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Pipeline run

857a64e4-51b1-4a9c-b147-9672375dc355

Pipeline LLM cost (USD)
API 1: $0.0045 API 2: $0.0003 API 3: $0.0000 Total: $0.0047

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 pipeline development
Build and optimize Azure ETL pipelines that ingest structured and unstructured data into data lakes/warehouses, using ADF, Databricks, Synapse, Python and SQL. Also monitor performance, automate workflows, and enforce data governance/security.
"Design, develop, and optimize scalable data pipelines and ETL workflows on Azure"
Tech stack maturity
Mainstream Modern
The stack centers on Azure cloud services with Python and SQL, which is a common contemporary data engineering setup that is modern but not bleeding-edge.
AI index (0 = no AI use, 5 = totally AI-dependent · v2.1)
0.50 / 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): Machine Learning
Evidence — skills matched in JD (21)
Azure Azure Data Factory Azure Databricks Azure Synapse Analytics Azure SQL Database Azure Blob Storage Azure Data Lake Python SQL ETL Scala SQL Server Integration Services Cosmos DB Apache Spark Hadoop Kafka Git Power BI Azure Machine Learning Agile Scrum
Skill cluster (8 dimension groups, role-scoped)
Programming Languages for Data Work
Python SQL Scala
ETL and ELT Tooling
Apache Spark Hadoop
BI and Visualization Tools
Power BI
Cloud Data Warehouses
Azure Synapse Analytics
Cloud Platforms
Azure
Cloud Storage and File Formats
Azure Blob Storage
Messaging and Event Streaming
Kafka
Cross-cutting / unaligned
Azure Data Factory Azure Databricks Azure SQL Database Azure Data Lake ETL SQL Server Integration Services Cosmos DB Git Azure Machine Learning Agile Scrum
Show KRA description ↓
• Data Pipeline Development: Design, develop, and optimize scalable data pipelines and ETL workflows on Azure, utilizing services such as Azure Data Factory, Azure Databricks, and Azure Synapse Analytics. • Cloud Data Integration: Integrate data from multiple sources, both structured and unstructured, into Azure-based data lakes and data warehouses. • ETL Process Management: Develop, test, and maintain ETL processes that efficiently transform, load, and process large volumes of data. • Azure Services Utilization: Leverage Azure services like Azure SQL Database, Azure Blob Storage, Azure Data Lake, Azure Databricks, and Azure Synapse Analytics to manage and analyze data. • Data Warehousing & Data Lakes: Design and manage enterprise-level data warehouses and data lakes in Azure, ensuring data quality, accessibility, and security. • Optimization & Automation: Continuously optimize and automate data pipelines for performance and cost-efficiency. Monitor and troubleshoot data pipeline performance issues. • Data Governance & Security: Implement data governance practices, including data security, privacy compliance, and ensuring adherence to industry standards. • Collaboration: Work closely with data scientists, data analysts, and business stakeholders to understand data requirements and deliver actionable data insights. • Documentation & Reporting: Document data pipeline workflows, architecture, and other technical processes to ensure transparency and maintainability. • Stay Updated: Stay current with emerging Azure technologies, data engineering best practices, and industry trends to ensure the company's data infrastructure remains cutting-edge. • Experience: Minimum of 5 years of experience in data engineering or a related field, with extensive experience in Azure cloud technologies. • Azure Expertise: Hands-on experience with key Azure services such as Azure Data Factory, Azure Databricks, Azure SQL Database, Azure Blob Storage, Azure Synapse Analytics, and Azure Data Lake. • Programming Skills: Strong programming skills in languages like Python, SQL, and/or Scala for building data pipelines and automation. • ETL Tools: Experience with ETL tools such as Azure Data Factory, SQL Server Integration Services (SSIS), or similar platforms. • Database Management: Strong knowledge of relational databases, NoSQL databases (e.g., Cosmos DB), and data warehousing concepts. • Big Data Technologies: Familiarity with big data technologies like Apache Spark, Hadoop, or Kafka is a plus. • Data Modeling & Architecture: Experience with data modeling, data architecture, and designing scalable data solutions. • Problem-Solving Skills: Strong analytical skills with the ability to diagnose and resolve complex data issues. • Version Control: Familiarity with Git or other version control systems for collaborative development. • Communication: Strong written and verbal communication skills with the ability to communicate complex technical concepts to non-technical stakeholders. • Certifications: Microsoft Certified: Azure Data Engineer Associate or any related Azure certifications. • Big Data Frameworks: Hands-on experience with big data frameworks like Apache Spark or Hadoop. • Agile Experience: I am familiar with Agile methodologies and working in Scrum teams. • Data Analytics: Experience with data analytics platforms and tools such as Power BI, Azure Machine Learning, or similar solutions.

Signals

Skill data-engineer
0.50
Alias data-engineer
1.00
KRA data-engineer
0.69

Post-classification

Centroidupdated · n=129
Alias collision log
New-role queue
New skills captured7
New KRA captured

Captured for admin review

Azure Data Factory primary Data Engineer pending
Azure Databricks primary Data Engineer pending
Azure SQL Database primary Data Engineer pending
Azure Data Lake primary Data Engineer pending
ETL primary Data Engineer pending
SQL Server Integration Services Data Engineer pending
Azure Machine Learning Data Engineer pending
Status: completed Created: 2026-05-27T14:06:56.920982Z Updated: 2026-05-27T14:09:57.131498Z API 3 duration: 72813 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 0.50 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
1
Skipped

Job description

Job Title: Azure Data Engineer
Location: Remote
Job Type: Full-Time
Experience: 5 years


Responsibilities:
• Data Pipeline Development: Design, develop, and optimize scalable data pipelines and ETL workflows on Azure, utilizing services such as Azure Data Factory, Azure Databricks, and Azure Synapse Analytics.
• Cloud Data Integration: Integrate data from multiple sources, both structured and unstructured, into Azure-based data lakes and data warehouses.
• ETL Process Management: Develop, test, and maintain ETL processes that efficiently transform, load, and process large volumes of data.
• Azure Services Utilization: Leverage Azure services like Azure SQL Database, Azure Blob Storage, Azure Data Lake, Azure Databricks, and Azure Synapse Analytics to manage and analyze data.
• Data Warehousing & Data Lakes: Design and manage enterprise-level data warehouses and data lakes in Azure, ensuring data quality, accessibility, and security.
• Optimization & Automation: Continuously optimize and automate data pipelines for performance and cost-efficiency. Monitor and troubleshoot data pipeline performance issues.
• Data Governance & Security: Implement data governance practices, including data security, privacy compliance, and ensuring adherence to industry standards.
• Collaboration: Work closely with data scientists, data analysts, and business stakeholders to understand data requirements and deliver actionable data insights.
• Documentation & Reporting: Document data pipeline workflows, architecture, and other technical processes to ensure transparency and maintainability.
• Stay Updated: Stay current with emerging Azure technologies, data engineering best practices, and industry trends to ensure the company's data infrastructure remains cutting-edge.


Required Qualifications:
• Experience: Minimum of 5 years of experience in data engineering or a related field, with extensive experience in Azure cloud technologies.
• Azure Expertise: Hands-on experience with key Azure services such as Azure Data Factory, Azure Databricks, Azure SQL Database, Azure Blob Storage, Azure Synapse Analytics, and Azure Data Lake.
• Programming Skills: Strong programming skills in languages like Python, SQL, and/or Scala for building data pipelines and automation.
• ETL Tools: Experience with ETL tools such as Azure Data Factory, SQL Server Integration Services (SSIS), or similar platforms.
• Database Management: Strong knowledge of relational databases, NoSQL databases (e.g., Cosmos DB), and data warehousing concepts.
• Big Data Technologies: Familiarity with big data technologies like Apache Spark, Hadoop, or Kafka is a plus.
• Data Modeling & Architecture: Experience with data modeling, data architecture, and designing scalable data solutions.
• Problem-Solving Skills: Strong analytical skills with the ability to diagnose and resolve complex data issues.
• Version Control: Familiarity with Git or other version control systems for collaborative development.
• Communication: Strong written and verbal communication skills with the ability to communicate complex technical concepts to non-technical stakeholders.


Preferred Qualifications:
• Certifications: Microsoft Certified: Azure Data Engineer Associate or any related Azure certifications.
• Big Data Frameworks: Hands-on experience with big data frameworks like Apache Spark or Hadoop.
• Agile Experience: I am familiar with Agile methodologies and working in Scrum teams.
• Data Analytics: Experience with data analytics platforms and tools such as Power BI, Azure Machine Learning, or similar solutions.

Skills from this JD

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

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

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)
Azure Data Factory 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
Azure Databricks 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
Azure Synapse Analytics Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Azure Synapse Analytics id=108 · azure-synapse-analytics

Aliases — catalog

  • Azure Synapse Analytics (CANONICAL) primary

Context tags (catalog)

Apache Spark Azure Data Lake Storage Data Factory Delta Lake PolyBase SQL pools Spark pools Synapse Studio T-SQL dedicated SQL pool linked services notebooks pipelines serverless SQL pool workspace

Stored enrichment (catalog DB)

Category
Service
Sub-category
Analytics Service
Vendor
Microsoft
License
proprietary
Year introduced
2019
Confidence
0.95
Version strategy
NOT_APPLICABLE

Maturity reasoning: Common in cloud data-platform JDs and Microsoft’s Azure analytics stack; often listed alongside Databricks/ADF for warehousing and ETL, indicating broad hiring demand.

Skill profile (library / DB)

Skill nature
CLOUD_SERVICE
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
11
Sub-category id
117
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Cloud Data Warehouses Catalog dimension db id 22

    Library dimension (catalog)

    Roles linked in library: Data Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Cloud Data Warehouses
cloud-data-warehouses
Existing dimension (library) · Role↔dimension saved
Azure SQL Database 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
Databases
Skill nature
PLATFORM
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Azure Blob Storage Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Azure Blob Storage id=172 · azure-blob-storage

Aliases — catalog

  • Azure Blob Storage (CANONICAL) primary

Context tags (catalog)

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

Stored enrichment (catalog DB)

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

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

Skill profile (library / DB)

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

Dimensions (API 2 worklist)

  • Cloud Storage and Data Services Catalog dimension db id 144

    Library dimension (catalog)

    Roles linked in library: Cloud Architect

  • Cloud Storage and File Formats Catalog dimension db id 35

    Library dimension (catalog)

    Roles linked in library: Data Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Cloud Storage and Data Services
cloud-storage-and-data-services
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Cloud Storage and File Formats
cloud-storage-and-file-formats
Existing dimension (library) · Role↔dimension saved
Azure Data Lake 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
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 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 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)
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 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 for Data Work
programming-languages-for-data-work
Existing dimension (library) · Role↔dimension saved
Scala Secondary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Scala id=102 · scala

Aliases — catalog

  • Scala (CANONICAL) primary

Context tags (catalog)

Akka Apache Kafka Cats Flink JVM Monads Play Framework SBT ScalaTest Shapeless Spark Spark SQL ZIO case class for-comprehension functional programming implicit pattern matching typeclass

Stored enrichment (catalog DB)

Category
Language
Sub-category
Programming Language
Vendor
EPFL
License
apache_2
Year introduced
2004
Confidence
0.99
Version strategy
NOT_APPLICABLE

Maturity reasoning: Scala still appears in many backend/data engineering JDs, especially with Spark and Akka, and remains supported by major JVM ecosystems; it’s not a sunset technology.

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 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

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
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)
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
SQL Server Integration Services Secondary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

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

Derived legacy fields
Category
Data Engineering Tools
Sub-category
general
Skill nature
TOOL
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Cosmos DB Secondary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Cosmos DB id=515 · cosmos-db

Aliases — catalog

  • Cosmos DB (CANONICAL)

Context tags (catalog)

Azure Cassandra API Core (SQL) API Gremlin API MongoDB API NoSQL RU/s TTL Table API change feed consistency levels global distribution multi-region writes partition key throughput

Stored enrichment (catalog DB)

Category
Service
Sub-category
Managed Nosql Database Service
Vendor
Microsoft
License
proprietary
Year introduced
2010
Confidence
0.97
Version strategy
NOT_APPLICABLE

Maturity reasoning: Frequently appears in Azure/cloud data engineer JDs and Microsoft positions; strong vendor support and active docs indicate broad adoption rather than niche use.

Skill profile (library / DB)

Skill nature
CLOUD_SERVICE
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
11
Sub-category id
55
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)
Apache Spark Secondary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Apache Spark id=1350 · apache-spark

Aliases — catalog

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

Context tags (catalog)

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

Stored enrichment (catalog DB)

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

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

Skill profile (library / DB)

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

Dimensions (API 2 worklist)

  • ETL and ELT Tooling Catalog dimension db id 24

    Library dimension (catalog)

    Roles linked in library: Data Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
ETL and ELT Tooling
etl-and-elt-tooling
Existing dimension (library) · Role↔dimension saved
Hadoop Secondary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Hadoop id=1351 · hadoop

Aliases — catalog

  • Hadoop (CANONICAL)

Context tags (catalog)

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

Stored enrichment (catalog DB)

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

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

Skill profile (library / DB)

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

Dimensions (API 2 worklist)

  • ETL and ELT Tooling Catalog dimension db id 24

    Library dimension (catalog)

    Roles linked in library: Data Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
ETL and ELT Tooling
etl-and-elt-tooling
Existing dimension (library) · Role↔dimension saved
Kafka Secondary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Kafka id=36 · kafka

Aliases — catalog

  • Kafka (CANONICAL) primary

Context tags (catalog)

Apache Flink Apache Kafka Apache Pulsar Apache Spark Avro KSQL Kafka API Kafka Connect Kafka Streams ZooKeeper Zookeeper backpressure brokers consumer consumer group consumer groups event sourcing event-driven architecture exactly-once semantics fault tolerance high throughput log compaction message broker message queue microservices offsets partition partitioning partitions producer producer API real-time analytics real-time data replication schema registry stream processing topic topic partitioning topics

Stored enrichment (catalog DB)

Category
Datastore
Sub-category
Event Stream Store
Vendor
Confluent
License
apache_2
Year introduced
2011
Confidence
0.90
Version strategy
NOT_APPLICABLE

Maturity reasoning: Kafka appears in many production JDs for event streaming and data pipelines, and remains a standard platform in cloud/vendor offerings (e.g., Confluent, AWS MSK), indicating broad hiring demand.

Skill profile (library / DB)

Skill nature
TOOL
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
3
Sub-category id
3533
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Asynchronous Messaging and Event Streaming Catalog dimension db id 297

    Library dimension (catalog)

    Roles linked in library: .NET Backend Developer, Go Backend Developer, Kotlin Backend Developer, Node.js Backend Developer, Scala Backend Developer

  • Messaging and Background Jobs Catalog dimension db id 291

    Library dimension (catalog)

    Roles linked in library: PHP Backend Developer, Python Backend Developer, Ruby Backend Developer

  • Messaging and Event Streaming Catalog dimension db id 8

    Library dimension (catalog)

    Roles linked in library: Backend Developer, Data Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Asynchronous Messaging and Event Streaming
asynchronous-messaging-and-event-streaming
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Messaging and Background Jobs
messaging-and-background-jobs
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Messaging and Event Streaming
messaging-and-event-streaming
Existing dimension (library) · Role↔dimension saved
Git Secondary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Git id=1002 · git

Aliases — catalog

  • Git (CANONICAL)

Context tags (catalog)

CI/CD GitHub GitLab branching checkout clone commit fork merging pull request rebase remote repository stash versioning

Stored enrichment (catalog DB)

Category
Tool
Sub-category
Version Control Tool
Vendor
Linus Torvalds
License
gpl_v2
Year introduced
2005
Confidence
0.99
Version strategy
NOT_APPLICABLE

Maturity reasoning: Git is a hiring-pipeline staple: it appears in the vast majority of software engineering job descriptions and is the default VCS on GitHub/GitLab/Bitbucket.

Skill profile (library / DB)

Skill nature
TOOL
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
13
Sub-category id
730
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)
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
Azure Machine Learning Secondary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Azure ML id=212 · azure-ml

Aliases — catalog

  • Azure ML (CANONICAL) primary

Context tags (catalog)

AKS AutoML Azure Databricks Azure DevOps Azure Functions Azure Machine Learning ML Studio MLflow REST API SDK v2 TensorFlow automated ML compute cluster compute instance data labeling data preprocessing datastore designer endpoint deployment feature store hyperparameter tuning model deployment model monitoring model registry notebooks pipeline orchestration pipelines scikit-learn workspace

Stored enrichment (catalog DB)

Category
Platform
Sub-category
Ml Platform
Vendor
Microsoft
License
proprietary
Year introduced
2018
Confidence
0.97
Version strategy
NOT_APPLICABLE

Maturity reasoning: Azure ML appears frequently in ML/DS job postings and Microsoft’s Azure AI portfolio, indicating broad enterprise adoption for model training and deployment on Azure.

Skill profile (library / DB)

Skill nature
PLATFORM
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
9
Sub-category id
175
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • MLOps Platforms and Lifecycle Catalog dimension db id 43

    Library dimension (catalog)

    Roles linked in library: ML Engineer, MLOps Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
MLOps Platforms and Lifecycle
mlops-platforms-and-lifecycle
Skipped — no persistable v3 meta for new skill
skill_not_in_db_v3_proposed
Agile Secondary 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
367
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)
Scrum Secondary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Scrum id=1215 · scrum

Aliases — catalog

  • Scrum (CANONICAL) primary

Context tags (catalog)

Agile Kanban backlog burndown chart cross-functional team daily standup definition of done increment product owner release planning retrospective scrum framework scrum master sprint story points user stories velocity

Stored enrichment (catalog DB)

Category
Methodology
Sub-category
Scrum
Confidence
0.99
Version strategy
NOT_APPLICABLE

Maturity reasoning: Scrum appears in a large share of agile project-management and product-owner job descriptions, and Scrum Alliance/PSM certifications are widely requested in hiring pipelines.

Skill profile (library / DB)

Skill nature
METHODOLOGY
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
8
Sub-category id
921
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)

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
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 Synapse Analytics in_db
Cloud Data Warehouses
cloud-data-warehouses
Existing dimension (library) · Role↔dimension saved
Azure Blob Storage in_db
Cloud Storage and Data Services
cloud-storage-and-data-services
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Azure Blob Storage in_db
Cloud Storage and File Formats
cloud-storage-and-file-formats
Existing dimension (library) · Role↔dimension saved
Python in_db
Cloud Security Scripting & DSL Languages
cloud-security-scripting-dsl-languages
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Python in_db
Programming Languages
programming-languages
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Python in_db
Programming Languages 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)
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 for Data Work
programming-languages-for-data-work
Existing dimension (library) · Role↔dimension saved
Scala in_db
Programming Languages for Data Work
programming-languages-for-data-work
Existing dimension (library) · Role↔dimension saved
Scala in_db
Programming Languages for ML Systems
programming-languages-for-ml-systems
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Cosmos DB in_db
NoSQL Databases
nosql-databases
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Apache Spark in_db
ETL and ELT Tooling
etl-and-elt-tooling
Existing dimension (library) · Role↔dimension saved
Hadoop in_db
ETL and ELT Tooling
etl-and-elt-tooling
Existing dimension (library) · Role↔dimension saved
Kafka in_db
Asynchronous Messaging and Event Streaming
asynchronous-messaging-and-event-streaming
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Kafka in_db
Messaging and Background Jobs
messaging-and-background-jobs
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Kafka in_db
Messaging and Event Streaming
messaging-and-event-streaming
Existing dimension (library) · Role↔dimension saved
Git in_db
React Frontend Development
d_init_01
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Power BI in_db
BI and Visualization Tools
bi-and-visualization-tools
Existing dimension (library) · Role↔dimension saved
Azure Machine Learning new
MLOps Platforms and Lifecycle
mlops-platforms-and-lifecycle
Skipped — no persistable v3 meta for new skill skill_not_in_db_v3_proposed
Agile in_db
React Frontend Development
d_init_01
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Scrum in_db
React Frontend Development
d_init_01
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)

Library artifacts (this run)

Kind Detail DB id
canonical_skill_proposed Azure Data Factory | type=Cloud Platforms subtype=general nature=PLATFORM lifespan=MULTI_YEAR
canonical_skill_proposed Azure Databricks | type=Cloud Platforms subtype=general nature=PLATFORM lifespan=MULTI_YEAR
canonical_skill_proposed Azure SQL Database | type=Cloud Platforms subtype=Databases nature=PLATFORM lifespan=MULTI_YEAR
canonical_skill_proposed Azure Data Lake | type=Cloud Platforms subtype=general nature=PLATFORM lifespan=MULTI_YEAR
canonical_skill_proposed ETL | type=Data Engineering Tools subtype=general nature=PRACTICE lifespan=MULTI_YEAR
canonical_skill_proposed SQL Server Integration Services | type=Data Engineering Tools subtype=general nature=TOOL lifespan=MULTI_YEAR
dimension_skill_link_proposed Azure Machine Learning ↔ MLOps Platforms and Lifecycle
nano JD Parser — gpt-4.1-nano click to toggle
RoleAzure Data Engineer
ExperienceMinimum of 5 years of experience in data engineering or a related field
DomainIT Services & Consulting
Location(remote)
JD type pass

Certifications

Microsoft Certified: Azure Data Engineer Associate
Show raw JSON
{
  "JD_type": "pass",
  "about_company": null,
  "certifications": [
    "Microsoft Certified: Azure Data Engineer Associate"
  ],
  "company_name": null,
  "ctc": null,
  "domain": {
    "primary": {
      "aliases": [],
      "domain": "IT Services \u0026 Consulting"
    },
    "secondary": null
  },
  "education": [],
  "experience": {
    "max": null,
    "min": 5,
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  "job_locations": [
    {
      "aliases": [],
      "city": null,
      "country": null,
      "state": null,
      "work_mode": "remote"
    }
  ],
  "role": "Azure Data Engineer",
  "role_aliases": [
    "Data Engineer",
    "Azure Engineer",
    "Cloud Data Engineer"
  ],
  "role_archetype": "Data",
  "roles_and_responsibilities": [
    {
      "bullet_count": 10,
      "heading": "Responsibilities",
      "heading_was_present": true,
      "source_marker": {
        "first_5_words": "\u2022 Data Pipeline Development: Design,",
        "last_5_words": "data infrastructure remains cutting-edge."
      },
      "text": "\u2022 Data Pipeline Development: Design, develop, and optimize scalable data pipelines and ETL workflows on Azure, utilizing services such as Azure Data Factory, Azure Databricks, and Azure Synapse Analytics.\n\u2022 Cloud Data Integration: Integrate data from multiple sources, both structured and unstructured, into Azure-based data lakes and data warehouses.\n\u2022 ETL Process Management: Develop, test, and maintain ETL processes that efficiently transform, load, and process large volumes of data.\n\u2022 Azure Services Utilization: Leverage Azure services like Azure SQL Database, Azure Blob Storage, Azure Data Lake, Azure Databricks, and Azure Synapse Analytics to manage and analyze data.\n\u2022 Data Warehousing \u0026 Data Lakes: Design and manage enterprise-level data warehouses and data lakes in Azure, ensuring data quality, accessibility, and security.\n\u2022 Optimization \u0026 Automation: Continuously optimize and automate data pipelines for performance and cost-efficiency. Monitor and troubleshoot data pipeline performance issues.\n\u2022 Data Governance \u0026 Security: Implement data governance practices, including data security, privacy compliance, and ensuring adherence to industry standards.\n\u2022 Collaboration: Work closely with data scientists, data analysts, and business stakeholders to understand data requirements and deliver actionable data insights.\n\u2022 Documentation \u0026 Reporting: Document data pipeline workflows, architecture, and other technical processes to ensure transparency and maintainability.\n\u2022 Stay Updated: Stay current with emerging Azure technologies, data engineering best practices, and industry trends to ensure the company\u0027s data infrastructure remains cutting-edge.",
      "word_count": 239
    },
    {
      "bullet_count": 10,
      "heading": "Required Qualifications",
      "heading_was_present": true,
      "source_marker": {
        "first_5_words": "\u2022 Experience: Minimum of 5",
        "last_5_words": "to non-technical stakeholders."
      },
      "text": "\u2022 Experience: Minimum of 5 years of experience in data engineering or a related field, with extensive experience in Azure cloud technologies.\n\u2022 Azure Expertise: Hands-on experience with key Azure services such as Azure Data Factory, Azure Databricks, Azure SQL Database, Azure Blob Storage, Azure Synapse Analytics, and Azure Data Lake.\n\u2022 Programming Skills: Strong programming skills in languages like Python, SQL, and/or Scala for building data pipelines and automation.\n\u2022 ETL Tools: Experience with ETL tools such as Azure Data Factory, SQL Server Integration Services (SSIS), or similar platforms.\n\u2022 Database Management: Strong knowledge of relational databases, NoSQL databases (e.g., Cosmos DB), and data warehousing concepts.\n\u2022 Big Data Technologies: Familiarity with big data technologies like Apache Spark, Hadoop, or Kafka is a plus.\n\u2022 Data Modeling \u0026 Architecture: Experience with data modeling, data architecture, and designing scalable data solutions.\n\u2022 Problem-Solving Skills: Strong analytical skills with the ability to diagnose and resolve complex data issues.\n\u2022 Version Control: Familiarity with Git or other version control systems for collaborative development.\n\u2022 Communication: Strong written and verbal communication skills with the ability to communicate complex technical concepts to non-technical stakeholders.",
      "word_count": 229
    },
    {
      "bullet_count": 4,
      "heading": "Preferred Qualifications",
      "heading_was_present": true,
      "source_marker": {
        "first_5_words": "\u2022 Certifications: Microsoft Certified:",
        "last_5_words": "or similar solutions."
      },
      "text": "\u2022 Certifications: Microsoft Certified: Azure Data Engineer Associate or any related Azure certifications.\n\u2022 Big Data Frameworks: Hands-on experience with big data frameworks like Apache Spark or Hadoop.\n\u2022 Agile Experience: I am familiar with Agile methodologies and working in Scrum teams.\n\u2022 Data Analytics: Experience with data analytics platforms and tools such as Power BI, Azure Machine Learning, or similar solutions.",
      "word_count": 56
    }
  ],
  "urls": []
}
API 1 — extract-from-jd click to toggle
{
  "final_skills": [
    {
      "is_primary": true,
      "skill_name": "Azure"
    },
    {
      "is_primary": true,
      "skill_name": "Azure Data Factory"
    },
    {
      "is_primary": true,
      "skill_name": "Azure Databricks"
    },
    {
      "is_primary": true,
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    },
    {
      "is_primary": true,
      "skill_name": "Azure SQL Database"
    },
    {
      "is_primary": true,
      "skill_name": "Azure Blob Storage"
    },
    {
      "is_primary": true,
      "skill_name": "Azure Data Lake"
    },
    {
      "is_primary": true,
      "skill_name": "Python"
    },
    {
      "is_primary": true,
      "skill_name": "SQL"
    },
    {
      "is_primary": false,
      "skill_name": "Scala"
    },
    {
      "is_primary": true,
      "skill_name": "ETL"
    },
    {
      "is_primary": false,
      "skill_name": "SQL Server Integration Services"
    },
    {
      "is_primary": false,
      "skill_name": "Cosmos DB"
    },
    {
      "is_primary": false,
      "skill_name": "Apache Spark"
    },
    {
      "is_primary": false,
      "skill_name": "Hadoop"
    },
    {
      "is_primary": false,
      "skill_name": "Kafka"
    },
    {
      "is_primary": false,
      "skill_name": "Git"
    },
    {
      "is_primary": false,
      "skill_name": "Power BI"
    },
    {
      "is_primary": false,
      "skill_name": "Azure Machine Learning"
    },
    {
      "is_primary": false,
      "skill_name": "Agile"
    },
    {
      "is_primary": false,
      "skill_name": "Scrum"
    }
  ],
  "jd_role": {
    "display_name": "Azure Data Engineer",
    "rationale": null,
    "role_aliases": [
      "Data Engineer",
      "Azure Engineer",
      "Cloud Data Engineer"
    ],
    "role_archetype": "Data",
    "slug": ""
  },
  "nano_parsed": {
    "JD_type": "pass",
    "about_company": null,
    "certifications": [
      "Microsoft Certified: Azure Data Engineer Associate"
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    "company_name": null,
    "ctc": null,
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    },
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      "min": 5,
      "raw": "Minimum of 5 years of experience in data engineering or a related field"
    },
    "job_locations": [
      {
        "aliases": [],
        "city": null,
        "country": null,
        "state": null,
        "work_mode": "remote"
      }
    ],
    "role": "Azure Data Engineer",
    "role_aliases": [
      "Data Engineer",
      "Azure Engineer",
      "Cloud Data Engineer"
    ],
    "role_archetype": "Data",
    "roles_and_responsibilities": [
      {
        "bullet_count": 10,
        "heading": "Responsibilities",
        "heading_was_present": true,
        "source_marker": {
          "first_5_words": "\u2022 Data Pipeline Development: Design,",
          "last_5_words": "data infrastructure remains cutting-edge."
        },
        "text": "\u2022 Data Pipeline Development: Design, develop, and optimize scalable data pipelines and ETL workflows on Azure, utilizing services such as Azure Data Factory, Azure Databricks, and Azure Synapse Analytics.\n\u2022 Cloud Data Integration: Integrate data from multiple sources, both structured and unstructured, into Azure-based data lakes and data warehouses.\n\u2022 ETL Process Management: Develop, test, and maintain ETL processes that efficiently transform, load, and process large volumes of data.\n\u2022 Azure Services Utilization: Leverage Azure services like Azure SQL Database, Azure Blob Storage, Azure Data Lake, Azure Databricks, and Azure Synapse Analytics to manage and analyze data.\n\u2022 Data Warehousing \u0026 Data Lakes: Design and manage enterprise-level data warehouses and data lakes in Azure, ensuring data quality, accessibility, and security.\n\u2022 Optimization \u0026 Automation: Continuously optimize and automate data pipelines for performance and cost-efficiency. Monitor and troubleshoot data pipeline performance issues.\n\u2022 Data Governance \u0026 Security: Implement data governance practices, including data security, privacy compliance, and ensuring adherence to industry standards.\n\u2022 Collaboration: Work closely with data scientists, data analysts, and business stakeholders to understand data requirements and deliver actionable data insights.\n\u2022 Documentation \u0026 Reporting: Document data pipeline workflows, architecture, and other technical processes to ensure transparency and maintainability.\n\u2022 Stay Updated: Stay current with emerging Azure technologies, data engineering best practices, and industry trends to ensure the company\u0027s data infrastructure remains cutting-edge.",
        "word_count": 239
      },
      {
        "bullet_count": 10,
        "heading": "Required Qualifications",
        "heading_was_present": true,
        "source_marker": {
          "first_5_words": "\u2022 Experience: Minimum of 5",
          "last_5_words": "to non-technical stakeholders."
        },
        "text": "\u2022 Experience: Minimum of 5 years of experience in data engineering or a related field, with extensive experience in Azure cloud technologies.\n\u2022 Azure Expertise: Hands-on experience with key Azure services such as Azure Data Factory, Azure Databricks, Azure SQL Database, Azure Blob Storage, Azure Synapse Analytics, and Azure Data Lake.\n\u2022 Programming Skills: Strong programming skills in languages like Python, SQL, and/or Scala for building data pipelines and automation.\n\u2022 ETL Tools: Experience with ETL tools such as Azure Data Factory, SQL Server Integration Services (SSIS), or similar platforms.\n\u2022 Database Management: Strong knowledge of relational databases, NoSQL databases (e.g., Cosmos DB), and data warehousing concepts.\n\u2022 Big Data Technologies: Familiarity with big data technologies like Apache Spark, Hadoop, or Kafka is a plus.\n\u2022 Data Modeling \u0026 Architecture: Experience with data modeling, data architecture, and designing scalable data solutions.\n\u2022 Problem-Solving Skills: Strong analytical skills with the ability to diagnose and resolve complex data issues.\n\u2022 Version Control: Familiarity with Git or other version control systems for collaborative development.\n\u2022 Communication: Strong written and verbal communication skills with the ability to communicate complex technical concepts to non-technical stakeholders.",
        "word_count": 229
      },
      {
        "bullet_count": 4,
        "heading": "Preferred Qualifications",
        "heading_was_present": true,
        "source_marker": {
          "first_5_words": "\u2022 Certifications: Microsoft Certified:",
          "last_5_words": "or similar solutions."
        },
        "text": "\u2022 Certifications: Microsoft Certified: Azure Data Engineer Associate or any related Azure certifications.\n\u2022 Big Data Frameworks: Hands-on experience with big data frameworks like Apache Spark or Hadoop.\n\u2022 Agile Experience: I am familiar with Agile methodologies and working in Scrum teams.\n\u2022 Data Analytics: Experience with data analytics platforms and tools such as Power BI, Azure Machine Learning, or similar solutions.",
        "word_count": 56
      }
    ],
    "urls": []
  },
  "rejected": false,
  "rejection_reason": null,
  "run_id": "857a64e4-51b1-4a9c-b147-9672375dc355",
  "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": "Monitors pipeline health, SLA breach alerts, and job failure notifications, and performs root cause analysis for data pipeline incidents.",
            "sentence": "Monitor and troubleshoot data pipeline performance issues.",
            "similarity": 0.7156
          },
          {
            "kra_text": "Works with data analysts, data scientists, and business stakeholders to define data models, ingestion schedules, and data delivery requirements.",
            "sentence": "Collaboration: Work closely with data scientists, data analysts, and business stakeholders to understand data requirements and deliver actionable data insights.",
            "similarity": 0.7016
          },
          {
            "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": "Data Pipeline Development: Design, develop, and optimize scalable data pipelines and ETL workflows on Azure, utilizing services such as Azure Data Factory, Azure Databricks, and Azure Synapse Analytics.",
            "similarity": 0.6493
          }
        ],
        "matched_count": null,
        "matched_skills": null,
        "role_id": 2,
        "score": 0.6888,
        "slug": "data-engineer",
        "total_count": null
      },
      {
        "display_name": "AI Engineer",
        "kra_matches": [
          {
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            "sentence": "Optimization \u0026 Automation: Continuously optimize and automate data pipelines for performance and cost-efficiency.",
            "similarity": 0.5829
          },
          {
            "kra_text": "Monitors AI feature behavior in production including response quality metrics, latency percentiles, token cost per request, and error rates.",
            "sentence": "Monitor and troubleshoot data pipeline performance issues.",
            "similarity": 0.5402
          },
          {
            "kra_text": "Designs and implements prompt engineering workflows, few-shot examples, chain-of-thought patterns, and structured output parsing for AI feature pipelines.",
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            "similarity": 0.5222
          }
        ],
        "matched_count": null,
        "matched_skills": null,
        "role_id": 13,
        "score": 0.5485,
        "slug": "ai-engineer",
        "total_count": null
      },
      {
        "display_name": "DevOps Engineer",
        "kra_matches": [
          {
            "kra_text": "Monitors CI/CD pipeline reliability, identifies bottlenecks in delivery workflows, and improves deployment frequency, lead time, and failure recovery rate.",
            "sentence": "Monitor and troubleshoot data pipeline performance issues.",
            "similarity": 0.6613
          },
          {
            "kra_text": "Monitors CI/CD pipeline reliability, identifies bottlenecks in delivery workflows, and improves deployment frequency, lead time, and failure recovery rate.",
            "sentence": "Documentation \u0026 Reporting: Document data pipeline workflows, architecture, and other technical processes to ensure transparency and maintainability.",
            "similarity": 0.4938
          },
          {
            "kra_text": "Monitors CI/CD pipeline reliability, identifies bottlenecks in delivery workflows, and improves deployment frequency, lead time, and failure recovery rate.",
            "sentence": "Optimization \u0026 Automation: Continuously optimize and automate data pipelines for performance and cost-efficiency.",
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          }
        ],
        "matched_count": null,
        "matched_skills": null,
        "role_id": 10,
        "score": 0.5465,
        "slug": "devops-engineer",
        "total_count": null
      },
      {
        "display_name": "Java Backend Developer",
        "kra_matches": [
          {
            "kra_text": "persistence and data modeling",
            "sentence": "Data Modeling \u0026 Architecture: Experience with data modeling, data architecture, and designing scalable data solutions.",
            "similarity": 0.5629
          },
          {
            "kra_text": "backend performance tuning",
            "sentence": "Monitor and troubleshoot data pipeline performance issues.",
            "similarity": 0.5213
          },
          {
            "kra_text": "persistence and data modeling",
            "sentence": "Documentation \u0026 Reporting: Document data pipeline workflows, architecture, and other technical processes to ensure transparency and maintainability.",
            "similarity": 0.4678
          }
        ],
        "matched_count": null,
        "matched_skills": null,
        "role_id": 79,
        "score": 0.5173,
        "slug": "java-backend-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": "Optimization \u0026 Automation: Continuously optimize and automate data pipelines for performance and cost-efficiency.",
            "similarity": 0.5361
          },
          {
            "kra_text": "Sets up model monitoring dashboards, data drift detection, prediction performance tracking, and alert routing for production ML systems.",
            "sentence": "Monitor and troubleshoot data pipeline performance issues.",
            "similarity": 0.5241
          },
          {
            "kra_text": "Coordinates model promotion workflows across development, staging, and production environments including integration testing and data contract validation.",
            "sentence": "Documentation \u0026 Reporting: Document data pipeline workflows, architecture, and other technical processes to ensure transparency and maintainability.",
            "similarity": 0.4798
          }
        ],
        "matched_count": null,
        "matched_skills": null,
        "role_id": 16,
        "score": 0.5133,
        "slug": "ml-ops-engineer",
        "total_count": null
      }
    ],
    "skill_match_roles": [
      {
        "display_name": "Data Engineer",
        "kra_matches": null,
        "matched_count": 5,
        "matched_skills": [
          "Azure",
          "Azure Blob Storage",
          "Azure Synapse Analytics",
          "Python",
          "SQL"
        ],
        "role_id": 2,
        "score": 0.5,
        "slug": "data-engineer",
        "total_count": 10
      },
      {
        "display_name": "ML Engineer",
        "kra_matches": null,
        "matched_count": 2,
        "matched_skills": [
          "Azure",
          "Python"
        ],
        "role_id": 3,
        "score": 0.2,
        "slug": "ml-engineer",
        "total_count": 10
      },
      {
        "display_name": "Backend Developer",
        "kra_matches": null,
        "matched_count": 2,
        "matched_skills": [
          "Azure",
          "Python"
        ],
        "role_id": 1,
        "score": 0.2,
        "slug": "backend-engineer",
        "total_count": 10
      },
      {
        "display_name": "Cyber Security Engineer",
        "kra_matches": null,
        "matched_count": 2,
        "matched_skills": [
          "Azure",
          "Python"
        ],
        "role_id": 5,
        "score": 0.2,
        "slug": "cybersecurity-engineer",
        "total_count": 10
      },
      {
        "display_name": "Cloud Architect",
        "kra_matches": null,
        "matched_count": 2,
        "matched_skills": [
          "Azure",
          "Azure Blob Storage"
        ],
        "role_id": 9,
        "score": 0.2,
        "slug": "cloud-architect",
        "total_count": 10
      }
    ]
  },
  "stage4_decision": {
    "alias_collision_detected": false,
    "case": "A",
    "chosen_role": {
      "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
    },
    "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 0.50 does not contradict",
    "sub_role": null
  },
  "stage5_updates": {
    "centroid_n_after": 129,
    "centroid_updated": true,
    "collision_log_id": null,
    "new_kra_attached": null,
    "new_skills_attached": [
      {
        "is_primary": true,
        "queue_id": 6972,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "Azure Data Factory",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 6973,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "Azure Databricks",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 6974,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "Azure SQL Database",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 6975,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "Azure Data Lake",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 6976,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "ETL",
        "status": "pending"
      },
      {
        "is_primary": false,
        "queue_id": 6977,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "SQL Server Integration Services",
        "status": "pending"
      },
      {
        "is_primary": false,
        "queue_id": 6978,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "Azure Machine Learning",
        "status": "pending"
      }
    ],
    "queue_entry_id": null,
    "v3_pipeline_triggered": false,
    "v3_role_slug": null,
    "v3_run_id": null
  }
}
API 2 — extract-details
{
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      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 407,
      "existing_alias_text": "Azure",
      "input_term": "Azure",
      "matched_canonical": {
        "category_id": 9,
        "display_name": "Azure",
        "id": 188,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "PLATFORM",
        "slug": "azure",
        "sub_category_id": 46,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 302,
      "existing_alias_text": "Azure Synapse Analytics",
      "input_term": "Azure Synapse Analytics",
      "matched_canonical": {
        "category_id": 11,
        "display_name": "Azure Synapse Analytics",
        "id": 108,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "CLOUD_SERVICE",
        "slug": "azure-synapse-analytics",
        "sub_category_id": 117,
        "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": 381,
      "existing_alias_text": "Azure Blob Storage",
      "input_term": "Azure Blob Storage",
      "matched_canonical": {
        "category_id": 11,
        "display_name": "Azure Blob Storage",
        "id": 172,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "CLOUD_SERVICE",
        "slug": "azure-blob-storage",
        "sub_category_id": 120,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 67,
      "existing_alias_text": "Python",
      "input_term": "Python",
      "matched_canonical": {
        "category_id": 6,
        "display_name": "Python",
        "id": 5,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "LANGUAGE",
        "slug": "python",
        "sub_category_id": 96,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 271,
      "existing_alias_text": "SQL",
      "input_term": "SQL",
      "matched_canonical": {
        "category_id": 6,
        "display_name": "SQL",
        "id": 101,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "LANGUAGE",
        "slug": "sql",
        "sub_category_id": 97,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 272,
      "existing_alias_text": "Scala",
      "input_term": "Scala",
      "matched_canonical": {
        "category_id": 6,
        "display_name": "Scala",
        "id": 102,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "LANGUAGE",
        "slug": "scala",
        "sub_category_id": 96,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 863,
      "existing_alias_text": "Cosmos DB",
      "input_term": "Cosmos DB",
      "matched_canonical": {
        "category_id": 11,
        "display_name": "Cosmos DB",
        "id": 515,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "CLOUD_SERVICE",
        "slug": "cosmos-db",
        "sub_category_id": 55,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 2004,
      "existing_alias_text": "Apache Spark",
      "input_term": "Apache Spark",
      "matched_canonical": {
        "category_id": 5,
        "display_name": "Apache Spark",
        "id": 1350,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "FRAMEWORK",
        "slug": "apache-spark",
        "sub_category_id": 1021,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 2010,
      "existing_alias_text": "Hadoop",
      "input_term": "Hadoop",
      "matched_canonical": {
        "category_id": 5,
        "display_name": "Hadoop",
        "id": 1351,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "FRAMEWORK",
        "slug": "hadoop",
        "sub_category_id": 91,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 173,
      "existing_alias_text": "Kafka",
      "input_term": "Kafka",
      "matched_canonical": {
        "category_id": 3,
        "display_name": "Kafka",
        "id": 36,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "TOOL",
        "slug": "kafka",
        "sub_category_id": 3533,
        "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": 1613,
      "existing_alias_text": "Git",
      "input_term": "Git",
      "matched_canonical": {
        "category_id": 13,
        "display_name": "Git",
        "id": 1002,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "TOOL",
        "slug": "git",
        "sub_category_id": 730,
        "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",
        "id": 151,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "PLATFORM",
        "slug": "power-bi",
        "sub_category_id": 111,
        "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": 464,
      "existing_alias_text": "Azure ML",
      "input_term": "Azure Machine Learning",
      "matched_canonical": {
        "category_id": 9,
        "display_name": "Azure ML",
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        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "PLATFORM",
        "slug": "azure-ml",
        "sub_category_id": 175,
        "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": 868,
      "existing_alias_text": "Agile",
      "input_term": "Agile",
      "matched_canonical": {
        "category_id": 8,
        "display_name": "Agile",
        "id": 520,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "METHODOLOGY",
        "slug": "agile",
        "sub_category_id": 367,
        "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": 1851,
      "existing_alias_text": "Scrum",
      "input_term": "Scrum",
      "matched_canonical": {
        "category_id": 8,
        "display_name": "Scrum",
        "id": 1215,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "METHODOLOGY",
        "slug": "scrum",
        "sub_category_id": 921,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "matched_via": "alias"
    }
  ],
  "candidate_roles": [
    {
      "display_name": ".NET Backend Developer",
      "id": 83,
      "rationale": null,
      "role_archetype": "Engineering",
      "slug": "dotnet-backend-developer",
      "source": "db"
    },
    {
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      "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.",
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      "slug": "cybersecurity-engineer",
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    },
    {
      "display_name": "Data Engineer",
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      "rationale": null,
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      "slug": "data-engineer",
      "source": "db"
    },
    {
      "display_name": "DevOps Engineer",
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      "rationale": null,
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      "slug": "devops-engineer",
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    },
    {
      "display_name": "Fullstack Developer",
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      "slug": "full-stack-engineer",
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    {
      "display_name": "Go Backend Developer",
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      "role_archetype": "Engineering",
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      "source": "db"
    },
    {
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      "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": "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"
    },
    {
      "display_name": "Node.js Backend Developer",
      "id": 82,
      "rationale": null,
      "role_archetype": "Engineering",
      "slug": "node-backend-developer",
      "source": "db"
    },
    {
      "display_name": "Python Backend Developer",
      "id": 80,
      "rationale": null,
      "role_archetype": "Engineering",
      "slug": "python-backend-developer",
      "source": "db"
    },
    {
      "display_name": "Scala Backend Developer",
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      "rationale": null,
      "role_archetype": "Engineering",
      "slug": "scala-backend-developer",
      "source": "db"
    },
    {
      "display_name": "AI Engineer",
      "id": 13,
      "rationale": null,
      "role_archetype": null,
      "slug": "ai-engineer",
      "source": "db"
    },
    {
      "display_name": "Cloud Architect",
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      "rationale": null,
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      "slug": "cloud-architect",
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    },
    {
      "display_name": "Cloud Security Engineer",
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      "rationale": null,
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      "slug": "cloud-security-engineer",
      "source": "db"
    },
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      "rationale": null,
      "role_archetype": "Engineering",
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    },
    {
      "display_name": "AR/VR Engineer",
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      "rationale": null,
      "role_archetype": null,
      "slug": "ar-vr-engineer",
      "source": "db"
    },
    {
      "display_name": "Pega Developer",
      "id": 24,
      "rationale": null,
      "role_archetype": null,
      "slug": "pega-developer",
      "source": "db"
    },
    {
      "display_name": "PHP Backend Developer",
      "id": 86,
      "rationale": null,
      "role_archetype": "Engineering",
      "slug": "php-backend-developer",
      "source": "db"
    },
    {
      "display_name": "Ruby Backend Developer",
      "id": 85,
      "rationale": null,
      "role_archetype": "Engineering",
      "slug": "ruby-backend-developer",
      "source": "db"
    }
  ],
  "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 0.50 does not contradict",
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    "slug": "data-engineer",
    "source": "db"
  },
  "dimensions": [
    {
      "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"
      },
      "input_skill": "Azure",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": ".NET Backend Developer",
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          "rationale": null,
          "role_archetype": "Engineering",
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          "source": "db"
        },
        {
          "display_name": "Backend Developer",
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          "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": "Cyber Security Engineer",
          "id": 5,
          "rationale": null,
          "role_archetype": null,
          "slug": "cybersecurity-engineer",
          "source": "db"
        },
        {
          "display_name": "Data Engineer",
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          "rationale": null,
          "role_archetype": null,
          "slug": "data-engineer",
          "source": "db"
        },
        {
          "display_name": "DevOps Engineer",
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          "rationale": null,
          "role_archetype": null,
          "slug": "devops-engineer",
          "source": "db"
        },
        {
          "display_name": "Fullstack Developer",
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          "rationale": null,
          "role_archetype": null,
          "slug": "full-stack-engineer",
          "source": "db"
        },
        {
          "display_name": "Go Backend Developer",
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          "rationale": null,
          "role_archetype": "Engineering",
          "slug": "go-backend-developer",
          "source": "db"
        },
        {
          "display_name": "Java Backend Developer",
          "id": 79,
          "rationale": null,
          "role_archetype": "Engineering",
          "slug": "java-backend-developer",
          "source": "db"
        },
        {
          "display_name": "Kotlin Backend Developer",
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          "rationale": null,
          "role_archetype": "Engineering",
          "slug": "kotlin-server-backend-developer",
          "source": "db"
        },
        {
          "display_name": "ML Engineer",
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          "rationale": null,
          "role_archetype": null,
          "slug": "ml-engineer",
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        },
        {
          "display_name": "MLOps Engineer",
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          "rationale": null,
          "role_archetype": null,
          "slug": "ml-ops-engineer",
          "source": "db"
        },
        {
          "display_name": "Node.js Backend Developer",
          "id": 82,
          "rationale": null,
          "role_archetype": "Engineering",
          "slug": "node-backend-developer",
          "source": "db"
        },
        {
          "display_name": "Python Backend Developer",
          "id": 80,
          "rationale": null,
          "role_archetype": "Engineering",
          "slug": "python-backend-developer",
          "source": "db"
        },
        {
          "display_name": "Scala Backend Developer",
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          "rationale": null,
          "role_archetype": "Engineering",
          "slug": "scala-backend-developer",
          "source": "db"
        }
      ]
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Cloud Platforms \u0026 Managed Services",
        "id": 221,
        "rationale": "Operates and integrates vendor-specific cloud compute, storage, and hosting services.",
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        "source": "db"
      },
      "input_skill": "Azure",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": "Fullstack Developer",
          "id": 15,
          "rationale": null,
          "role_archetype": null,
          "slug": "full-stack-engineer",
          "source": "db"
        },
        {
          "display_name": "Go Backend Developer",
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          "rationale": null,
          "role_archetype": "Engineering",
          "slug": "go-backend-developer",
          "source": "db"
        },
        {
          "display_name": "Node.js Backend Developer",
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          "rationale": null,
          "role_archetype": "Engineering",
          "slug": "node-backend-developer",
          "source": "db"
        }
      ]
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Cloud Platforms for AI Deployment",
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        "rationale": "Major cloud services that provide infrastructure and managed services for AI workloads.",
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        "source": "db"
      },
      "input_skill": "Azure",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": "AI Engineer",
          "id": 13,
          "rationale": null,
          "role_archetype": null,
          "slug": "ai-engineer",
          "source": "db"
        }
      ]
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Cloud Provider Platforms",
        "id": 131,
        "rationale": "Major cloud platforms and their core service ecosystems used to design target-state architectures, choose deployment boundaries, and evaluate managed capabilities. This is the primary substrate for cloud architecture decisions.",
        "slug": "cloud-provider-platforms",
        "source": "db"
      },
      "input_skill": "Azure",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": "Cloud Architect",
          "id": 9,
          "rationale": null,
          "role_archetype": null,
          "slug": "cloud-architect",
          "source": "db"
        },
        {
          "display_name": "Cloud Security Engineer",
          "id": 23,
          "rationale": null,
          "role_archetype": null,
          "slug": "cloud-security-engineer",
          "source": "db"
        }
      ]
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Cloud Security Posture Tools",
        "id": 64,
        "rationale": "Cloud-native security platforms used to assess misconfiguration, workload exposure, and cloud control coverage. This dimension includes the major CNAPP/CSPM/CWPP vendors and cloud security services the role reviews and tunes.",
        "slug": "cloud-security-posture-tools",
        "source": "db"
      },
      "input_skill": "Azure",
      "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",
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          "rationale": null,
          "role_archetype": null,
          "slug": "cybersecurity-engineer",
          "source": "db"
        }
      ]
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Cloud Data Warehouses",
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        "rationale": "Managed analytical storage and compute platforms used for curated datasets, reporting, and downstream analytics. These systems are central to data modeling, performance tuning, and cost-aware query design.",
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        "source": "db"
      },
      "input_skill": "Azure Synapse Analytics",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": "Data Engineer",
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          "rationale": null,
          "role_archetype": null,
          "slug": "data-engineer",
          "source": "db"
        }
      ]
    },
    {
      "dimension": {
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        "display_name": "Cloud Storage and Data Services",
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        "rationale": "Cloud-native storage and managed data services used to place workloads, choose durability tiers, and define platform boundaries. This is a coherent cluster because architects evaluate storage fit, access patterns, and managed service tradeoffs.",
        "slug": "cloud-storage-and-data-services",
        "source": "db"
      },
      "input_skill": "Azure Blob Storage",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": "Cloud Architect",
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          "rationale": null,
          "role_archetype": null,
          "slug": "cloud-architect",
          "source": "db"
        }
      ]
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Cloud Storage and File Formats",
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        "rationale": "Object storage and data file formats used as the physical substrate for data movement and lake-style analytics. Data engineers need these to manage landing zones, partitioned datasets, and efficient interchange.",
        "slug": "cloud-storage-and-file-formats",
        "source": "db"
      },
      "input_skill": "Azure Blob Storage",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": "Data Engineer",
          "id": 2,
          "rationale": null,
          "role_archetype": null,
          "slug": "data-engineer",
          "source": "db"
        }
      ]
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Cloud 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",
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          "rationale": null,
          "role_archetype": "A Backend Engineer designs, builds, and maintains the server-side logic and data handling that power applications and services. They focus on implementing reliable business functionality, integrating with other systems, and ensuring the backend is scalable, maintainable, and observable.",
          "slug": "backend-engineer",
          "source": "db"
        },
        {
          "display_name": "Fullstack Developer",
          "id": 435,
          "rationale": null,
          "role_archetype": "Engineering",
          "slug": "fullstack-developer",
          "source": "db"
        },
        {
          "display_name": "Fullstack Developer",
          "id": 15,
          "rationale": null,
          "role_archetype": null,
          "slug": "full-stack-engineer",
          "source": "db"
        }
      ]
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Programming Languages 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",
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          "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",
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          "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",
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          "rationale": null,
          "role_archetype": null,
          "slug": "ml-engineer",
          "source": "db"
        },
        {
          "display_name": "MLOps Engineer",
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          "rationale": null,
          "role_archetype": null,
          "slug": "ml-ops-engineer",
          "source": "db"
        }
      ]
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Programming Languages for XR",
        "id": 97,
        "rationale": "Primary implementation languages used to build immersive client features, interaction logic, and device-specific runtime behavior. This is the core coding surface for AR/VR experiences.",
        "slug": "programming-languages-for-xr",
        "source": "db"
      },
      "input_skill": "Python",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": "AR/VR Engineer",
          "id": 8,
          "rationale": null,
          "role_archetype": null,
          "slug": "ar-vr-engineer",
          "source": "db"
        }
      ]
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Python Programming",
        "id": 290,
        "rationale": "Core Python language skills used to implement backend business logic, request handlers, integrations, and service internals. This is the primary coding surface for the role.",
        "slug": "python-programming",
        "source": "db"
      },
      "input_skill": "Python",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": "Python Backend Developer",
          "id": 80,
          "rationale": null,
          "role_archetype": "Engineering",
          "slug": "python-backend-developer",
          "source": "db"
        }
      ]
    },
    {
      "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": "SQL",
      "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 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": "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": "Scala",
      "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": "Scala",
      "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": "NoSQL Databases",
        "id": 19,
        "rationale": "Models and manages data using non-relational database systems.",
        "slug": "nosql-databases",
        "source": "db"
      },
      "input_skill": "Cosmos DB",
      "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": "ETL and ELT Tooling",
        "id": 24,
        "rationale": "Packaged tools for extracting, loading, and transforming data across systems. This dimension covers connector-based ingestion, transformation frameworks, and managed integration products.",
        "slug": "etl-and-elt-tooling",
        "source": "db"
      },
      "input_skill": "Apache Spark",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": "Data Engineer",
          "id": 2,
          "rationale": null,
          "role_archetype": null,
          "slug": "data-engineer",
          "source": "db"
        }
      ]
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "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": "Asynchronous Messaging and Event Streaming",
        "id": 297,
        "rationale": "Asynchronous communication patterns and broker technologies used to decouple backend services and move work off the request path. Includes queues, pub/sub, event streams, consumer groups, dead-letter queues, and delivery semantics across systems such as Kafka, RabbitMQ, NATS, SQS/SNS, Pulsar, and ActiveMQ.",
        "slug": "asynchronous-messaging-and-event-streaming",
        "source": "db"
      },
      "input_skill": "Kafka",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": ".NET Backend Developer",
          "id": 83,
          "rationale": null,
          "role_archetype": "Engineering",
          "slug": "dotnet-backend-developer",
          "source": "db"
        },
        {
          "display_name": "Go Backend Developer",
          "id": 81,
          "rationale": null,
          "role_archetype": "Engineering",
          "slug": "go-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": "Node.js Backend Developer",
          "id": 82,
          "rationale": null,
          "role_archetype": "Engineering",
          "slug": "node-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": "Messaging and Background Jobs",
        "id": 291,
        "rationale": "Asynchronous processing patterns and worker systems used to decouple backend work from request handling. This is a coherent cluster because the role supports background jobs, retries, and deferred processing.",
        "slug": "messaging-and-background-jobs",
        "source": "db"
      },
      "input_skill": "Kafka",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": "PHP Backend Developer",
          "id": 86,
          "rationale": null,
          "role_archetype": "Engineering",
          "slug": "php-backend-developer",
          "source": "db"
        },
        {
          "display_name": "Python Backend Developer",
          "id": 80,
          "rationale": null,
          "role_archetype": "Engineering",
          "slug": "python-backend-developer",
          "source": "db"
        },
        {
          "display_name": "Ruby Backend Developer",
          "id": 85,
          "rationale": null,
          "role_archetype": "Engineering",
          "slug": "ruby-backend-developer",
          "source": "db"
        }
      ]
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Messaging and Event Streaming",
        "id": 8,
        "rationale": "Transport-layer systems used to move events and decouple producers from consumers. Data engineers use these systems to ingest, buffer, and distribute event data before downstream processing.",
        "slug": "messaging-and-event-streaming",
        "source": "db"
      },
      "input_skill": "Kafka",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": "Backend Developer",
          "id": 1,
          "rationale": null,
          "role_archetype": "A Backend Engineer designs, builds, and maintains the server-side logic and data handling that power applications and services. They focus on implementing reliable business functionality, integrating with other systems, and ensuring the backend is scalable, maintainable, and observable.",
          "slug": "backend-engineer",
          "source": "db"
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        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "TOOL",
        "slug": "git",
        "sub_category_id": 730,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "dimensions": [
        {
          "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": "Git",
          "llm_role": null,
          "roles_from_db": []
        }
      ],
      "input_skill": "Git",
      "matched_via": "alias",
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": null,
      "source_tag": "db",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [
        {
          "alias_text": "Power BI",
          "alias_type": "CANONICAL",
          "id": 360,
          "is_primary": true,
          "match_strategy": "CASE_INSENSITIVE"
        }
      ],
      "canonical": {
        "category_id": 9,
        "display_name": "Power BI",
        "id": 151,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "PLATFORM",
        "slug": "power-bi",
        "sub_category_id": 111,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "dimensions": [
        {
          "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"
            }
          ]
        }
      ],
      "input_skill": "Power BI",
      "matched_via": "alias",
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": null,
      "source_tag": "db",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [
        {
          "alias_text": "Azure ML",
          "alias_type": "CANONICAL",
          "id": 464,
          "is_primary": true,
          "match_strategy": "CASE_INSENSITIVE"
        }
      ],
      "canonical": {
        "category_id": 9,
        "display_name": "Azure ML",
        "id": 212,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "PLATFORM",
        "slug": "azure-ml",
        "sub_category_id": 175,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "dimensions": [
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "MLOps Platforms and Lifecycle",
            "id": 43,
            "rationale": "End-to-end managed platforms used to train, deploy, register, and govern models across their lifecycle. This is the operational control plane for production ML workflows.",
            "slug": "mlops-platforms-and-lifecycle",
            "source": "db"
          },
          "input_skill": "Azure Machine Learning",
          "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"
            }
          ]
        }
      ],
      "input_skill": "Azure Machine Learning",
      "matched_via": "embedding_alias",
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": null,
      "source_tag": "db",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [
        {
          "alias_text": "Agile",
          "alias_type": "CANONICAL",
          "id": 868,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        }
      ],
      "canonical": {
        "category_id": 8,
        "display_name": "Agile",
        "id": 520,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "METHODOLOGY",
        "slug": "agile",
        "sub_category_id": 367,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "dimensions": [
        {
          "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": []
        }
      ],
      "input_skill": "Agile",
      "matched_via": "alias",
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": null,
      "source_tag": "db",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [
        {
          "alias_text": "Scrum",
          "alias_type": "CANONICAL",
          "id": 1851,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        }
      ],
      "canonical": {
        "category_id": 8,
        "display_name": "Scrum",
        "id": 1215,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "METHODOLOGY",
        "slug": "scrum",
        "sub_category_id": 921,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "dimensions": [
        {
          "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": "Scrum",
          "llm_role": null,
          "roles_from_db": []
        }
      ],
      "input_skill": "Scrum",
      "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": [
    "Azure Data Factory",
    "Azure Databricks",
    "Azure SQL Database",
    "Azure Data Lake",
    "ETL",
    "SQL Server Integration Services"
  ]
}
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 0.50 does not contradict",
    "role_archetype": null,
    "slug": "data-engineer",
    "source": "db"
  },
  "chosen_role_resolution": "in_db",
  "final_input_skills": [
    {
      "skill": "Azure",
      "tag": "in_db"
    },
    {
      "skill": "Azure Data Factory",
      "tag": "new"
    },
    {
      "skill": "Azure Databricks",
      "tag": "new"
    },
    {
      "skill": "Azure Synapse Analytics",
      "tag": "in_db"
    },
    {
      "skill": "Azure SQL Database",
      "tag": "new"
    },
    {
      "skill": "Azure Blob Storage",
      "tag": "in_db"
    },
    {
      "skill": "Azure Data Lake",
      "tag": "new"
    },
    {
      "skill": "Python",
      "tag": "in_db"
    },
    {
      "skill": "SQL",
      "tag": "in_db"
    },
    {
      "skill": "Scala",
      "tag": "in_db"
    },
    {
      "skill": "ETL",
      "tag": "new"
    },
    {
      "skill": "SQL Server Integration Services",
      "tag": "new"
    },
    {
      "skill": "Cosmos DB",
      "tag": "in_db"
    },
    {
      "skill": "Apache Spark",
      "tag": "in_db"
    },
    {
      "skill": "Hadoop",
      "tag": "in_db"
    },
    {
      "skill": "Kafka",
      "tag": "in_db"
    },
    {
      "skill": "Git",
      "tag": "in_db"
    },
    {
      "skill": "Power BI",
      "tag": "in_db"
    },
    {
      "skill": "Azure Machine Learning",
      "tag": "in_db"
    },
    {
      "skill": "Agile",
      "tag": "in_db"
    },
    {
      "skill": "Scrum",
      "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": "Azure",
        "llm_role": null,
        "matched_chosen_role": true,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension saved",
        "role_dimension_saved": true,
        "roles_from_db": [
          {
            "display_name": ".NET Backend Developer",
            "id": 83,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "dotnet-backend-developer",
            "source": "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": "Cyber Security Engineer",
            "id": 5,
            "rationale": null,
            "role_archetype": null,
            "slug": "cybersecurity-engineer",
            "source": "db"
          },
          {
            "display_name": "Data Engineer",
            "id": 2,
            "rationale": null,
            "role_archetype": null,
            "slug": "data-engineer",
            "source": "db"
          },
          {
            "display_name": "DevOps Engineer",
            "id": 10,
            "rationale": null,
            "role_archetype": null,
            "slug": "devops-engineer",
            "source": "db"
          },
          {
            "display_name": "Fullstack Developer",
            "id": 15,
            "rationale": null,
            "role_archetype": null,
            "slug": "full-stack-engineer",
            "source": "db"
          },
          {
            "display_name": "Go Backend Developer",
            "id": 81,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "go-backend-developer",
            "source": "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": "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"
          },
          {
            "display_name": "Node.js Backend Developer",
            "id": 82,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "node-backend-developer",
            "source": "db"
          },
          {
            "display_name": "Python Backend Developer",
            "id": 80,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "python-backend-developer",
            "source": "db"
          },
          {
            "display_name": "Scala Backend Developer",
            "id": 87,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "scala-backend-developer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 188,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 2,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Cloud Platforms \u0026 Managed Services",
          "id": 221,
          "rationale": "Operates and integrates vendor-specific cloud compute, storage, and hosting services.",
          "slug": "cloud-platforms-managed-services",
          "source": "db"
        },
        "dimension_id": 221,
        "input_skill": "Azure",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "Fullstack Developer",
            "id": 15,
            "rationale": null,
            "role_archetype": null,
            "slug": "full-stack-engineer",
            "source": "db"
          },
          {
            "display_name": "Go Backend Developer",
            "id": 81,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "go-backend-developer",
            "source": "db"
          },
          {
            "display_name": "Node.js Backend Developer",
            "id": 82,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "node-backend-developer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 188,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 2,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Cloud Platforms for AI Deployment",
          "id": 211,
          "rationale": "Major cloud services that provide infrastructure and managed services for AI workloads.",
          "slug": "cloud-platforms-for-ai-deployment",
          "source": "db"
        },
        "dimension_id": 211,
        "input_skill": "Azure",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "AI Engineer",
            "id": 13,
            "rationale": null,
            "role_archetype": null,
            "slug": "ai-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 188,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 2,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Cloud Provider Platforms",
          "id": 131,
          "rationale": "Major cloud platforms and their core service ecosystems used to design target-state architectures, choose deployment boundaries, and evaluate managed capabilities. This is the primary substrate for cloud architecture decisions.",
          "slug": "cloud-provider-platforms",
          "source": "db"
        },
        "dimension_id": 131,
        "input_skill": "Azure",
        "llm_role": null,
        "matched_chosen_role": 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",
            "id": 9,
            "rationale": null,
            "role_archetype": null,
            "slug": "cloud-architect",
            "source": "db"
          },
          {
            "display_name": "Cloud Security Engineer",
            "id": 23,
            "rationale": null,
            "role_archetype": null,
            "slug": "cloud-security-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 188,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 2,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Cloud Security Posture Tools",
          "id": 64,
          "rationale": "Cloud-native security platforms used to assess misconfiguration, workload exposure, and cloud control coverage. This dimension includes the major CNAPP/CSPM/CWPP vendors and cloud security services the role reviews and tunes.",
          "slug": "cloud-security-posture-tools",
          "source": "db"
        },
        "dimension_id": 64,
        "input_skill": "Azure",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "Cloud Security Engineer",
            "id": 23,
            "rationale": null,
            "role_archetype": null,
            "slug": "cloud-security-engineer",
            "source": "db"
          },
          {
            "display_name": "Cyber Security Engineer",
            "id": 5,
            "rationale": null,
            "role_archetype": null,
            "slug": "cybersecurity-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 188,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 2,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Cloud Data Warehouses",
          "id": 22,
          "rationale": "Managed analytical storage and compute platforms used for curated datasets, reporting, and downstream analytics. These systems are central to data modeling, performance tuning, and cost-aware query design.",
          "slug": "cloud-data-warehouses",
          "source": "db"
        },
        "dimension_id": 22,
        "input_skill": "Azure Synapse Analytics",
        "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": 108,
        "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": "Azure Blob Storage",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "Cloud Architect",
            "id": 9,
            "rationale": null,
            "role_archetype": null,
            "slug": "cloud-architect",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 172,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 2,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Cloud Storage and File Formats",
          "id": 35,
          "rationale": "Object storage and data file formats used as the physical substrate for data movement and lake-style analytics. Data engineers need these to manage landing zones, partitioned datasets, and efficient interchange.",
          "slug": "cloud-storage-and-file-formats",
          "source": "db"
        },
        "dimension_id": 35,
        "input_skill": "Azure Blob Storage",
        "llm_role": null,
        "matched_chosen_role": true,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension saved",
        "role_dimension_saved": true,
        "roles_from_db": [
          {
            "display_name": "Data Engineer",
            "id": 2,
            "rationale": null,
            "role_archetype": null,
            "slug": "data-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 172,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 2,
        "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"
        },
        "dimension_id": 248,
        "input_skill": "Python",
        "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"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 5,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 2,
        "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"
        },
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        "input_skill": "Python",
        "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",
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            "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.",
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          },
          {
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            "rationale": null,
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            "slug": "fullstack-developer",
            "source": "db"
          },
          {
            "display_name": "Fullstack Developer",
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            "rationale": null,
            "role_archetype": null,
            "slug": "full-stack-engineer",
            "source": "db"
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        ],
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        "skill_id": 5,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 2,
        "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"
        },
        "dimension_id": 59,
        "input_skill": "Python",
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        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "Cyber Security Engineer",
            "id": 5,
            "rationale": null,
            "role_archetype": null,
            "slug": "cybersecurity-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 5,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 2,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Programming Languages for Data Work",
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          "rationale": "Languages used to implement data pipelines, transformations, and operational glue. This is the primary coding surface for building ingestion, enrichment, and automation logic in data engineering.",
          "slug": "programming-languages-for-data-work",
          "source": "db"
        },
        "dimension_id": 21,
        "input_skill": "Python",
        "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"
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        ],
        "skill_dimension_saved": true,
        "skill_id": 5,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 2,
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          "difficulty_hint": "well_known",
          "display_name": "Programming Languages for ML Systems",
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          "rationale": "Languages used to build training code, inference services, evaluation jobs, and ML glue code. This is the primary implementation surface for ML engineers across experimentation and productionization.",
          "slug": "programming-languages-for-ml-systems",
          "source": "db"
        },
        "dimension_id": 39,
        "input_skill": "Python",
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        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "ML Engineer",
            "id": 3,
            "rationale": null,
            "role_archetype": null,
            "slug": "ml-engineer",
            "source": "db"
          },
          {
            "display_name": "MLOps Engineer",
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            "rationale": null,
            "role_archetype": null,
            "slug": "ml-ops-engineer",
            "source": "db"
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        ],
        "skill_dimension_saved": true,
        "skill_id": 5,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 2,
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          "difficulty_hint": "well_known",
          "display_name": "Programming Languages for XR",
          "id": 97,
          "rationale": "Primary implementation languages used to build immersive client features, interaction logic, and device-specific runtime behavior. This is the core coding surface for AR/VR experiences.",
          "slug": "programming-languages-for-xr",
          "source": "db"
        },
        "dimension_id": 97,
        "input_skill": "Python",
        "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": "AR/VR Engineer",
            "id": 8,
            "rationale": null,
            "role_archetype": null,
            "slug": "ar-vr-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 5,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 2,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Python Programming",
          "id": 290,
          "rationale": "Core Python language skills used to implement backend business logic, request handlers, integrations, and service internals. This is the primary coding surface for the role.",
          "slug": "python-programming",
          "source": "db"
        },
        "dimension_id": 290,
        "input_skill": "Python",
        "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": "Python Backend Developer",
            "id": 80,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "python-backend-developer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 5,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 2,
        "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"
        },
        "dimension_id": 267,
        "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": "Pega Developer",
            "id": 24,
            "rationale": null,
            "role_archetype": null,
            "slug": "pega-developer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 101,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
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        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Programming Languages for Data Work",
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          "rationale": "Languages used to implement data pipelines, transformations, and operational glue. This is the primary coding surface for building ingestion, enrichment, and automation logic in data engineering.",
          "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",
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          }
        ],
        "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": "Scala",
        "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": 102,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 2,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Programming Languages for ML Systems",
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          "rationale": "Languages used to build training code, inference services, evaluation jobs, and ML glue code. This is the primary implementation surface for ML engineers across experimentation and productionization.",
          "slug": "programming-languages-for-ml-systems",
          "source": "db"
        },
        "dimension_id": 39,
        "input_skill": "Scala",
        "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",
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            "rationale": null,
            "role_archetype": null,
            "slug": "ml-ops-engineer",
            "source": "db"
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        ],
        "skill_dimension_saved": true,
        "skill_id": 102,
        "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": "Cosmos DB",
        "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",
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            "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": 515,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 2,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "ETL and ELT Tooling",
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          "rationale": "Packaged tools for extracting, loading, and transforming data across systems. This dimension covers connector-based ingestion, transformation frameworks, and managed integration products.",
          "slug": "etl-and-elt-tooling",
          "source": "db"
        },
        "dimension_id": 24,
        "input_skill": "Apache 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,
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        "skipped_reason": null
      },
      {
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        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "ETL and ELT Tooling",
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          "rationale": "Packaged tools for extracting, loading, and transforming data across systems. This dimension covers connector-based ingestion, transformation frameworks, and managed integration products.",
          "slug": "etl-and-elt-tooling",
          "source": "db"
        },
        "dimension_id": 24,
        "input_skill": "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",
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            "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": "Asynchronous Messaging and Event Streaming",
          "id": 297,
          "rationale": "Asynchronous communication patterns and broker technologies used to decouple backend services and move work off the request path. Includes queues, pub/sub, event streams, consumer groups, dead-letter queues, and delivery semantics across systems such as Kafka, RabbitMQ, NATS, SQS/SNS, Pulsar, and ActiveMQ.",
          "slug": "asynchronous-messaging-and-event-streaming",
          "source": "db"
        },
        "dimension_id": 297,
        "input_skill": "Kafka",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": ".NET Backend Developer",
            "id": 83,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "dotnet-backend-developer",
            "source": "db"
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          {
            "display_name": "Go Backend Developer",
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            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "go-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": "Node.js Backend Developer",
            "id": 82,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "node-backend-developer",
            "source": "db"
          },
          {
            "display_name": "Scala Backend Developer",
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            "rationale": null,
            "role_archetype": "Engineering",
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            "source": "db"
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        ],
        "skill_dimension_saved": true,
        "skill_id": 36,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 2,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Messaging and Background Jobs",
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          "rationale": "Asynchronous processing patterns and worker systems used to decouple backend work from request handling. This is a coherent cluster because the role supports background jobs, retries, and deferred processing.",
          "slug": "messaging-and-background-jobs",
          "source": "db"
        },
        "dimension_id": 291,
        "input_skill": "Kafka",
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        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "PHP Backend Developer",
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            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "php-backend-developer",
            "source": "db"
          },
          {
            "display_name": "Python Backend Developer",
            "id": 80,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "python-backend-developer",
            "source": "db"
          },
          {
            "display_name": "Ruby Backend Developer",
            "id": 85,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "ruby-backend-developer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 36,
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        "skipped_reason": null
      },
      {
        "chosen_role_id": 2,
        "dimension": {
          "difficulty_hint": "well_known",
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          "slug": "messaging-and-event-streaming",
          "source": "db"
        },
        "dimension_id": 8,
        "input_skill": "Kafka",
        "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": "Backend Developer",
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            "rationale": null,
            "role_archetype": "A Backend Engineer designs, builds, and maintains the server-side logic and data handling that power applications and services. They focus on implementing reliable business functionality, integrating with other systems, and ensuring the backend is scalable, maintainable, and observable.",
            "slug": "backend-engineer",
            "source": "db"
          },
          {
            "display_name": "Data Engineer",
            "id": 2,
            "rationale": null,
            "role_archetype": null,
            "slug": "data-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 36,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 2,
        "dimension": {
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          "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": "Git",
        "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": 1002,
        "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.",
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          "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",
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            "rationale": null,
            "role_archetype": null,
            "slug": "data-engineer",
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          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 151,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
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        },
        "dimension_id": 43,
        "input_skill": "Azure Machine Learning",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Skipped \u2014 no persistable v3 meta for new skill",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "ML Engineer",
            "id": 3,
            "rationale": null,
            "role_archetype": null,
            "slug": "ml-engineer",
            "source": "db"
          },
          {
            "display_name": "MLOps Engineer",
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            "rationale": null,
            "role_archetype": null,
            "slug": "ml-ops-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": "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": "Agile",
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        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [],
        "skill_dimension_saved": true,
        "skill_id": 520,
        "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": "Scrum",
        "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": 1215,
        "skill_tag": "in_db",
        "skipped_reason": null
      }
    ],
    "new_skills_created": 0,
    "role_dimension_saved": 0,
    "skill_dimension_saved": 0,
    "skipped": 1
  },
  "planner_output": null,
  "run_id": "857a64e4-51b1-4a9c-b147-9672375dc355"
}

LLM Calls

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

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