Java Language and JVM
java-language-and-jvm
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Java in_db
Kotlin and Java
kotlin-and-java
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Java in_db
Native Mobile Languages
native-mobile-languages
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Java in_db
Pega Programming Languages & DSLs
pega-programming-languages-dsls
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Java in_db
Programming Languages
programming-languages
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Java in_db
Programming Languages & DSLs
programming-languages-dsls
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Java in_db
Programming Languages for Data Work
programming-languages-for-data-work
Existing dimension (library) · Role↔dimension saved

Library artifacts (this run)

Kind Detail DB id
canonical_skill_proposed Microsoft Fabric | type=Cloud Platforms subtype=general nature=PLATFORM lifespan=MULTI_YEAR
canonical_skill_proposed Data Pipelines | type=Data Engineering Tools subtype=general nature=CONCEPT lifespan=MULTI_YEAR
canonical_skill_proposed Notebooks | type=Data Engineering Tools subtype=general nature=TOOL lifespan=MULTI_YEAR
canonical_skill_proposed Workflow Orchestration | type=Data Engineering Tools subtype=general nature=CONCEPT lifespan=MULTI_YEAR
canonical_skill_proposed Data Warehouses | type=Databases subtype=general nature=CONCEPT lifespan=MULTI_YEAR
canonical_skill_proposed ETL | type=Data Engineering Tools subtype=general nature=PRACTICE lifespan=MULTI_YEAR
canonical_skill_proposed ELT | type=Data Engineering Tools subtype=general nature=PRACTICE lifespan=MULTI_YEAR
dimension_skill_link_proposed Google Cloud ↔ Cloud & Hosting Providers
dimension_skill_link_proposed Google Cloud ↔ Cloud Provider Platforms
dimension_skill_link_proposed Google Cloud ↔ Cloud Security Posture Tools
dimension_skill_link_proposed Google Cloud ↔ Vendor Product Families
dimension_skill_link_proposed PySpark ↔ ETL and ELT Tooling
role_dimension_link_proposed Data Engineer ↔ ETL and ELT Tooling
nano JD Parser — gpt-4.1-nano click to toggle
RoleData Engineer
CompanyJacobs
Experience5+ years of experience in data engineering, or a related field
DomainIT Services & Consulting
JD type pass
Show raw JSON
{
  "JD_type": "pass",
  "about_company": {
    "source_marker": {
      "first_5_words": "At\u202fJacobs we value people.",
      "last_5_words": "not where you are, which counts!"
    },
    "text": "At\u202fJacobs we value people. Having the right balance of belonging, career and lifestyle enables us to consistently deliver and exceed clients\u2019 expectations.\nWorking alongside industry leaders, you will have the opportunity to develop your career working on key projects in an environment which encourages collaboration, knowledge sharing and innovation. To support your professional growth, Jacobs flexible working arrangements, extended leave options and a host of social, health and wellbeing events and initiatives will underpin our commitment to you.\nAt Jacobs it\u2019s all about what you do, not where you are, which counts!",
    "word_count": 84
  },
  "certifications": [],
  "company_name": "Jacobs",
  "ctc": null,
  "domain": {
    "primary": {
      "aliases": [
        "ITES",
        "BPO",
        "Tech Consulting"
      ],
      "domain": "IT Services \u0026 Consulting"
    },
    "secondary": null
  },
  "education": [
    {
      "level": "Master\u0027s",
      "qualification": "Graduate Degree - Computer Science (or relevant)",
      "raw": "Graduate degree educated in computer science or a relevant subject",
      "requirement": "required"
    }
  ],
  "experience": {
    "max": null,
    "min": 5,
    "raw": "5+ years of experience in data engineering, or a related field"
  },
  "job_locations": [],
  "role": "Data Engineer",
  "role_aliases": [
    "Data Engineer",
    "Data Developer",
    "Data Architect"
  ],
  "role_archetype": "Data",
  "roles_and_responsibilities": [
    {
      "bullet_count": 13,
      "heading": "Your impact",
      "heading_was_present": true,
      "source_marker": {
        "first_5_words": "Proficiency in using cloud services",
        "last_5_words": "Agile and DevOps best practices."
      },
      "text": "\u2022 Proficiency in using cloud services such as AWS, Azure, or Google Cloud for data storage and architecture.\n\u2022 Ability to design and manage SQL and NoSQL databases, create data models, and work with database management systems (DBMS).\n\u2022 Experience working with Microsoft Fabric, including Lakehouses, Data Pipelines, Notebooks, and semantic models, with a strong understanding of Fabric\u2019s end\u2011to\u2011end data architecture.\n\u2022 Hands-on expertise in Databricks for building scalable data pipelines using PySpark, Delta Lake, Unity Catalog, and workflow orchestration.\n\u2022 Expertise in creating and managing data warehouses, including ETL and ELT processes to move data efficiently.\n\u2022 Knowledge of big data frameworks like Hadoop and Spark, and experience with data lakes, Lakehouse and pipelines for handling large volumes of data.\n\u2022 Exposure of BI tools such as Tableau, Power BI, or Looker to visualize and interpret data.\n\u2022 Knowledge of integrating with source systems using APIs to enable seamless data exchange and interoperability\n\u2022 Understanding of data encryption, masking, and compliance with standards like GDPR and HIPAA to ensure data protection.\n\u2022 Ability to understand the business requirements and translate them into functional and technical requirements\n\u2022 Strong analytical and problem-solving skills as well as excellent oral and written communication skills.\n\u2022 Work closely with various stakeholders to understand their data needs and deliver solutions that meet their requirement\n\u2022 Experience with Agile and DevOps best practices.",
      "word_count": 233
    },
    {
      "bullet_count": 4,
      "heading": "Here\u0027s what you\u0027ll need",
      "heading_was_present": true,
      "source_marker": {
        "first_5_words": "5+ years of experience in",
        "last_5_words": "to continuously improve skills."
      },
      "text": "\u2022 5+ years of experience in data engineering, or a related field\n\u2022 Familiarity with programming languages like Python, R, or Java for data manipulation and analysis.\n\u2022 Experience in managing data-related projects and familiarity with Agile methodologies to ensure timely delivery.\n\u2022 Willingness to stay updated with the latest trends and technologies in data architecture to continuously improve skills.",
      "word_count": 56
    }
  ],
  "urls": []
}
API 1 — extract-from-jd click to toggle
{
  "final_skills": [
    {
      "is_primary": true,
      "skill_name": "AWS"
    },
    {
      "is_primary": true,
      "skill_name": "Azure"
    },
    {
      "is_primary": true,
      "skill_name": "Google Cloud"
    },
    {
      "is_primary": true,
      "skill_name": "SQL"
    },
    {
      "is_primary": true,
      "skill_name": "NoSQL"
    },
    {
      "is_primary": true,
      "skill_name": "Microsoft Fabric"
    },
    {
      "is_primary": true,
      "skill_name": "Lakehouse"
    },
    {
      "is_primary": true,
      "skill_name": "Data Pipelines"
    },
    {
      "is_primary": true,
      "skill_name": "Notebooks"
    },
    {
      "is_primary": true,
      "skill_name": "Databricks"
    },
    {
      "is_primary": true,
      "skill_name": "PySpark"
    },
    {
      "is_primary": true,
      "skill_name": "Delta Lake"
    },
    {
      "is_primary": true,
      "skill_name": "Unity Catalog"
    },
    {
      "is_primary": true,
      "skill_name": "Workflow Orchestration"
    },
    {
      "is_primary": true,
      "skill_name": "Data Warehouses"
    },
    {
      "is_primary": true,
      "skill_name": "ETL"
    },
    {
      "is_primary": true,
      "skill_name": "ELT"
    },
    {
      "is_primary": true,
      "skill_name": "Hadoop"
    },
    {
      "is_primary": true,
      "skill_name": "Spark"
    },
    {
      "is_primary": true,
      "skill_name": "Data Lakes"
    },
    {
      "is_primary": false,
      "skill_name": "Tableau"
    },
    {
      "is_primary": false,
      "skill_name": "Power BI"
    },
    {
      "is_primary": false,
      "skill_name": "Looker"
    },
    {
      "is_primary": true,
      "skill_name": "APIs"
    },
    {
      "is_primary": true,
      "skill_name": "GDPR"
    },
    {
      "is_primary": true,
      "skill_name": "HIPAA"
    },
    {
      "is_primary": true,
      "skill_name": "Agile"
    },
    {
      "is_primary": true,
      "skill_name": "DevOps"
    },
    {
      "is_primary": true,
      "skill_name": "Python"
    },
    {
      "is_primary": false,
      "skill_name": "R"
    },
    {
      "is_primary": false,
      "skill_name": "Java"
    }
  ],
  "jd_role": {
    "display_name": "Data Engineer",
    "rationale": null,
    "role_aliases": [
      "Data Engineer",
      "Data Developer",
      "Data Architect"
    ],
    "role_archetype": "Data",
    "slug": ""
  },
  "nano_parsed": {
    "JD_type": "pass",
    "about_company": {
      "source_marker": {
        "first_5_words": "At\u202fJacobs we value people.",
        "last_5_words": "not where you are, which counts!"
      },
      "text": "At\u202fJacobs we value people. Having the right balance of belonging, career and lifestyle enables us to consistently deliver and exceed clients\u2019 expectations.\nWorking alongside industry leaders, you will have the opportunity to develop your career working on key projects in an environment which encourages collaboration, knowledge sharing and innovation. To support your professional growth, Jacobs flexible working arrangements, extended leave options and a host of social, health and wellbeing events and initiatives will underpin our commitment to you.\nAt Jacobs it\u2019s all about what you do, not where you are, which counts!",
      "word_count": 84
    },
    "certifications": [],
    "company_name": "Jacobs",
    "ctc": null,
    "domain": {
      "primary": {
        "aliases": [
          "ITES",
          "BPO",
          "Tech Consulting"
        ],
        "domain": "IT Services \u0026 Consulting"
      },
      "secondary": null
    },
    "education": [
      {
        "level": "Master\u0027s",
        "qualification": "Graduate Degree - Computer Science (or relevant)",
        "raw": "Graduate degree educated in computer science or a relevant subject",
        "requirement": "required"
      }
    ],
    "experience": {
      "max": null,
      "min": 5,
      "raw": "5+ years of experience in data engineering, or a related field"
    },
    "job_locations": [],
    "role": "Data Engineer",
    "role_aliases": [
      "Data Engineer",
      "Data Developer",
      "Data Architect"
    ],
    "role_archetype": "Data",
    "roles_and_responsibilities": [
      {
        "bullet_count": 13,
        "heading": "Your impact",
        "heading_was_present": true,
        "source_marker": {
          "first_5_words": "Proficiency in using cloud services",
          "last_5_words": "Agile and DevOps best practices."
        },
        "text": "\u2022 Proficiency in using cloud services such as AWS, Azure, or Google Cloud for data storage and architecture.\n\u2022 Ability to design and manage SQL and NoSQL databases, create data models, and work with database management systems (DBMS).\n\u2022 Experience working with Microsoft Fabric, including Lakehouses, Data Pipelines, Notebooks, and semantic models, with a strong understanding of Fabric\u2019s end\u2011to\u2011end data architecture.\n\u2022 Hands-on expertise in Databricks for building scalable data pipelines using PySpark, Delta Lake, Unity Catalog, and workflow orchestration.\n\u2022 Expertise in creating and managing data warehouses, including ETL and ELT processes to move data efficiently.\n\u2022 Knowledge of big data frameworks like Hadoop and Spark, and experience with data lakes, Lakehouse and pipelines for handling large volumes of data.\n\u2022 Exposure of BI tools such as Tableau, Power BI, or Looker to visualize and interpret data.\n\u2022 Knowledge of integrating with source systems using APIs to enable seamless data exchange and interoperability\n\u2022 Understanding of data encryption, masking, and compliance with standards like GDPR and HIPAA to ensure data protection.\n\u2022 Ability to understand the business requirements and translate them into functional and technical requirements\n\u2022 Strong analytical and problem-solving skills as well as excellent oral and written communication skills.\n\u2022 Work closely with various stakeholders to understand their data needs and deliver solutions that meet their requirement\n\u2022 Experience with Agile and DevOps best practices.",
        "word_count": 233
      },
      {
        "bullet_count": 4,
        "heading": "Here\u0027s what you\u0027ll need",
        "heading_was_present": true,
        "source_marker": {
          "first_5_words": "5+ years of experience in",
          "last_5_words": "to continuously improve skills."
        },
        "text": "\u2022 5+ years of experience in data engineering, or a related field\n\u2022 Familiarity with programming languages like Python, R, or Java for data manipulation and analysis.\n\u2022 Experience in managing data-related projects and familiarity with Agile methodologies to ensure timely delivery.\n\u2022 Willingness to stay updated with the latest trends and technologies in data architecture to continuously improve skills.",
        "word_count": 56
      }
    ],
    "urls": []
  },
  "rejected": false,
  "rejection_reason": null,
  "run_id": "f0ab237f-4c64-4ff1-802c-0c58a67096de",
  "stage3_signals": {
    "alias_found": true,
    "alias_match_roles": [
      {
        "display_name": "Data Engineer",
        "kra_matches": null,
        "matched_count": null,
        "matched_skills": null,
        "role_id": 2,
        "score": 1.0,
        "slug": "data-engineer",
        "total_count": null
      }
    ],
    "kra_match_roles": [
      {
        "display_name": "Data Engineer",
        "kra_matches": [
          {
            "kra_text": "Works with data analysts, data scientists, and business stakeholders to define data models, ingestion schedules, and data delivery requirements.",
            "sentence": "Work closely with various stakeholders to understand their data needs and deliver solutions that meet their requirement",
            "similarity": 0.6663
          },
          {
            "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": "Hands-on expertise in Databricks for building scalable data pipelines using PySpark, Delta Lake, Unity Catalog, and workflow orchestration.",
            "similarity": 0.6167
          },
          {
            "kra_text": "Designs dimensional models, star schemas, data vault structures, and curated data mart tables to support BI tools and self-service analytics consumption.",
            "sentence": "Ability to design and manage SQL and NoSQL databases, create data models, and work with database management systems (DBMS).",
            "similarity": 0.5951
          }
        ],
        "matched_count": null,
        "matched_skills": null,
        "role_id": 2,
        "score": 0.626,
        "slug": "data-engineer",
        "total_count": null
      },
      {
        "display_name": "Fullstack Developer",
        "kra_matches": [
          {
            "kra_text": "Designs and queries relational databases like PostgreSQL and document stores like MongoDB, writing migrations, indexes, and optimized queries.",
            "sentence": "Ability to design and manage SQL and NoSQL databases, create data models, and work with database management systems (DBMS).",
            "similarity": 0.6149
          },
          {
            "kra_text": "Works closely with product managers and UX designers to translate requirements and wireframes into working software features through iterative development.",
            "sentence": "Ability to understand the business requirements and translate them into functional and technical requirements",
            "similarity": 0.5061
          },
          {
            "kra_text": "Works closely with product managers and UX designers to translate requirements and wireframes into working software features through iterative development.",
            "sentence": "Work closely with various stakeholders to understand their data needs and deliver solutions that meet their requirement",
            "similarity": 0.4858
          }
        ],
        "matched_count": null,
        "matched_skills": null,
        "role_id": 15,
        "score": 0.5356,
        "slug": "full-stack-engineer",
        "total_count": null
      },
      {
        "display_name": "ML Engineer",
        "kra_matches": [
          {
            "kra_text": "Translates product requirements into machine learning system specifications including feature definitions, model architecture choices, and success metric definitions.",
            "sentence": "Ability to understand the business requirements and translate them into functional and technical requirements",
            "similarity": 0.534
          },
          {
            "kra_text": "Prepares, cleans, and transforms training datasets, manages feature stores, and builds feature engineering pipelines for model training.",
            "sentence": "Hands-on expertise in Databricks for building scalable data pipelines using PySpark, Delta Lake, Unity Catalog, and workflow orchestration.",
            "similarity": 0.4861
          },
          {
            "kra_text": "Prepares, cleans, and transforms training datasets, manages feature stores, and builds feature engineering pipelines for model training.",
            "sentence": "Experience working with Microsoft Fabric, including Lakehouses, Data Pipelines, Notebooks, and semantic models, with a strong understanding of Fabric\u2019s end\u2011to\u2011end data architecture.",
            "similarity": 0.4203
          }
        ],
        "matched_count": null,
        "matched_skills": null,
        "role_id": 3,
        "score": 0.4801,
        "slug": "ml-engineer",
        "total_count": null
      },
      {
        "display_name": "Flutter Developer",
        "kra_matches": [
          {
            "kra_text": "translate product and design requirements",
            "sentence": "Ability to understand the business requirements and translate them into functional and technical requirements",
            "similarity": 0.5417
          },
          {
            "kra_text": "collaborate with design, product, and backend teams",
            "sentence": "Work closely with various stakeholders to understand their data needs and deliver solutions that meet their requirement",
            "similarity": 0.4815
          },
          {
            "kra_text": "collaborate with design, product, and backend teams",
            "sentence": "Experience working with Microsoft Fabric, including Lakehouses, Data Pipelines, Notebooks, and semantic models, with a strong understanding of Fabric\u2019s end\u2011to\u2011end data architecture.",
            "similarity": 0.3814
          }
        ],
        "matched_count": null,
        "matched_skills": null,
        "role_id": 74,
        "score": 0.4682,
        "slug": "flutter-developer",
        "total_count": null
      },
      {
        "display_name": "MLOps Engineer",
        "kra_matches": [
          {
            "kra_text": "Automates ML platform operations including scheduled retraining triggers, pipeline orchestration, evaluation workflows, and alerting configuration.",
            "sentence": "Hands-on expertise in Databricks for building scalable data pipelines using PySpark, Delta Lake, Unity Catalog, and workflow orchestration.",
            "similarity": 0.4504
          },
          {
            "kra_text": "Coordinates model promotion workflows across development, staging, and production environments including integration testing and data contract validation.",
            "sentence": "Work closely with various stakeholders to understand their data needs and deliver solutions that meet their requirement",
            "similarity": 0.4448
          },
          {
            "kra_text": "Orchestrates model serving deployments to production using Kubernetes, MLflow Model Registry, SageMaker, or Kubeflow Serving infrastructure.",
            "sentence": "Experience working with Microsoft Fabric, including Lakehouses, Data Pipelines, Notebooks, and semantic models, with a strong understanding of Fabric\u2019s end\u2011to\u2011end data architecture.",
            "similarity": 0.4295
          }
        ],
        "matched_count": null,
        "matched_skills": null,
        "role_id": 16,
        "score": 0.4416,
        "slug": "ml-ops-engineer",
        "total_count": null
      }
    ],
    "skill_match_roles": [
      {
        "display_name": "Data Engineer",
        "kra_matches": null,
        "matched_count": 7,
        "matched_skills": [
          "AWS",
          "Apache Spark",
          "Azure",
          "Hadoop",
          "Python",
          "SQL",
          "Unity Catalog"
        ],
        "role_id": 2,
        "score": 0.2692,
        "slug": "data-engineer",
        "total_count": 26
      },
      {
        "display_name": "Engineering Manager",
        "kra_matches": null,
        "matched_count": 7,
        "matched_skills": [
          "AWS",
          "Agile",
          "Azure",
          "GDPR",
          "HIPAA",
          "Python",
          "SQL"
        ],
        "role_id": 121,
        "score": 0.2692,
        "slug": "engineering-manager",
        "total_count": 26
      },
      {
        "display_name": "Cloud Security Engineer",
        "kra_matches": null,
        "matched_count": 5,
        "matched_skills": [
          "AWS",
          "Azure",
          "GDPR",
          "HIPAA",
          "Python"
        ],
        "role_id": 23,
        "score": 0.1923,
        "slug": "cloud-security-engineer",
        "total_count": 26
      },
      {
        "display_name": "Cyber Security Engineer",
        "kra_matches": null,
        "matched_count": 5,
        "matched_skills": [
          "AWS",
          "Azure",
          "GDPR",
          "HIPAA",
          "Python"
        ],
        "role_id": 5,
        "score": 0.1923,
        "slug": "cybersecurity-engineer",
        "total_count": 26
      },
      {
        "display_name": "ML Engineer",
        "kra_matches": null,
        "matched_count": 4,
        "matched_skills": [
          "AWS",
          "Azure",
          "Delta Lake",
          "Python"
        ],
        "role_id": 3,
        "score": 0.1538,
        "slug": "ml-engineer",
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      }
    ]
  },
  "stage4_decision": {
    "alias_collision_detected": false,
    "case": "A",
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      "display_name": "Data Engineer",
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      "matched_count": null,
      "matched_skills": null,
      "role_id": 2,
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      "slug": "data-engineer",
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    },
    "confidence": 1.0,
    "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": "Exact alias hit on data-engineer (1.0) \u2014 no other alias at this confidence; skill_top data-engineer 0.27 does not contradict",
    "sub_role": null
  },
  "stage5_updates": {
    "centroid_n_after": 486,
    "centroid_updated": true,
    "collision_log_id": null,
    "new_kra_attached": null,
    "new_skills_attached": [
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        "is_primary": true,
        "queue_id": 22743,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "Google Cloud",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 22744,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "Microsoft Fabric",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 22745,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "Data Pipelines",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 22746,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "Notebooks",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 22747,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "PySpark",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 22748,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "Workflow Orchestration",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 22749,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "Data Warehouses",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 22750,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "ETL",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 22751,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "ELT",
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    ],
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    "v3_pipeline_triggered": false,
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    "v3_run_id": null
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}
API 2 — extract-details
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        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "PLATFORM",
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        "typical_lifespan": "EVERGREEN",
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      "matched_via": "alias"
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    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
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      "existing_alias_text": "Azure",
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      "matched_canonical": {
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        "display_name": "Azure",
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        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "PLATFORM",
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        "sub_category_id": 46,
        "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": 741,
      "existing_alias_text": "Google Cloud Platform",
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        "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": "embedding_alias"
    },
    {
      "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": {
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        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "LANGUAGE",
        "slug": "sql",
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        "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": 1989,
      "existing_alias_text": "NoSQL",
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        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "CONCEPT",
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        "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": 2018,
      "existing_alias_text": "Lakehouse",
      "input_term": "Lakehouse",
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        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "PATTERN",
        "slug": "lakehouse",
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        "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": 1838,
      "existing_alias_text": "Databricks",
      "input_term": "Databricks",
      "matched_canonical": {
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        "display_name": "Databricks",
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        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "PLATFORM",
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        "sub_category_id": 911,
        "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": 2004,
      "existing_alias_text": "Apache Spark",
      "input_term": "PySpark",
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        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "FRAMEWORK",
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        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
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      "matched_via": "embedding_alias"
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    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 498,
      "existing_alias_text": "Delta Lake",
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        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "TOOL",
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        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
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      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 1839,
      "existing_alias_text": "Unity Catalog",
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        "category_id": 9,
        "display_name": "Unity Catalog",
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        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "PLATFORM",
        "slug": "unity-catalog",
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        "typical_lifespan": "EVERGREEN",
        "volatility": "EMERGING"
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      "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",
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        "is_also_category": false,
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      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 2510,
      "existing_alias_text": "spark",
      "input_term": "Spark",
      "matched_canonical": {
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        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "FRAMEWORK",
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        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
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      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 2017,
      "existing_alias_text": "Data Lakes",
      "input_term": "Data Lakes",
      "matched_canonical": {
        "category_id": 1,
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        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "PATTERN",
        "slug": "data-lakes",
        "sub_category_id": 1025,
        "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": 359,
      "existing_alias_text": "Tableau",
      "input_term": "Tableau",
      "matched_canonical": {
        "category_id": 9,
        "display_name": "Tableau",
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        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "PLATFORM",
        "slug": "tableau",
        "sub_category_id": 111,
        "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": 360,
      "existing_alias_text": "Power BI",
      "input_term": "Power BI",
      "matched_canonical": {
        "category_id": 9,
        "display_name": "Power BI",
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        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "PLATFORM",
        "slug": "power-bi",
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        "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": 361,
      "existing_alias_text": "Looker",
      "input_term": "Looker",
      "matched_canonical": {
        "category_id": 9,
        "display_name": "Looker",
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        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "PLATFORM",
        "slug": "looker",
        "sub_category_id": 111,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
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      "matched_via": "alias"
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    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 1828,
      "existing_alias_text": "APIs",
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      "matched_canonical": {
        "category_id": 10,
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        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "PROTOCOL",
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        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
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      "matched_via": "alias"
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    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 718,
      "existing_alias_text": "GDPR",
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      "matched_canonical": {
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        "display_name": "GDPR",
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        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "STANDARD",
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        "volatility": "STABLE"
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      "matched_via": "alias"
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    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 713,
      "existing_alias_text": "HIPAA",
      "input_term": "HIPAA",
      "matched_canonical": {
        "category_id": 12,
        "display_name": "HIPAA",
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        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "STANDARD",
        "slug": "hipaa",
        "sub_category_id": 274,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
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      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 868,
      "existing_alias_text": "Agile",
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      "matched_canonical": {
        "category_id": 8,
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        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "METHODOLOGY",
        "slug": "agile",
        "sub_category_id": 3594,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
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      "matched_via": "alias"
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    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 1852,
      "existing_alias_text": "DevOps",
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        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "METHODOLOGY",
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        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
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      "matched_via": "alias"
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    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
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      "existing_alias_text": "Python",
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        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "LANGUAGE",
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        "typical_lifespan": "EVERGREEN",
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      "matched_via": "alias"
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    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
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      "existing_alias_text": "R",
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        "display_name": "R",
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        "skill_nature": "LANGUAGE",
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      "matched_via": "alias"
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    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
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      "existing_alias_text": "Java",
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        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "LANGUAGE",
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        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
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      "matched_via": "alias"
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  ],
  "candidate_roles": [
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        "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": "SQL",
      "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 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": "SQL",
      "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": "NoSQL Databases",
        "id": 19,
        "rationale": "Models and manages data using non-relational database systems.",
        "slug": "nosql-databases",
        "source": "db"
      },
      "input_skill": "NoSQL",
      "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"
        }
      ]
    },
    {
      "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": "Lakehouse",
      "llm_role": null,
      "roles_from_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": "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": "PySpark",
      "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": "Model and Data Versioning",
        "id": 48,
        "rationale": "Versioning systems for datasets, features, and model artifacts at the storage layer. This enables reproducible training, rollback, lineage of artifacts, and controlled promotion of model assets.",
        "slug": "model-and-data-versioning",
        "source": "db"
      },
      "input_skill": "Delta Lake",
      "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": "Data Lineage and Metadata",
        "id": 28,
        "rationale": "Cataloging, documenting, and tracing how data moves and changes across systems. This dimension supports impact analysis, governance, discoverability, and operational understanding of datasets.",
        "slug": "data-lineage-and-metadata",
        "source": "db"
      },
      "input_skill": "Unity Catalog",
      "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": "Unity Catalog",
      "llm_role": null,
      "roles_from_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": "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": "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": "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": "Data Lakes",
      "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": "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": "Data Lakes",
      "llm_role": null,
      "roles_from_db": []
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "BI and Visualization Tools",
        "id": 31,
        "rationale": "Tools used to expose curated data to analysts and business users through dashboards, reports, and semantic exploration. Data engineers support these tools by shaping reliable datasets and performant models.",
        "slug": "bi-and-visualization-tools",
        "source": "db"
      },
      "input_skill": "Tableau",
      "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": "BI and Visualization Tools",
        "id": 31,
        "rationale": "Tools used to expose curated data to analysts and business users through dashboards, reports, and semantic exploration. Data engineers support these tools by shaping reliable datasets and performant models.",
        "slug": "bi-and-visualization-tools",
        "source": "db"
      },
      "input_skill": "Power BI",
      "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": "BI and Visualization Tools",
        "id": 31,
        "rationale": "Tools used to expose curated data to analysts and business users through dashboards, reports, and semantic exploration. Data engineers support these tools by shaping reliable datasets and performant models.",
        "slug": "bi-and-visualization-tools",
        "source": "db"
      },
      "input_skill": "Looker",
      "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": "APIs",
      "llm_role": null,
      "roles_from_db": []
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Compliance and Security Frameworks",
        "id": 73,
        "rationale": "Formal control frameworks and regulatory standards used to assess and document security posture. This dimension is coherent because the role translates technical controls into auditable requirements and evidence.",
        "slug": "compliance-and-security-frameworks",
        "source": "db"
      },
      "input_skill": "GDPR",
      "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": "Standards, Protocols \u0026 Compliance",
        "id": 452,
        "rationale": "Ensure teams adhere to industry standards, security protocols, and regulatory compliance requirements.",
        "slug": "standards-protocols-compliance",
        "source": "db"
      },
      "input_skill": "GDPR",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": "Engineering Manager",
          "id": 121,
          "rationale": null,
          "role_archetype": null,
          "slug": "engineering-manager",
          "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": "Web Standards \u0026 Compliance",
        "id": 343,
        "rationale": "Ensuring WordPress sites adhere to web markup, styling, accessibility, and privacy regulations.",
        "slug": "web-standards-compliance",
        "source": "db"
      },
      "input_skill": "GDPR",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": "WordPress Dev",
          "id": 227,
          "rationale": null,
          "role_archetype": "Engineering",
          "slug": "wordpress-dev",
          "source": "db"
        }
      ]
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Web Standards, Protocols \u0026 Compliance",
        "id": 436,
        "rationale": "Adhering to industry standards and regulatory compliance when designing and integrating storefront solutions.",
        "slug": "web-standards-protocols-compliance",
        "source": "db"
      },
      "input_skill": "GDPR",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": "Shopify Dev",
          "id": 230,
          "rationale": null,
          "role_archetype": "Engineering",
          "slug": "shopify-dev",
          "source": "db"
        }
      ]
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Compliance and Security Frameworks",
        "id": 73,
        "rationale": "Formal control frameworks and regulatory standards used to assess and document security posture. This dimension is coherent because the role translates technical controls into auditable requirements and evidence.",
        "slug": "compliance-and-security-frameworks",
        "source": "db"
      },
      "input_skill": "HIPAA",
      "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": "Standards, Protocols \u0026 Compliance",
        "id": 452,
        "rationale": "Ensure teams adhere to industry standards, security protocols, and regulatory compliance requirements.",
        "slug": "standards-protocols-compliance",
        "source": "db"
      },
      "input_skill": "HIPAA",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": "Engineering Manager",
          "id": 121,
          "rationale": null,
          "role_archetype": null,
          "slug": "engineering-manager",
          "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": "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": "Agile",
      "llm_role": null,
      "roles_from_db": []
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Software Concepts, Patterns \u0026 Practices",
        "id": 478,
        "rationale": "Champion foundational software design patterns, development methodologies, and engineering best practices.",
        "slug": "software-concepts-patterns-practices",
        "source": "db"
      },
      "input_skill": "Agile",
      "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": "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": "DevOps",
      "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": "Deployment and Release Patterns",
        "id": 140,
        "rationale": "Patterns for promoting changes safely across environments, including rollout, rollback, and release gating strategies. Cloud Architects define these patterns so teams can deploy consistently across the platform.",
        "slug": "deployment-and-release-patterns",
        "source": "db"
      },
      "input_skill": "DevOps",
      "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": "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": "DevOps",
      "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": 15,
          "rationale": null,
          "role_archetype": null,
          "slug": "full-stack-engineer",
          "source": "db"
        },
        {
          "display_name": "Fullstack Developer",
          "id": 435,
          "rationale": null,
          "role_archetype": "Engineering",
          "slug": "fullstack-developer",
          "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,
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          "id": 8,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "Java 12",
          "alias_type": "VERSION",
          "id": 2213,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "Java 13",
          "alias_type": "VERSION",
          "id": 2214,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "Java 14",
          "alias_type": "VERSION",
          "id": 2215,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "Java 15",
          "alias_type": "VERSION",
          "id": 2216,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "Java 16",
          "alias_type": "VERSION",
          "id": 2217,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "Java 17",
          "alias_type": "VERSION",
          "id": 9,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "Java 18",
          "alias_type": "VERSION",
          "id": 2219,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "Java 19",
          "alias_type": "VERSION",
          "id": 2220,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "Java 20",
          "alias_type": "VERSION",
          "id": 2221,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "Java 21",
          "alias_type": "VERSION",
          "id": 10,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "Java 5",
          "alias_type": "VERSION",
          "id": 288,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "Java 6",
          "alias_type": "VERSION",
          "id": 289,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "Java 7",
          "alias_type": "VERSION",
          "id": 290,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "Java 8",
          "alias_type": "VERSION",
          "id": 7,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "Java 9",
          "alias_type": "VERSION",
          "id": 2210,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "Java11",
          "alias_type": "VERSION",
          "id": 2976,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "Java17",
          "alias_type": "VERSION",
          "id": 2977,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "Java21",
          "alias_type": "VERSION",
          "id": 2978,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "Java8",
          "alias_type": "VERSION",
          "id": 2971,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "OpenJDK 11",
          "alias_type": "VERSION",
          "id": 21,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "OpenJDK 17",
          "alias_type": "VERSION",
          "id": 22,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "OpenJDK 21",
          "alias_type": "VERSION",
          "id": 23,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "OpenJDK 8",
          "alias_type": "VERSION",
          "id": 20,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "java 11",
          "alias_type": "VERSION",
          "id": 1512,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "java 17",
          "alias_type": "VERSION",
          "id": 1513,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "java 21",
          "alias_type": "VERSION",
          "id": 1514,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "java 4",
          "alias_type": "VERSION",
          "id": 1496,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "java 5",
          "alias_type": "VERSION",
          "id": 1497,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "java 6",
          "alias_type": "VERSION",
          "id": 1498,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "java 7",
          "alias_type": "VERSION",
          "id": 1499,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "java 8",
          "alias_type": "VERSION",
          "id": 1500,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "java lts",
          "alias_type": "VERSION",
          "id": 3122,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "java-11",
          "alias_type": "VERSION",
          "id": 1515,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "java-17",
          "alias_type": "VERSION",
          "id": 1516,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "java-21",
          "alias_type": "VERSION",
          "id": 1517,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "java-4",
          "alias_type": "VERSION",
          "id": 1501,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "java-5",
          "alias_type": "VERSION",
          "id": 1502,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "java-6",
          "alias_type": "VERSION",
          "id": 1503,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "java-7",
          "alias_type": "VERSION",
          "id": 1504,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "java-8",
          "alias_type": "VERSION",
          "id": 1505,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "java11",
          "alias_type": "VERSION",
          "id": 1506,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "java17",
          "alias_type": "VERSION",
          "id": 1507,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "java21",
          "alias_type": "VERSION",
          "id": 1508,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "java4",
          "alias_type": "VERSION",
          "id": 1482,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "java5",
          "alias_type": "VERSION",
          "id": 1483,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "java6",
          "alias_type": "VERSION",
          "id": 1484,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "java7",
          "alias_type": "VERSION",
          "id": 1485,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "java8",
          "alias_type": "VERSION",
          "id": 1486,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "jdk 11",
          "alias_type": "VERSION",
          "id": 1509,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "jdk 17",
          "alias_type": "VERSION",
          "id": 1510,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "jdk 21",
          "alias_type": "VERSION",
          "id": 1511,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "jdk 4",
          "alias_type": "VERSION",
          "id": 1487,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "jdk 5",
          "alias_type": "VERSION",
          "id": 1488,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "jdk 6",
          "alias_type": "VERSION",
          "id": 1489,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "jdk 7",
          "alias_type": "VERSION",
          "id": 1490,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "jdk 8",
          "alias_type": "VERSION",
          "id": 1491,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "jdk11",
          "alias_type": "VERSION",
          "id": 1492,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "jdk17",
          "alias_type": "VERSION",
          "id": 1493,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "jdk21",
          "alias_type": "VERSION",
          "id": 1494,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "jdk4",
          "alias_type": "VERSION",
          "id": 1477,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "jdk5",
          "alias_type": "VERSION",
          "id": 1478,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "jdk6",
          "alias_type": "VERSION",
          "id": 1479,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "jdk7",
          "alias_type": "VERSION",
          "id": 1480,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "jdk8",
          "alias_type": "VERSION",
          "id": 1481,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "jvm21",
          "alias_type": "VERSION",
          "id": 1495,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        }
      ],
      "canonical": {
        "category_id": 6,
        "display_name": "Java",
        "id": 1,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "LANGUAGE",
        "slug": "java",
        "sub_category_id": 96,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "dimensions": [
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Java Language and JVM",
            "id": 279,
            "rationale": "Core Java implementation skills used to build backend service logic, utilities, and internal abstractions. This is the primary coding surface for the role and includes language features plus JVM behavior that affect correctness and maintainability.",
            "slug": "java-language-and-jvm",
            "source": "db"
          },
          "input_skill": "Java",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "Java Backend Developer",
              "id": 79,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "java-backend-developer",
              "source": "db"
            },
            {
              "display_name": "Kotlin Backend Developer",
              "id": 84,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "kotlin-server-backend-developer",
              "source": "db"
            },
            {
              "display_name": "Scala Backend Developer",
              "id": 87,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "scala-backend-developer",
              "source": "db"
            }
          ]
        },
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Kotlin and Java",
            "id": 161,
            "rationale": "Primary implementation languages for Android app features, platform integration, and client-side business logic. Android engineers use these languages to build screens, state flows, service adapters, and device-aware behavior.",
            "slug": "kotlin-and-java",
            "source": "db"
          },
          "input_skill": "Java",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "Android Developer",
              "id": 4,
              "rationale": null,
              "role_archetype": null,
              "slug": "android-engineer",
              "source": "db"
            }
          ]
        },
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Native Mobile Languages",
            "id": 274,
            "rationale": "Primary implementation languages used to build platform-specific app features, UI logic, and device integrations. This is the core coding surface for native mobile work on one platform.",
            "slug": "native-mobile-languages",
            "source": "db"
          },
          "input_skill": "Java",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "Native Mobile Developer",
              "id": 75,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "native-mobile-developer",
              "source": "db"
            }
          ]
        },
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Pega Programming Languages \u0026 DSLs",
            "id": 267,
            "rationale": "Programming languages and domain-specific languages used in Pega development.",
            "slug": "pega-programming-languages-dsls",
            "source": "db"
          },
          "input_skill": "Java",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "Pega Developer",
              "id": 24,
              "rationale": null,
              "role_archetype": null,
              "slug": "pega-developer",
              "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": "Java",
          "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": 15,
              "rationale": null,
              "role_archetype": null,
              "slug": "full-stack-engineer",
              "source": "db"
            },
            {
              "display_name": "Fullstack Developer",
              "id": 435,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "fullstack-developer",
              "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": "Java",
          "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 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": "Java",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "Data Engineer",
              "id": 2,
              "rationale": null,
              "role_archetype": null,
              "slug": "data-engineer",
              "source": "db"
            }
          ]
        }
      ],
      "input_skill": "Java",
      "matched_via": "alias",
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": null,
      "source_tag": "db",
      "was_in_llm_skills": true
    }
  ],
  "unmatched_skills": [
    "Microsoft Fabric",
    "Data Pipelines",
    "Notebooks",
    "Workflow Orchestration",
    "Data Warehouses",
    "ETL",
    "ELT"
  ]
}
API 3 — final-role-output
{
  "chosen_role": {
    "display_name": "Data Engineer",
    "id": 2,
    "rationale": "Exact alias hit on data-engineer (1.0) \u2014 no other alias at this confidence; skill_top data-engineer 0.27 does not contradict",
    "role_archetype": null,
    "slug": "data-engineer",
    "source": "db"
  },
  "chosen_role_resolution": "in_db",
  "final_input_skills": [
    {
      "skill": "AWS",
      "tag": "in_db"
    },
    {
      "skill": "Azure",
      "tag": "in_db"
    },
    {
      "skill": "Google Cloud",
      "tag": "in_db"
    },
    {
      "skill": "SQL",
      "tag": "in_db"
    },
    {
      "skill": "NoSQL",
      "tag": "in_db"
    },
    {
      "skill": "Microsoft Fabric",
      "tag": "new"
    },
    {
      "skill": "Lakehouse",
      "tag": "in_db"
    },
    {
      "skill": "Data Pipelines",
      "tag": "new"
    },
    {
      "skill": "Notebooks",
      "tag": "new"
    },
    {
      "skill": "Databricks",
      "tag": "in_db"
    },
    {
      "skill": "PySpark",
      "tag": "in_db"
    },
    {
      "skill": "Delta Lake",
      "tag": "in_db"
    },
    {
      "skill": "Unity Catalog",
      "tag": "in_db"
    },
    {
      "skill": "Workflow Orchestration",
      "tag": "new"
    },
    {
      "skill": "Data Warehouses",
      "tag": "new"
    },
    {
      "skill": "ETL",
      "tag": "new"
    },
    {
      "skill": "ELT",
      "tag": "new"
    },
    {
      "skill": "Hadoop",
      "tag": "in_db"
    },
    {
      "skill": "Spark",
      "tag": "in_db"
    },
    {
      "skill": "Data Lakes",
      "tag": "in_db"
    },
    {
      "skill": "Tableau",
      "tag": "in_db"
    },
    {
      "skill": "Power BI",
      "tag": "in_db"
    },
    {
      "skill": "Looker",
      "tag": "in_db"
    },
    {
      "skill": "APIs",
      "tag": "in_db"
    },
    {
      "skill": "GDPR",
      "tag": "in_db"
    },
    {
      "skill": "HIPAA",
      "tag": "in_db"
    },
    {
      "skill": "Agile",
      "tag": "in_db"
    },
    {
      "skill": "DevOps",
      "tag": "in_db"
    },
    {
      "skill": "Python",
      "tag": "in_db"
    },
    {
      "skill": "R",
      "tag": "in_db"
    },
    {
      "skill": "Java",
      "tag": "in_db"
    }
  ],
  "llm_cost_api1_usd": null,
  "llm_cost_api2_usd": null,
  "llm_cost_api3_usd": null,
  "llm_cost_total_usd": null,
  "persistence": {
    "items": [
      {
        "chosen_role_id": 2,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Cloud Platforms",
          "id": 20,
          "rationale": "Underlying cloud providers that host the managed services or infrastructure used by the role, such as AWS, Azure, and GCP.",
          "slug": "cloud-platforms",
          "source": "db"
        },
        "dimension_id": 20,
        "input_skill": "AWS",
        "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": ".NET Backend Developer",
            "id": 83,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "dotnet-backend-developer",
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        "roles_from_db": [
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          "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"
        },
        "dimension_id": 475,
        "input_skill": "SQL",
        "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": 101,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 2,
        "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"
        },
        "dimension_id": 21,
        "input_skill": "SQL",
        "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": "Data Engineer",
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            "rationale": null,
            "role_archetype": null,
            "slug": "data-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 101,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 2,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "NoSQL Databases",
          "id": 19,
          "rationale": "Models and manages data using non-relational database systems.",
          "slug": "nosql-databases",
          "source": "db"
        },
        "dimension_id": 19,
        "input_skill": "NoSQL",
        "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": "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"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 1346,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 2,
        "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": "Lakehouse",
        "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": 1359,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 2,
        "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": 2,
        "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": "PySpark",
        "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": "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": 2,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Model and Data Versioning",
          "id": 48,
          "rationale": "Versioning systems for datasets, features, and model artifacts at the storage layer. This enables reproducible training, rollback, lineage of artifacts, and controlled promotion of model assets.",
          "slug": "model-and-data-versioning",
          "source": "db"
        },
        "dimension_id": 48,
        "input_skill": "Delta Lake",
        "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",
            "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"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 237,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 2,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Data Lineage and Metadata",
          "id": 28,
          "rationale": "Cataloging, documenting, and tracing how data moves and changes across systems. This dimension supports impact analysis, governance, discoverability, and operational understanding of datasets.",
          "slug": "data-lineage-and-metadata",
          "source": "db"
        },
        "dimension_id": 28,
        "input_skill": "Unity Catalog",
        "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": "Data Engineer",
            "id": 2,
            "rationale": null,
            "role_archetype": null,
            "slug": "data-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 1203,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 2,
        "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": "Unity Catalog",
        "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": 1203,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 2,
        "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": true,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension saved",
        "role_dimension_saved": true,
        "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": 2,
        "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": "Spark",
        "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": "Data Engineer",
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            "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": 2,
        "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": "Data Lakes",
        "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 Architect",
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            "rationale": null,
            "role_archetype": null,
            "slug": "cloud-architect",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 1358,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 2,
        "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": "Data Lakes",
        "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": 1358,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 2,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "BI and Visualization Tools",
          "id": 31,
          "rationale": "Tools used to expose curated data to analysts and business users through dashboards, reports, and semantic exploration. Data engineers support these tools by shaping reliable datasets and performant models.",
          "slug": "bi-and-visualization-tools",
          "source": "db"
        },
        "dimension_id": 31,
        "input_skill": "Tableau",
        "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": "Data Engineer",
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            "rationale": null,
            "role_archetype": null,
            "slug": "data-engineer",
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          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 150,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 2,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "BI and Visualization Tools",
          "id": 31,
          "rationale": "Tools used to expose curated data to analysts and business users through dashboards, reports, and semantic exploration. Data engineers support these tools by shaping reliable datasets and performant models.",
          "slug": "bi-and-visualization-tools",
          "source": "db"
        },
        "dimension_id": 31,
        "input_skill": "Power BI",
        "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": "Data Engineer",
            "id": 2,
            "rationale": null,
            "role_archetype": null,
            "slug": "data-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 151,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 2,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "BI and Visualization Tools",
          "id": 31,
          "rationale": "Tools used to expose curated data to analysts and business users through dashboards, reports, and semantic exploration. Data engineers support these tools by shaping reliable datasets and performant models.",
          "slug": "bi-and-visualization-tools",
          "source": "db"
        },
        "dimension_id": 31,
        "input_skill": "Looker",
        "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": "Data Engineer",
            "id": 2,
            "rationale": null,
            "role_archetype": null,
            "slug": "data-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 152,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 2,
        "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": "APIs",
        "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": 1192,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 2,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Compliance and Security Frameworks",
          "id": 73,
          "rationale": "Formal control frameworks and regulatory standards used to assess and document security posture. This dimension is coherent because the role translates technical controls into auditable requirements and evidence.",
          "slug": "compliance-and-security-frameworks",
          "source": "db"
        },
        "dimension_id": 73,
        "input_skill": "GDPR",
        "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": 402,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 2,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Standards, Protocols \u0026 Compliance",
          "id": 452,
          "rationale": "Ensure teams adhere to industry standards, security protocols, and regulatory compliance requirements.",
          "slug": "standards-protocols-compliance",
          "source": "db"
        },
        "dimension_id": 452,
        "input_skill": "GDPR",
        "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"
          },
          {
            "display_name": "Sitecore Dev",
            "id": 233,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "sitecore-dev",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 402,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 2,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Web Standards \u0026 Compliance",
          "id": 343,
          "rationale": "Ensuring WordPress sites adhere to web markup, styling, accessibility, and privacy regulations.",
          "slug": "web-standards-compliance",
          "source": "db"
        },
        "dimension_id": 343,
        "input_skill": "GDPR",
        "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": "WordPress Dev",
            "id": 227,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "wordpress-dev",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 402,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 2,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Web Standards, Protocols \u0026 Compliance",
          "id": 436,
          "rationale": "Adhering to industry standards and regulatory compliance when designing and integrating storefront solutions.",
          "slug": "web-standards-protocols-compliance",
          "source": "db"
        },
        "dimension_id": 436,
        "input_skill": "GDPR",
        "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": "Shopify Dev",
            "id": 230,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "shopify-dev",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 402,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 2,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Compliance and Security Frameworks",
          "id": 73,
          "rationale": "Formal control frameworks and regulatory standards used to assess and document security posture. This dimension is coherent because the role translates technical controls into auditable requirements and evidence.",
          "slug": "compliance-and-security-frameworks",
          "source": "db"
        },
        "dimension_id": 73,
        "input_skill": "HIPAA",
        "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": 397,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 2,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Standards, Protocols \u0026 Compliance",
          "id": 452,
          "rationale": "Ensure teams adhere to industry standards, security protocols, and regulatory compliance requirements.",
          "slug": "standards-protocols-compliance",
          "source": "db"
        },
        "dimension_id": 452,
        "input_skill": "HIPAA",
        "llm_role": null,
        "matched_chosen_role": false,
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        "role_dimension_saved": false,
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        "roles_from_db": [
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