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Pipeline run

4b36e8e4-28d7-4c2a-ac67-6dbc4b17a937

Pipeline LLM cost (USD)
API 1: $0.0042 API 2: $0.0004 API 3: $0.0000 Total: $0.0046

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
Lead SSIS/SQL/ADF ETL work: design, build, tune, and deploy enterprise data pipelines plus data lakes/marts/warehouse models, while coordinating delivery, mentoring engineers, and enforcing data quality/governance.
"Lead the design, development, and deployment of robust data pipelines and ETL processes using SSIS, SQL Server, and Azure Data Factory (ADF)."
Tech stack maturity
Mainstream Legacy
SQL and star-schema are classic data engineering skills most associated with established warehouse-centric architectures rather than cloud-native or bleeding-edge stacks.
AI index (0 = no AI use, 5 = totally AI-dependent · v2.1)
0.00 / 5
· Title match
· Has AI skill
· AI skill (primary)
· AI skill (secondary)
· On AI team
· Builds AI products
vocab breakdown (legacy)
Assistants (×1):
Frameworks (×2):
Models / concepts (×3):
Evidence — skills matched in JD (21)
SSIS SQL Azure Data Factory Microsoft SQL Server T-SQL ETL Data Warehouse Data Mart Star Schema Snowflake Schema DDL DML Relational Database Design Query Performance Tuning Lakehouse Azure AWS Snowflake Data Lake REST JSON
Skill cluster (7 dimension groups, role-scoped)
Cloud Platforms
Azure AWS
API Interface and Contract Design
JSON
Cloud Data Warehouses
Snowflake
Data Modeling and Schema Design
Star Schema
Integration Protocols & Standards
REST
Programming Languages for Data Work
SQL
Cross-cutting / unaligned
SSIS Azure Data Factory Microsoft SQL Server T-SQL ETL Data Warehouse Data Mart Snowflake Schema DDL DML Relational Database Design Query Performance Tuning Lakehouse Data Lake
Show KRA description ↓
We are looking for a highly motivated and experienced Lead Data Engineer with deep expertise in SSIS, SQL, and a strong ability to drive and lead data projects. The ideal candidate should have moderate experience with Azure Data Factory (ADF) and a passion for designing scalable, high-performance data solutions that support critical enterprise operations and analytics. You will play a lead role in shaping our data infrastructure and delivering high-quality solutions in collaboration with cross-functional teams. • Lead the design, development, and deployment of robust data pipelines and ETL processes using SSIS, SQL Server, and Azure Data Factory (ADF). • Drive end-to-end project execution — including planning, technical architecture, implementation, testing, and delivery. • Develop and maintain data lakes, data marts, and data warehouse solutions, supporting both structured and semi-structured data. • Ensure performance tuning and optimization of SQL queries, stored procedures, and ETL jobs. • Collaborate with business stakeholders and technical teams to gather data requirements and translate them into scalable solutions. • Mentor junior engineers and conduct code reviews to ensure adherence to best practices. • Contribute to architectural decisions involving Lakehouse, cloud adoption, and data integration strategies. • Implement and maintain data quality, governance, and security standards. • Maintain accurate project documentation and communicate effectively with technical and non-technical audiences. Mandatory Technical Skills: • Advanced proficiency in Microsoft SQL Server and T-SQL (stored procedures, views, indexing, triggers) • Expert-level experience with SSIS for ETL development • Hands-on experience in data warehouse and data mart modeling (Star/Snowflake schemas) • Strong understanding of DDL/DML operations and relational database design • Solid background in query performance tuning and optimization Moderate Experience Required • Azure Data Factory (ADF) for data pipeline orchestration and cloud-based ETL Preferred / Nice To Have • Exposure to Lakehouse architecture • Experience with Cloud platforms (Azure, AWS, or Snowflake) • Familiarity with Data Lakes and handling large volumes of data • Working knowledge of REST APIs, JSON, and integration patterns.

Signals

Skill data-engineer
0.14
Alias data-engineer
1.00
KRA data-engineer
0.66

Post-classification

Centroidupdated · n=400
Alias collision log
New-role queue
New skills captured13
New KRA captured

Captured for admin review

SSIS primary Data Engineer pending
Azure Data Factory primary Data Engineer pending
Microsoft SQL Server primary Data Engineer pending
T-SQL primary Data Engineer pending
ETL primary Data Engineer pending
Data Warehouse primary Data Engineer pending
Data Mart primary Data Engineer pending
Snowflake Schema primary Data Engineer pending
DDL primary Data Engineer pending
DML primary Data Engineer pending
Relational Database Design primary Data Engineer pending
Query Performance Tuning primary Data Engineer pending
Data Lake Data Engineer pending
Status: completed Created: 2026-05-27T16:08:13.218841Z Updated: 2026-05-27T16:10:25.372927Z API 3 duration: 61828 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 0.14 does not contradict

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

0
New skills
0
Skill↔dim saved
0
Role↔dim saved
5
Skipped

Job description

Role: Lead Data Engineer

Location: Kochi / Trivandrum

Experience: 6+years

Manadatory skills : MS SQL , SSIS ,ADF ,DataLake

Strong in SQL ,SSIS ,Database

should have excellent communication

Salary- 18 to 23 lpa

Job Purpose

We are looking for a highly motivated and experienced Lead Data Engineer with deep expertise in SSIS, SQL, and a

strong ability to drive and lead data projects. The ideal candidate should have moderate experience with Azure

Data Factory (ADF) and a passion for designing scalable, high-performance data solutions that support critical

enterprise operations and analytics.

You will play a lead role in shaping our data infrastructure and delivering high-quality solutions in collaboration

with cross-functional teams.

Key Responsibilities

• Lead the design, development, and deployment of robust data pipelines and ETL processes using SSIS, SQL


Server, and Azure Data Factory (ADF).

• Drive end-to-end project execution — including planning, technical architecture, implementation, testing,


and delivery.

• Develop and maintain data lakes, data marts, and data warehouse solutions, supporting both structured


and semi-structured data.

• Ensure performance tuning and optimization of SQL queries, stored procedures, and ETL jobs.
• Collaborate with business stakeholders and technical teams to gather data requirements and translate


them into scalable solutions.

• Mentor junior engineers and conduct code reviews to ensure adherence to best practices.
• Contribute to architectural decisions involving Lakehouse, cloud adoption, and data integration strategies.
• Implement and maintain data quality, governance, and security standards.
• Maintain accurate project documentation and communicate effectively with technical and non-technical


audiences.

Skills And Competencies

Mandatory Technical Skills:

• Advanced proficiency in Microsoft SQL Server and T-SQL (stored procedures, views, indexing, triggers)
• Expert-level experience with SSIS for ETL development
• Hands-on experience in data warehouse and data mart modeling (Star/Snowflake schemas)
• Strong understanding of DDL/DML operations and relational database design
• Solid background in query performance tuning and optimization


Moderate Experience Required

• Azure Data Factory (ADF) for data pipeline orchestration and cloud-based ETL


Preferred / Nice To Have

• Exposure to Lakehouse architecture
• Experience with Cloud platforms (Azure, AWS, or Snowflake)
• Familiarity with Data Lakes and handling large volumes of data
• Working knowledge of REST APIs, JSON, and integration patterns


To adhere to ISMS policies and procedures.

Skills from this JD

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

SSIS Primary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

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

Derived legacy fields
Category
Data Engineering Tools
Sub-category
general
Skill nature
TOOL
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
SQL Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: SQL id=101 · sql

Aliases — catalog

  • SQL (CANONICAL) primary

Context tags (catalog)

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

Stored enrichment (catalog DB)

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

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

Skill profile (library / DB)

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

Dimensions (API 2 worklist)

  • Pega Programming Languages & DSLs Catalog dimension db id 267

    Library dimension (catalog)

    Roles linked in library: Pega Developer

  • Programming Languages & DSLs Catalog dimension db id 475

    Library dimension (catalog)

    Roles linked in library: Engineering Manager

  • Programming Languages for Data Work Catalog dimension db id 21

    Library dimension (catalog)

    Roles linked in library: Data Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Pega Programming Languages & DSLs
pega-programming-languages-dsls
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Programming Languages & DSLs
programming-languages-dsls
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Programming Languages for Data Work
programming-languages-for-data-work
Existing dimension (library) · Role↔dimension saved
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
Data Integration
Skill nature
PLATFORM
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Microsoft SQL Server Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: SQL Server id=18 · sql-server

Aliases — catalog

  • SQL Server (CANONICAL) primary
  • SQL Server 2000 (VERSION)
  • SQL Server 2005 (VERSION)
  • SQL Server 2008 (VERSION)
  • SQL Server 2012 (VERSION)
  • SQL Server 2014 (VERSION)
  • SQL Server 2016 (VERSION)
  • SQL Server 2017 (VERSION)
  • SQL Server 2019 (VERSION)
  • SQL Server 2022 (VERSION)
  • SQL Server 6.5 (VERSION)
  • SQL Server 7.0 (VERSION)

Context tags (catalog)

Always On CLR Integration Clustered Index ETL Execution Plan Linked Servers Query Store Replication SQL Agent SQL Server Agent SQL Server Integration Services SQL Server Management Studio SQL Server Reporting Services SSIS SSMS SSRS Stored Procedures T-SQL TempDB backup and recovery backup and restore clustering data migration data warehousing database design database normalization indexing performance tuning query optimization replication stored procedures transaction log transaction logs

Stored enrichment (catalog DB)

Category
Datastore
Sub-category
Relational Database
Vendor
Microsoft
License
proprietary
Year introduced
1989
Confidence
0.99
Version strategy
NOT_APPLICABLE

Maturity reasoning: SQL Server appears in many enterprise job descriptions and remains a major Microsoft-supported RDBMS with active Azure SQL/SQL Server demand; it is a common hiring-pipeline staple, not a sunset technology.

Skill profile (library / DB)

Skill nature
TOOL
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
3
Sub-category id
29
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Relational Database Design Catalog dimension db id 4

    Library dimension (catalog)

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

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Relational Database Design
relational-database-design
Skipped — no persistable v3 meta for new skill
skill_not_in_db_v3_proposed
T-SQL 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
Programming Languages
Sub-category
general
Skill nature
LANGUAGE
Volatility
STABLE
Typical lifespan
EVERGREEN
Version strategy
UNVERSIONED
ETL Primary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

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

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

Skill enrichment (orchestrator / LLM)

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

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

Skill enrichment (orchestrator / LLM)

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

Derived legacy fields
Category
Data Engineering Tools
Sub-category
general
Skill nature
CONCEPT
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Star Schema Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Star schema id=126 · star-schema

Aliases — catalog

  • Star schema (CANONICAL) primary

Context tags (catalog)

ETL Kimball OLAP aggregate table business intelligence conformed dimensions data mart denormalization dimension table fact table grain slowly changing dimension snowflake schema star join surrogate key

Stored enrichment (catalog DB)

Category
Architecture
Sub-category
Data Warehouse Architecture
Confidence
0.95
Version strategy
NOT_APPLICABLE

Maturity reasoning: Common data-warehouse pattern in BI/analytics JDs and vendor docs; widely used alongside Snowflake/BigQuery/Redshift for dimensional modeling, with no sunset or replacement signal.

Skill profile (library / DB)

Skill nature
PATTERN
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
1
Sub-category id
70
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Data Modeling and Schema Design Catalog dimension db id 26

    Library dimension (catalog)

    Roles linked in library: Data Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Data Modeling and Schema Design
data-modeling-and-schema-design
Existing dimension (library) · Role↔dimension saved
Snowflake Schema Primary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

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

Derived legacy fields
Category
Data Engineering Tools
Sub-category
general
Skill nature
CONCEPT
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
DDL 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
Programming Languages
Sub-category
general
Skill nature
CONCEPT
Volatility
STABLE
Typical lifespan
EVERGREEN
Version strategy
UNVERSIONED
DML 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
Programming Languages
Sub-category
general
Skill nature
CONCEPT
Volatility
STABLE
Typical lifespan
EVERGREEN
Version strategy
UNVERSIONED
Relational Database Design Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Relational Databases id=1345 · relational-databases

Aliases — catalog

  • Relational Databases (CANONICAL)

Context tags (catalog)

ACID Data Modeling Database Migration Entity-Relationship Indexes Joins MySQL Normalization Oracle PostgreSQL Query Optimization SQL Schema Design Stored Procedures Transactions

Stored enrichment (catalog DB)

Category
Domain
Sub-category
Relational Database Management
Confidence
0.90
Version strategy
NOT_APPLICABLE

Maturity reasoning: Relational databases remain a hiring staple across most backend/data JDs, with PostgreSQL, MySQL, and SQL Server appearing routinely; cloud vendors also center managed RDBMS offerings, signaling broad adoption.

Skill profile (library / DB)

Skill nature
CONCEPT
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
37
Sub-category id
1018
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
Skipped — no persistable v3 meta for new skill
skill_not_in_db_v3_proposed
Query Performance Tuning Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: query tuning id=3553 · query-tuning

Aliases — catalog

  • query tuning (CANONICAL) primary
  • Query Tuning (CANONICAL)

Context tags (catalog)

SQL SQL optimization caching cost estimation cost-based optimization data retrieval database indexing database optimization database performance database profiling database statistics database tuning execution plan execution time indexing join strategies load balancing parameter sniffing query analysis query complexity query execution query performance query profiling query rewriting resource allocation resource utilization statistics

Stored enrichment (catalog DB)

Category
Concept
Sub-category
Query Optimization
Confidence
0.82
Version strategy
NOT_APPLICABLE

Maturity reasoning: Common in DB/analytics job descriptions and vendor docs; PostgreSQL, MySQL, SQL Server, and Oracle all expose EXPLAIN/ANALYZE and tuning guides, showing broad hiring-pipeline demand.

Skill profile (library / DB)

Skill nature
CONCEPT
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
2
Sub-category id
3067
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Relational Database Design Catalog dimension db id 4

    Library dimension (catalog)

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

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Relational Database Design
relational-database-design
Skipped — no persistable v3 meta for new skill
skill_not_in_db_v3_proposed
Lakehouse Secondary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Lakehouse id=1359 · lakehouse

Aliases — catalog

  • Lakehouse (CANONICAL)

Context tags (catalog)

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

Stored enrichment (catalog DB)

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

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

Skill profile (library / DB)

Skill nature
PATTERN
Volatility
EMERGING
Typical lifespan
EVERGREEN
Category id
1
Sub-category id
1026
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • React Frontend Development Catalog dimension db id 96

    Library dimension (catalog)

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
React Frontend Development
d_init_01
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Azure Secondary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Azure id=188 · azure

Aliases — catalog

  • Azure (CANONICAL) primary

Context tags (catalog)

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

Stored enrichment (catalog DB)

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

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

Skill profile (library / DB)

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

Dimensions (API 2 worklist)

  • Cloud Platforms Catalog dimension db id 20

    Library dimension (catalog)

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

  • Cloud Platforms & Managed Services Catalog dimension db id 221

    Library dimension (catalog)

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

  • Cloud Platforms for AI Deployment Catalog dimension db id 211

    Library dimension (catalog)

    Roles linked in library: AI Engineer

  • Cloud Provider Platforms Catalog dimension db id 131

    Library dimension (catalog)

    Roles linked in library: Cloud Architect, Cloud Security Engineer

  • Cloud Security Posture Tools Catalog dimension db id 64

    Library dimension (catalog)

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

  • Vendor Product Families Catalog dimension db id 477

    Library dimension (catalog)

    Roles linked in library: Engineering Manager

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Cloud Platforms
cloud-platforms
Existing dimension (library) · Role↔dimension saved
Cloud Platforms & Managed Services
cloud-platforms-managed-services
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Cloud Platforms for AI Deployment
cloud-platforms-for-ai-deployment
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Cloud Provider Platforms
cloud-provider-platforms
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Cloud Security Posture Tools
cloud-security-posture-tools
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Vendor Product Families
vendor-product-families
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
AWS Secondary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: AWS id=187 · aws

Aliases — catalog

  • AWS (CANONICAL) primary

Context tags (catalog)

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

Stored enrichment (catalog DB)

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

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

Skill profile (library / DB)

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

Dimensions (API 2 worklist)

  • Cloud Platforms Catalog dimension db id 20

    Library dimension (catalog)

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

  • Cloud Platforms for AI Deployment Catalog dimension db id 211

    Library dimension (catalog)

    Roles linked in library: AI Engineer

  • Cloud Provider Platforms Catalog dimension db id 131

    Library dimension (catalog)

    Roles linked in library: Cloud Architect, Cloud Security Engineer

  • Cloud Security Posture Tools Catalog dimension db id 64

    Library dimension (catalog)

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

  • Vendor Product Families Catalog dimension db id 477

    Library dimension (catalog)

    Roles linked in library: Engineering Manager

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Cloud Platforms
cloud-platforms
Existing dimension (library) · Role↔dimension saved
Cloud Platforms for AI Deployment
cloud-platforms-for-ai-deployment
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Cloud Provider Platforms
cloud-provider-platforms
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Cloud Security Posture Tools
cloud-security-posture-tools
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Vendor Product Families
vendor-product-families
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Snowflake Secondary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Snowflake id=105 · snowflake

Aliases — catalog

  • Snowflake (CANONICAL) primary

Context tags (catalog)

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

Stored enrichment (catalog DB)

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

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

Skill profile (library / DB)

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

Dimensions (API 2 worklist)

  • Cloud Data Warehouses Catalog dimension db id 22

    Library dimension (catalog)

    Roles linked in library: Data Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Cloud Data Warehouses
cloud-data-warehouses
Existing dimension (library) · Role↔dimension saved
Data Lake Secondary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Data Lakes id=1358 · data-lakes

Aliases — catalog

  • Data Lakes (CANONICAL)

Context tags (catalog)

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

Stored enrichment (catalog DB)

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

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

Skill profile (library / DB)

Skill nature
PATTERN
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
1
Sub-category id
1025
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Cloud Storage and Data Services Catalog dimension db id 144

    Library dimension (catalog)

    Roles linked in library: Cloud Architect

  • React Frontend Development Catalog dimension db id 96

    Library dimension (catalog)

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Cloud Storage and Data Services
cloud-storage-and-data-services
Skipped — no persistable v3 meta for new skill
skill_not_in_db_v3_proposed
React Frontend Development
d_init_01
Skipped — no persistable v3 meta for new skill
skill_not_in_db_v3_proposed
REST Secondary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: REST id=11 · rest

Aliases — catalog

  • REST (CANONICAL) primary

Context tags (catalog)

API API design API versioning CRUD DELETE GET HATEOAS HTTP JSON OAuth OAuth2 OpenAPI POST PUT Postman RESTful Swagger URI Webhooks XML authentication client-server content negotiation endpoint endpoints middleware resource resource-oriented serialization stateless status codes versioning web services

Stored enrichment (catalog DB)

Category
Concept
Sub-category
Api Architecture Style
Year introduced
2000
Confidence
0.88
Version strategy
NOT_APPLICABLE

Maturity reasoning: REST is a default API architecture in many job descriptions and is widely supported by major vendors/frameworks; OpenAPI and RESTful endpoints remain standard in hiring pipelines.

Skill profile (library / DB)

Skill nature
CONCEPT
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
2
Sub-category id
2122
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • API Design and Contracts Catalog dimension db id 3

    Library dimension (catalog)

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

  • API Interface and Contract Design Catalog dimension db id 289

    Library dimension (catalog)

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

  • Integration Protocols & Standards Catalog dimension db id 271

    Library dimension (catalog)

    Roles linked in library: Pega Developer

  • Standards, Protocols & Compliance Catalog dimension db id 452

    Library dimension (catalog)

    Roles linked in library: Engineering Manager, Sitecore Dev

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
API Design and Contracts
api-design-and-contracts
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
API Interface and Contract Design
api-interface-and-contract-design
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Integration Protocols & Standards
integration-protocols-standards
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Standards, Protocols & Compliance
standards-protocols-compliance
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
JSON Secondary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: JSON id=1984 · json

Aliases — catalog

  • JSON (CANONICAL) primary

Context tags (catalog)

AJAX API JSON Schema JSON-LD JSONP JavaScript NoSQL REST configuration data binding data format data interchange data structure deserialization interoperability key-value pairs lightweight object notation schema serialization text-based

Stored enrichment (catalog DB)

Category
Format
Sub-category
Data Interchange Format
Confidence
0.99
Version strategy
NOT_APPLICABLE

Maturity reasoning: JSON is a default data interchange format in APIs and web stacks; it appears in a very high volume of job descriptions and is supported by every major language/runtime.

Skill profile (library / DB)

Skill nature
STANDARD
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
4
Sub-category id
1457
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • API Integration and Data Fetching Catalog dimension db id 127

    Library dimension (catalog)

    Roles linked in library: Angular Frontend Developer, Frontend Developer, Fullstack Developer, Fullstack Developer, React Frontend Developer, Svelte Frontend Developer, Vue Frontend Developer, Web Developer

  • API Interface and Contract Design Catalog dimension db id 289

    Library dimension (catalog)

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

  • Integration Protocols & Standards Catalog dimension db id 271

    Library dimension (catalog)

    Roles linked in library: Pega Developer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
API Integration and Data Fetching
api-integration-and-data-fetching
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
API Interface and Contract Design
api-interface-and-contract-design
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Integration Protocols & Standards
integration-protocols-standards
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
SQL in_db
Pega Programming Languages & DSLs
pega-programming-languages-dsls
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
SQL in_db
Programming Languages & DSLs
programming-languages-dsls
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
SQL in_db
Programming Languages for Data Work
programming-languages-for-data-work
Existing dimension (library) · Role↔dimension saved
Microsoft SQL Server new
Relational Database Design
relational-database-design
Skipped — no persistable v3 meta for new skill skill_not_in_db_v3_proposed
Star Schema in_db
Data Modeling and Schema Design
data-modeling-and-schema-design
Existing dimension (library) · Role↔dimension saved
Relational Database Design new
React Frontend Development
d_init_01
Skipped — no persistable v3 meta for new skill skill_not_in_db_v3_proposed
Query Performance Tuning new
Relational Database Design
relational-database-design
Skipped — no persistable v3 meta for new skill skill_not_in_db_v3_proposed
Lakehouse in_db
React Frontend Development
d_init_01
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Azure in_db
Cloud Platforms
cloud-platforms
Existing dimension (library) · Role↔dimension saved
Azure in_db
Cloud Platforms & Managed Services
cloud-platforms-managed-services
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Azure in_db
Cloud Platforms for AI Deployment
cloud-platforms-for-ai-deployment
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Azure in_db
Cloud Provider Platforms
cloud-provider-platforms
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Azure in_db
Cloud Security Posture Tools
cloud-security-posture-tools
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Azure in_db
Vendor Product Families
vendor-product-families
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
AWS in_db
Cloud Platforms
cloud-platforms
Existing dimension (library) · Role↔dimension saved
AWS in_db
Cloud Platforms for AI Deployment
cloud-platforms-for-ai-deployment
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
AWS in_db
Cloud Provider Platforms
cloud-provider-platforms
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
AWS in_db
Cloud Security Posture Tools
cloud-security-posture-tools
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
AWS in_db
Vendor Product Families
vendor-product-families
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Snowflake in_db
Cloud Data Warehouses
cloud-data-warehouses
Existing dimension (library) · Role↔dimension saved
Data Lake new
Cloud Storage and Data Services
cloud-storage-and-data-services
Skipped — no persistable v3 meta for new skill skill_not_in_db_v3_proposed
Data Lake new
React Frontend Development
d_init_01
Skipped — no persistable v3 meta for new skill skill_not_in_db_v3_proposed
REST in_db
API Design and Contracts
api-design-and-contracts
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
REST in_db
API Interface and Contract Design
api-interface-and-contract-design
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
REST in_db
Integration Protocols & Standards
integration-protocols-standards
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
REST in_db
Standards, Protocols & Compliance
standards-protocols-compliance
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
JSON in_db
API Integration and Data Fetching
api-integration-and-data-fetching
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
JSON in_db
API Interface and Contract Design
api-interface-and-contract-design
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
JSON in_db
Integration Protocols & Standards
integration-protocols-standards
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)

Library artifacts (this run)

Kind Detail DB id
canonical_skill_proposed SSIS | type=Data Engineering Tools subtype=general nature=TOOL lifespan=MULTI_YEAR
canonical_skill_proposed Azure Data Factory | type=Cloud Platforms subtype=Data Integration nature=PLATFORM lifespan=MULTI_YEAR
canonical_skill_proposed T-SQL | type=Programming Languages subtype=general nature=LANGUAGE lifespan=EVERGREEN
canonical_skill_proposed ETL | type=Data Engineering Tools subtype=general nature=CONCEPT lifespan=MULTI_YEAR
canonical_skill_proposed Data Warehouse | type=Data Engineering Tools subtype=general nature=CONCEPT lifespan=MULTI_YEAR
canonical_skill_proposed Data Mart | type=Data Engineering Tools subtype=general nature=CONCEPT lifespan=MULTI_YEAR
canonical_skill_proposed Snowflake Schema | type=Data Engineering Tools subtype=general nature=CONCEPT lifespan=MULTI_YEAR
canonical_skill_proposed DDL | type=Programming Languages subtype=general nature=CONCEPT lifespan=EVERGREEN
canonical_skill_proposed DML | type=Programming Languages subtype=general nature=CONCEPT lifespan=EVERGREEN
dimension_skill_link_proposed Microsoft SQL Server ↔ Relational Database Design
dimension_skill_link_proposed Relational Database Design ↔ React Frontend Development
dimension_skill_link_proposed Query Performance Tuning ↔ Relational Database Design
dimension_skill_link_proposed Data Lake ↔ Cloud Storage and Data Services
dimension_skill_link_proposed Data Lake ↔ React Frontend Development
nano JD Parser — gpt-4.1-nano click to toggle
RoleLead Data Engineer
Experience6+years
CTC{'max': 23, 'min': 18, 'raw': '18 to 23 lpa', 'period': 'annual', 'currency': 'INR'}
DomainIT Services & Consulting
Location Kochi, India
JD type pass
Show raw JSON
{
  "JD_type": "pass",
  "about_company": null,
  "certifications": [],
  "company_name": null,
  "ctc": {
    "currency": "INR",
    "max": 23,
    "min": 18,
    "period": "annual",
    "raw": "18 to 23 lpa"
  },
  "domain": {
    "primary": {
      "aliases": [],
      "domain": "IT Services \u0026 Consulting"
    },
    "secondary": null
  },
  "education": [],
  "experience": {
    "max": null,
    "min": 6,
    "raw": "6+years"
  },
  "job_locations": [
    {
      "aliases": [
        "Cochin"
      ],
      "city": "Kochi",
      "country": "India",
      "state": null,
      "work_mode": null
    },
    {
      "aliases": [
        "Thiruvananthapuram"
      ],
      "city": "Trivandrum",
      "country": "India",
      "state": null,
      "work_mode": null
    }
  ],
  "role": "Lead Data Engineer",
  "role_aliases": [
    "Data Engineer",
    "Lead Data Engineer",
    "Senior Data Engineer"
  ],
  "role_archetype": "Data",
  "roles_and_responsibilities": [
    {
      "bullet_count": 0,
      "heading": "Job Purpose",
      "heading_was_present": true,
      "source_marker": {
        "first_5_words": "We are looking for a",
        "last_5_words": "cross-functional teams."
      },
      "text": "We are looking for a highly motivated and experienced Lead Data Engineer with deep expertise in SSIS, SQL, and a strong ability to drive and lead data projects. The ideal candidate should have moderate experience with Azure Data Factory (ADF) and a passion for designing scalable, high-performance data solutions that support critical enterprise operations and analytics.\n\nYou will play a lead role in shaping our data infrastructure and delivering high-quality solutions in collaboration with cross-functional teams.",
      "word_count": 64
    },
    {
      "bullet_count": 8,
      "heading": "Key Responsibilities",
      "heading_was_present": true,
      "source_marker": {
        "first_5_words": "\u2022 Lead the design, development,",
        "last_5_words": "technical and non-technical audiences."
      },
      "text": "\u2022 Lead the design, development, and deployment of robust data pipelines and ETL processes using SSIS, SQL Server, and Azure Data Factory (ADF).\n\n\u2022 Drive end-to-end project execution \u2014 including planning, technical architecture, implementation, testing, and delivery.\n\n\u2022 Develop and maintain data lakes, data marts, and data warehouse solutions, supporting both structured and semi-structured data.\n\n\u2022 Ensure performance tuning and optimization of SQL queries, stored procedures, and ETL jobs.\n\u2022 Collaborate with business stakeholders and technical teams to gather data requirements and translate them into scalable solutions.\n\n\u2022 Mentor junior engineers and conduct code reviews to ensure adherence to best practices.\n\u2022 Contribute to architectural decisions involving Lakehouse, cloud adoption, and data integration strategies.\n\u2022 Implement and maintain data quality, governance, and security standards.\n\u2022 Maintain accurate project documentation and communicate effectively with technical and non-technical audiences.",
      "word_count": 186
    },
    {
      "bullet_count": 12,
      "heading": "Skills And Competencies",
      "heading_was_present": true,
      "source_marker": {
        "first_5_words": "Mandatory Technical Skills:",
        "last_5_words": "APIs, JSON, and integration patterns."
      },
      "text": "Mandatory Technical Skills:\n\n\u2022 Advanced proficiency in Microsoft SQL Server and T-SQL (stored procedures, views, indexing, triggers)\n\u2022 Expert-level experience with SSIS for ETL development\n\u2022 Hands-on experience in data warehouse and data mart modeling (Star/Snowflake schemas)\n\u2022 Strong understanding of DDL/DML operations and relational database design\n\u2022 Solid background in query performance tuning and optimization\n\nModerate Experience Required\n\n\u2022 Azure Data Factory (ADF) for data pipeline orchestration and cloud-based ETL\n\nPreferred / Nice To Have\n\n\u2022 Exposure to Lakehouse architecture\n\u2022 Experience with Cloud platforms (Azure, AWS, or Snowflake)\n\u2022 Familiarity with Data Lakes and handling large volumes of data\n\u2022 Working knowledge of REST APIs, JSON, and integration patterns.",
      "word_count": 174
    }
  ],
  "urls": []
}
API 1 — extract-from-jd click to toggle
{
  "final_skills": [
    {
      "is_primary": true,
      "skill_name": "SSIS"
    },
    {
      "is_primary": true,
      "skill_name": "SQL"
    },
    {
      "is_primary": true,
      "skill_name": "Azure Data Factory"
    },
    {
      "is_primary": true,
      "skill_name": "Microsoft SQL Server"
    },
    {
      "is_primary": true,
      "skill_name": "T-SQL"
    },
    {
      "is_primary": true,
      "skill_name": "ETL"
    },
    {
      "is_primary": true,
      "skill_name": "Data Warehouse"
    },
    {
      "is_primary": true,
      "skill_name": "Data Mart"
    },
    {
      "is_primary": true,
      "skill_name": "Star Schema"
    },
    {
      "is_primary": true,
      "skill_name": "Snowflake Schema"
    },
    {
      "is_primary": true,
      "skill_name": "DDL"
    },
    {
      "is_primary": true,
      "skill_name": "DML"
    },
    {
      "is_primary": true,
      "skill_name": "Relational Database Design"
    },
    {
      "is_primary": true,
      "skill_name": "Query Performance Tuning"
    },
    {
      "is_primary": false,
      "skill_name": "Lakehouse"
    },
    {
      "is_primary": false,
      "skill_name": "Azure"
    },
    {
      "is_primary": false,
      "skill_name": "AWS"
    },
    {
      "is_primary": false,
      "skill_name": "Snowflake"
    },
    {
      "is_primary": false,
      "skill_name": "Data Lake"
    },
    {
      "is_primary": false,
      "skill_name": "REST"
    },
    {
      "is_primary": false,
      "skill_name": "JSON"
    }
  ],
  "jd_role": {
    "display_name": "Lead Data Engineer",
    "rationale": null,
    "role_aliases": [
      "Data Engineer",
      "Lead Data Engineer",
      "Senior Data Engineer"
    ],
    "role_archetype": "Data",
    "slug": ""
  },
  "nano_parsed": {
    "JD_type": "pass",
    "about_company": null,
    "certifications": [],
    "company_name": null,
    "ctc": {
      "currency": "INR",
      "max": 23,
      "min": 18,
      "period": "annual",
      "raw": "18 to 23 lpa"
    },
    "domain": {
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        "aliases": [],
        "domain": "IT Services \u0026 Consulting"
      },
      "secondary": null
    },
    "education": [],
    "experience": {
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      "min": 6,
      "raw": "6+years"
    },
    "job_locations": [
      {
        "aliases": [
          "Cochin"
        ],
        "city": "Kochi",
        "country": "India",
        "state": null,
        "work_mode": null
      },
      {
        "aliases": [
          "Thiruvananthapuram"
        ],
        "city": "Trivandrum",
        "country": "India",
        "state": null,
        "work_mode": null
      }
    ],
    "role": "Lead Data Engineer",
    "role_aliases": [
      "Data Engineer",
      "Lead Data Engineer",
      "Senior Data Engineer"
    ],
    "role_archetype": "Data",
    "roles_and_responsibilities": [
      {
        "bullet_count": 0,
        "heading": "Job Purpose",
        "heading_was_present": true,
        "source_marker": {
          "first_5_words": "We are looking for a",
          "last_5_words": "cross-functional teams."
        },
        "text": "We are looking for a highly motivated and experienced Lead Data Engineer with deep expertise in SSIS, SQL, and a strong ability to drive and lead data projects. The ideal candidate should have moderate experience with Azure Data Factory (ADF) and a passion for designing scalable, high-performance data solutions that support critical enterprise operations and analytics.\n\nYou will play a lead role in shaping our data infrastructure and delivering high-quality solutions in collaboration with cross-functional teams.",
        "word_count": 64
      },
      {
        "bullet_count": 8,
        "heading": "Key Responsibilities",
        "heading_was_present": true,
        "source_marker": {
          "first_5_words": "\u2022 Lead the design, development,",
          "last_5_words": "technical and non-technical audiences."
        },
        "text": "\u2022 Lead the design, development, and deployment of robust data pipelines and ETL processes using SSIS, SQL Server, and Azure Data Factory (ADF).\n\n\u2022 Drive end-to-end project execution \u2014 including planning, technical architecture, implementation, testing, and delivery.\n\n\u2022 Develop and maintain data lakes, data marts, and data warehouse solutions, supporting both structured and semi-structured data.\n\n\u2022 Ensure performance tuning and optimization of SQL queries, stored procedures, and ETL jobs.\n\u2022 Collaborate with business stakeholders and technical teams to gather data requirements and translate them into scalable solutions.\n\n\u2022 Mentor junior engineers and conduct code reviews to ensure adherence to best practices.\n\u2022 Contribute to architectural decisions involving Lakehouse, cloud adoption, and data integration strategies.\n\u2022 Implement and maintain data quality, governance, and security standards.\n\u2022 Maintain accurate project documentation and communicate effectively with technical and non-technical audiences.",
        "word_count": 186
      },
      {
        "bullet_count": 12,
        "heading": "Skills And Competencies",
        "heading_was_present": true,
        "source_marker": {
          "first_5_words": "Mandatory Technical Skills:",
          "last_5_words": "APIs, JSON, and integration patterns."
        },
        "text": "Mandatory Technical Skills:\n\n\u2022 Advanced proficiency in Microsoft SQL Server and T-SQL (stored procedures, views, indexing, triggers)\n\u2022 Expert-level experience with SSIS for ETL development\n\u2022 Hands-on experience in data warehouse and data mart modeling (Star/Snowflake schemas)\n\u2022 Strong understanding of DDL/DML operations and relational database design\n\u2022 Solid background in query performance tuning and optimization\n\nModerate Experience Required\n\n\u2022 Azure Data Factory (ADF) for data pipeline orchestration and cloud-based ETL\n\nPreferred / Nice To Have\n\n\u2022 Exposure to Lakehouse architecture\n\u2022 Experience with Cloud platforms (Azure, AWS, or Snowflake)\n\u2022 Familiarity with Data Lakes and handling large volumes of data\n\u2022 Working knowledge of REST APIs, JSON, and integration patterns.",
        "word_count": 174
      }
    ],
    "urls": []
  },
  "rejected": false,
  "rejection_reason": null,
  "run_id": "4b36e8e4-28d7-4c2a-ac67-6dbc4b17a937",
  "stage3_signals": {
    "alias_found": true,
    "alias_match_roles": [
      {
        "display_name": "Data Engineer",
        "kra_matches": null,
        "matched_count": null,
        "matched_skills": null,
        "role_id": 2,
        "score": 1.0,
        "slug": "data-engineer",
        "total_count": null
      }
    ],
    "kra_match_roles": [
      {
        "display_name": "Data Engineer",
        "kra_matches": [
          {
            "kra_text": "Works with data analysts, data scientists, and business stakeholders to define data models, ingestion schedules, and data delivery requirements.",
            "sentence": "Collaborate with business stakeholders and technical teams to gather data requirements and translate them into scalable solutions.",
            "similarity": 0.6619
          },
          {
            "kra_text": "Designs dimensional models, star schemas, data vault structures, and curated data mart tables to support BI tools and self-service analytics consumption.",
            "sentence": "Hands-on experience in data warehouse and data mart modeling (Star/Snowflake schemas)",
            "similarity": 0.6594
          },
          {
            "kra_text": "Designs dimensional models, star schemas, data vault structures, and curated data mart tables to support BI tools and self-service analytics consumption.",
            "sentence": "Develop and maintain data lakes, data marts, and data warehouse solutions, supporting both structured and semi-structured data.",
            "similarity": 0.6488
          }
        ],
        "matched_count": null,
        "matched_skills": null,
        "role_id": 2,
        "score": 0.6567,
        "slug": "data-engineer",
        "total_count": null
      },
      {
        "display_name": "Java Backend Developer",
        "kra_matches": [
          {
            "kra_text": "backend performance tuning",
            "sentence": "Solid background in query performance tuning and optimization",
            "similarity": 0.6368
          },
          {
            "kra_text": "backend performance tuning",
            "sentence": "Ensure performance tuning and optimization of SQL queries, stored procedures, and ETL jobs.",
            "similarity": 0.5693
          },
          {
            "kra_text": "persistence and data modeling",
            "sentence": "Hands-on experience in data warehouse and data mart modeling (Star/Snowflake schemas)",
            "similarity": 0.5275
          }
        ],
        "matched_count": null,
        "matched_skills": null,
        "role_id": 79,
        "score": 0.5779,
        "slug": "java-backend-developer",
        "total_count": null
      },
      {
        "display_name": "Svelte Frontend Developer",
        "kra_matches": [
          {
            "kra_text": "performance tuning",
            "sentence": "Ensure performance tuning and optimization of SQL queries, stored procedures, and ETL jobs.",
            "similarity": 0.5962
          },
          {
            "kra_text": "performance tuning",
            "sentence": "Solid background in query performance tuning and optimization",
            "similarity": 0.5943
          },
          {
            "kra_text": "backend data integration",
            "sentence": "Develop and maintain data lakes, data marts, and data warehouse solutions, supporting both structured and semi-structured data.",
            "similarity": 0.4565
          }
        ],
        "matched_count": null,
        "matched_skills": null,
        "role_id": 92,
        "score": 0.549,
        "slug": "svelte-frontend-developer",
        "total_count": null
      },
      {
        "display_name": "Cloud Architect",
        "kra_matches": [
          {
            "kra_text": "Conducts architecture reviews, approves technical design documents, and guides engineering teams through cloud migration and modernization projects.",
            "sentence": "Contribute to architectural decisions involving Lakehouse, cloud adoption, and data integration strategies.",
            "similarity": 0.6025
          },
          {
            "kra_text": "Conducts architecture reviews, approves technical design documents, and guides engineering teams through cloud migration and modernization projects.",
            "sentence": "Mentor junior engineers and conduct code reviews to ensure adherence to best practices.",
            "similarity": 0.5209
          },
          {
            "kra_text": "Establishes cloud governance guardrails including budget alerts, resource quotas, policy-as-code enforcement, and compliance posture management.",
            "sentence": "Implement and maintain data quality, governance, and security standards.",
            "similarity": 0.4921
          }
        ],
        "matched_count": null,
        "matched_skills": null,
        "role_id": 9,
        "score": 0.5385,
        "slug": "cloud-architect",
        "total_count": null
      },
      {
        "display_name": "Scala Backend Developer",
        "kra_matches": [
          {
            "kra_text": "performance and reliability tuning",
            "sentence": "Ensure performance tuning and optimization of SQL queries, stored procedures, and ETL jobs.",
            "similarity": 0.5619
          },
          {
            "kra_text": "performance and reliability tuning",
            "sentence": "Solid background in query performance tuning and optimization",
            "similarity": 0.5462
          },
          {
            "kra_text": "application data modeling",
            "sentence": "Hands-on experience in data warehouse and data mart modeling (Star/Snowflake schemas)",
            "similarity": 0.5006
          }
        ],
        "matched_count": null,
        "matched_skills": null,
        "role_id": 87,
        "score": 0.5362,
        "slug": "scala-backend-developer",
        "total_count": null
      }
    ],
    "skill_match_roles": [
      {
        "display_name": "Data Engineer",
        "kra_matches": null,
        "matched_count": 2,
        "matched_skills": [
          "SQL",
          "Star schema"
        ],
        "role_id": 2,
        "score": 0.1429,
        "slug": "data-engineer",
        "total_count": 14
      },
      {
        "display_name": "Pega Developer",
        "kra_matches": null,
        "matched_count": 1,
        "matched_skills": [
          "SQL"
        ],
        "role_id": 24,
        "score": 0.0714,
        "slug": "pega-developer",
        "total_count": 14
      },
      {
        "display_name": "Engineering Manager",
        "kra_matches": null,
        "matched_count": 1,
        "matched_skills": [
          "SQL"
        ],
        "role_id": 121,
        "score": 0.0714,
        "slug": "engineering-manager",
        "total_count": 14
      }
    ]
  },
  "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 0.14 does not contradict",
    "sub_role": null
  },
  "stage5_updates": {
    "centroid_n_after": 400,
    "centroid_updated": true,
    "collision_log_id": null,
    "new_kra_attached": null,
    "new_skills_attached": [
      {
        "is_primary": true,
        "queue_id": 18464,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "SSIS",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 18465,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "Azure Data Factory",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 18466,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "Microsoft SQL Server",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 18467,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "T-SQL",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 18468,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "ETL",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 18469,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "Data Warehouse",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 18470,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "Data Mart",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 18471,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "Snowflake Schema",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 18472,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "DDL",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 18473,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "DML",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 18474,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "Relational Database Design",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 18475,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "Query Performance Tuning",
        "status": "pending"
      },
      {
        "is_primary": false,
        "queue_id": 18476,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "Data Lake",
        "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_persisted": false,
      "existing_alias_id": 271,
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      "input_term": "SQL",
      "matched_canonical": {
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        "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": "TODO: REMOVE AFTER TESTING \u2014 alias DB write disabled",
      "alias_persisted": false,
      "existing_alias_id": 135,
      "existing_alias_text": "SQL Server",
      "input_term": "Microsoft SQL Server",
      "matched_canonical": {
        "category_id": 3,
        "display_name": "SQL Server",
        "id": 18,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "TOOL",
        "slug": "sql-server",
        "sub_category_id": 29,
        "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": 330,
      "existing_alias_text": "Star schema",
      "input_term": "Star Schema",
      "matched_canonical": {
        "category_id": 1,
        "display_name": "Star schema",
        "id": 126,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "PATTERN",
        "slug": "star-schema",
        "sub_category_id": 70,
        "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": 1988,
      "existing_alias_text": "Relational Databases",
      "input_term": "Relational Database Design",
      "matched_canonical": {
        "category_id": 37,
        "display_name": "Relational Databases",
        "id": 1345,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "CONCEPT",
        "slug": "relational-databases",
        "sub_category_id": 1018,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "matched_via": "embedding_alias"
    },
    {
      "alias_persist_skipped_reason": "TODO: REMOVE AFTER TESTING \u2014 alias DB write disabled",
      "alias_persisted": false,
      "existing_alias_id": 5584,
      "existing_alias_text": "Query Tuning",
      "input_term": "Query Performance Tuning",
      "matched_canonical": {
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        "display_name": "query tuning",
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        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "CONCEPT",
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        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "matched_via": "embedding_alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 2018,
      "existing_alias_text": "Lakehouse",
      "input_term": "Lakehouse",
      "matched_canonical": {
        "category_id": 1,
        "display_name": "Lakehouse",
        "id": 1359,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "PATTERN",
        "slug": "lakehouse",
        "sub_category_id": 1026,
        "typical_lifespan": "EVERGREEN",
        "volatility": "EMERGING"
      },
      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 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": 406,
      "existing_alias_text": "AWS",
      "input_term": "AWS",
      "matched_canonical": {
        "category_id": 9,
        "display_name": "AWS",
        "id": 187,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "PLATFORM",
        "slug": "aws",
        "sub_category_id": 46,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 299,
      "existing_alias_text": "Snowflake",
      "input_term": "Snowflake",
      "matched_canonical": {
        "category_id": 9,
        "display_name": "Snowflake",
        "id": 105,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "PLATFORM",
        "slug": "snowflake",
        "sub_category_id": 113,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "TODO: REMOVE AFTER TESTING \u2014 alias DB write disabled",
      "alias_persisted": false,
      "existing_alias_id": 2017,
      "existing_alias_text": "Data Lakes",
      "input_term": "Data Lake",
      "matched_canonical": {
        "category_id": 1,
        "display_name": "Data Lakes",
        "id": 1358,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "PATTERN",
        "slug": "data-lakes",
        "sub_category_id": 1025,
        "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": 106,
      "existing_alias_text": "REST",
      "input_term": "REST",
      "matched_canonical": {
        "category_id": 2,
        "display_name": "REST",
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        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "CONCEPT",
        "slug": "rest",
        "sub_category_id": 2122,
        "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": 3018,
      "existing_alias_text": "JSON",
      "input_term": "JSON",
      "matched_canonical": {
        "category_id": 4,
        "display_name": "JSON",
        "id": 1984,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "STANDARD",
        "slug": "json",
        "sub_category_id": 1457,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "matched_via": "alias"
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  ],
  "candidate_roles": [
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      "rationale": null,
      "role_archetype": null,
      "slug": "pega-developer",
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    },
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      "slug": "engineering-manager",
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    },
    {
      "display_name": "Data Engineer",
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      "slug": "data-engineer",
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    },
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      "rationale": null,
      "role_archetype": "Engineering",
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      "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.",
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    },
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      "display_name": "Kotlin Backend Developer",
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      "rationale": null,
      "role_archetype": "Engineering",
      "slug": "kotlin-server-backend-developer",
      "source": "db"
    },
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      "display_name": "Node.js Backend Developer",
      "id": 82,
      "rationale": null,
      "role_archetype": "Engineering",
      "slug": "node-backend-developer",
      "source": "db"
    },
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      "rationale": null,
      "role_archetype": "Engineering",
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      "source": "db"
    },
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      "rationale": null,
      "role_archetype": "Engineering",
      "slug": "ruby-backend-developer",
      "source": "db"
    },
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      "rationale": null,
      "role_archetype": "Engineering",
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      "slug": "cybersecurity-engineer",
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    },
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      "rationale": null,
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      "slug": "devops-engineer",
      "source": "db"
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    {
      "display_name": "Fullstack Developer",
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      "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",
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      "rationale": null,
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      "slug": "java-backend-developer",
      "source": "db"
    },
    {
      "display_name": "ML Engineer",
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      "role_archetype": null,
      "slug": "ml-engineer",
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    },
    {
      "display_name": "MLOps Engineer",
      "id": 16,
      "rationale": null,
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      "slug": "ml-ops-engineer",
      "source": "db"
    },
    {
      "display_name": "AI Engineer",
      "id": 13,
      "rationale": null,
      "role_archetype": null,
      "slug": "ai-engineer",
      "source": "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"
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    {
      "display_name": "Fullstack Developer",
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      "rationale": null,
      "role_archetype": "Engineering",
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      "source": "db"
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      "rationale": null,
      "role_archetype": "Engineering",
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      "source": "db"
    },
    {
      "display_name": "Sitecore Dev",
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      "rationale": null,
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    },
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      "display_name": "Angular Frontend Developer",
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      "rationale": null,
      "role_archetype": "Engineering",
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      "slug": "frontend-engineer",
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      "rationale": null,
      "role_archetype": "Engineering",
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      "role_archetype": "Engineering",
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      "display_name": "Web Developer",
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      "role_archetype": null,
      "slug": "web-developer",
      "source": "db"
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  ],
  "chosen_role": {
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    "rationale": "Exact alias hit on data-engineer (1.0) \u2014 no other alias at this confidence; skill_top data-engineer 0.14 does not contradict",
    "role_archetype": null,
    "slug": "data-engineer",
    "source": "db"
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  "dimensions": [
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        "rationale": "Programming languages and domain-specific languages used in Pega development.",
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        "source": "db"
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        }
      ]
    },
    {
      "dimension": {
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      ]
    },
    {
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      ]
    },
    {
      "dimension": {
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      "roles_from_db": [
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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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              "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": "AWS",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "Cloud Security Engineer",
              "id": 23,
              "rationale": null,
              "role_archetype": null,
              "slug": "cloud-security-engineer",
              "source": "db"
            },
            {
              "display_name": "Cyber Security Engineer",
              "id": 5,
              "rationale": null,
              "role_archetype": null,
              "slug": "cybersecurity-engineer",
              "source": "db"
            }
          ]
        },
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Vendor Product Families",
            "id": 477,
            "rationale": "Coordinate usage, licensing, and architecture decisions for major vendor software and cloud product families.",
            "slug": "vendor-product-families",
            "source": "db"
          },
          "input_skill": "AWS",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "Engineering Manager",
              "id": 121,
              "rationale": null,
              "role_archetype": null,
              "slug": "engineering-manager",
              "source": "db"
            }
          ]
        }
      ],
      "input_skill": "AWS",
      "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": "Snowflake",
          "alias_type": "CANONICAL",
          "id": 299,
          "is_primary": true,
          "match_strategy": "CASE_INSENSITIVE"
        }
      ],
      "canonical": {
        "category_id": 9,
        "display_name": "Snowflake",
        "id": 105,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "PLATFORM",
        "slug": "snowflake",
        "sub_category_id": 113,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "dimensions": [
        {
          "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"
          },
          "input_skill": "Snowflake",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "Data Engineer",
              "id": 2,
              "rationale": null,
              "role_archetype": null,
              "slug": "data-engineer",
              "source": "db"
            }
          ]
        }
      ],
      "input_skill": "Snowflake",
      "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": "Data Lakes",
          "alias_type": "CANONICAL",
          "id": 2017,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        }
      ],
      "canonical": {
        "category_id": 1,
        "display_name": "Data Lakes",
        "id": 1358,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "PATTERN",
        "slug": "data-lakes",
        "sub_category_id": 1025,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "dimensions": [
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Cloud Storage and Data Services",
            "id": 144,
            "rationale": "Cloud-native storage and managed data services used to place workloads, choose durability tiers, and define platform boundaries. This is a coherent cluster because architects evaluate storage fit, access patterns, and managed service tradeoffs.",
            "slug": "cloud-storage-and-data-services",
            "source": "db"
          },
          "input_skill": "Data Lake",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "Cloud Architect",
              "id": 9,
              "rationale": null,
              "role_archetype": null,
              "slug": "cloud-architect",
              "source": "db"
            }
          ]
        },
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "React Frontend Development",
            "id": 96,
            "rationale": "Building interactive web user interfaces with React.js, including component composition, state management, hooks, and rendering patterns. React.js belongs here because it is a core library for client-side UI development in modern web applications.",
            "slug": "d_init_01",
            "source": "db"
          },
          "input_skill": "Data Lake",
          "llm_role": null,
          "roles_from_db": []
        }
      ],
      "input_skill": "Data Lake",
      "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": "REST",
          "alias_type": "CANONICAL",
          "id": 106,
          "is_primary": true,
          "match_strategy": "CASE_INSENSITIVE"
        }
      ],
      "canonical": {
        "category_id": 2,
        "display_name": "REST",
        "id": 11,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "CONCEPT",
        "slug": "rest",
        "sub_category_id": 2122,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "dimensions": [
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "API Design and Contracts",
            "id": 3,
            "rationale": "Designing request/response shapes and the rules that govern client-server interaction. This cluster is coherent because full stack engineers often own the contract between UI behavior and backend implementation.",
            "slug": "api-design-and-contracts",
            "source": "db"
          },
          "input_skill": "REST",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "Backend Developer",
              "id": 1,
              "rationale": null,
              "role_archetype": "A Backend Engineer designs, builds, and maintains the server-side logic and data handling that power applications and services. They focus on implementing reliable business functionality, integrating with other systems, and ensuring the backend is scalable, maintainable, and observable.",
              "slug": "backend-engineer",
              "source": "db"
            },
            {
              "display_name": "Fullstack Developer",
              "id": 15,
              "rationale": null,
              "role_archetype": null,
              "slug": "full-stack-engineer",
              "source": "db"
            },
            {
              "display_name": "Fullstack Developer",
              "id": 435,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "fullstack-developer",
              "source": "db"
            }
          ]
        },
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "API Interface and Contract Design",
            "id": 289,
            "rationale": "Designing backend service interfaces and contracts that other systems consume, including endpoint and operation shape, request/response payloads, schema and validation, pagination, filtering, idempotency, versioning, status codes, and backward compatibility across REST, GraphQL, gRPC, and OpenAPI-based APIs.",
            "slug": "api-interface-and-contract-design",
            "source": "db"
          },
          "input_skill": "REST",
          "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": "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"
            },
            {
              "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": "Integration Protocols \u0026 Standards",
            "id": 271,
            "rationale": "Standards and protocols for integrating Pega applications.",
            "slug": "integration-protocols-standards",
            "source": "db"
          },
          "input_skill": "REST",
          "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": "Standards, Protocols \u0026 Compliance",
            "id": 452,
            "rationale": "Ensure teams adhere to industry standards, security protocols, and regulatory compliance requirements.",
            "slug": "standards-protocols-compliance",
            "source": "db"
          },
          "input_skill": "REST",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "Engineering Manager",
              "id": 121,
              "rationale": null,
              "role_archetype": null,
              "slug": "engineering-manager",
              "source": "db"
            },
            {
              "display_name": "Sitecore Dev",
              "id": 233,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "sitecore-dev",
              "source": "db"
            }
          ]
        }
      ],
      "input_skill": "REST",
      "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": "JSON",
          "alias_type": "CANONICAL",
          "id": 3018,
          "is_primary": true,
          "match_strategy": "CASE_INSENSITIVE"
        }
      ],
      "canonical": {
        "category_id": 4,
        "display_name": "JSON",
        "id": 1984,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "STANDARD",
        "slug": "json",
        "sub_category_id": 1457,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "dimensions": [
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "API Integration and Data Fetching",
            "id": 127,
            "rationale": "Client-side integration with backend endpoints and third-party services, including request shaping, response handling, and synchronization with UI state. This is central to frontend work because most screens depend on remote data.",
            "slug": "api-integration-and-data-fetching",
            "source": "db"
          },
          "input_skill": "JSON",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "Angular Frontend Developer",
              "id": 90,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "angular-frontend-developer",
              "source": "db"
            },
            {
              "display_name": "Frontend Developer",
              "id": 7,
              "rationale": null,
              "role_archetype": null,
              "slug": "frontend-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"
            },
            {
              "display_name": "React Frontend Developer",
              "id": 89,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "react-frontend-developer",
              "source": "db"
            },
            {
              "display_name": "Svelte Frontend Developer",
              "id": 92,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "svelte-frontend-developer",
              "source": "db"
            },
            {
              "display_name": "Vue Frontend Developer",
              "id": 91,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "vue-frontend-developer",
              "source": "db"
            },
            {
              "display_name": "Web Developer",
              "id": 25,
              "rationale": null,
              "role_archetype": null,
              "slug": "web-developer",
              "source": "db"
            }
          ]
        },
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "API Interface and Contract Design",
            "id": 289,
            "rationale": "Designing backend service interfaces and contracts that other systems consume, including endpoint and operation shape, request/response payloads, schema and validation, pagination, filtering, idempotency, versioning, status codes, and backward compatibility across REST, GraphQL, gRPC, and OpenAPI-based APIs.",
            "slug": "api-interface-and-contract-design",
            "source": "db"
          },
          "input_skill": "JSON",
          "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": "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"
            },
            {
              "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": "Integration Protocols \u0026 Standards",
            "id": 271,
            "rationale": "Standards and protocols for integrating Pega applications.",
            "slug": "integration-protocols-standards",
            "source": "db"
          },
          "input_skill": "JSON",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "Pega Developer",
              "id": 24,
              "rationale": null,
              "role_archetype": null,
              "slug": "pega-developer",
              "source": "db"
            }
          ]
        }
      ],
      "input_skill": "JSON",
      "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": [
    "SSIS",
    "Azure Data Factory",
    "T-SQL",
    "ETL",
    "Data Warehouse",
    "Data Mart",
    "Snowflake Schema",
    "DDL",
    "DML"
  ]
}
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 0.14 does not contradict",
    "role_archetype": null,
    "slug": "data-engineer",
    "source": "db"
  },
  "chosen_role_resolution": "in_db",
  "final_input_skills": [
    {
      "skill": "SSIS",
      "tag": "new"
    },
    {
      "skill": "SQL",
      "tag": "in_db"
    },
    {
      "skill": "Azure Data Factory",
      "tag": "new"
    },
    {
      "skill": "Microsoft SQL Server",
      "tag": "in_db"
    },
    {
      "skill": "T-SQL",
      "tag": "new"
    },
    {
      "skill": "ETL",
      "tag": "new"
    },
    {
      "skill": "Data Warehouse",
      "tag": "new"
    },
    {
      "skill": "Data Mart",
      "tag": "new"
    },
    {
      "skill": "Star Schema",
      "tag": "in_db"
    },
    {
      "skill": "Snowflake Schema",
      "tag": "new"
    },
    {
      "skill": "DDL",
      "tag": "new"
    },
    {
      "skill": "DML",
      "tag": "new"
    },
    {
      "skill": "Relational Database Design",
      "tag": "in_db"
    },
    {
      "skill": "Query Performance Tuning",
      "tag": "in_db"
    },
    {
      "skill": "Lakehouse",
      "tag": "in_db"
    },
    {
      "skill": "Azure",
      "tag": "in_db"
    },
    {
      "skill": "AWS",
      "tag": "in_db"
    },
    {
      "skill": "Snowflake",
      "tag": "in_db"
    },
    {
      "skill": "Data Lake",
      "tag": "in_db"
    },
    {
      "skill": "REST",
      "tag": "in_db"
    },
    {
      "skill": "JSON",
      "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": "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
      },
      {
        "chosen_role_id": 2,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Programming Languages \u0026 DSLs",
          "id": 475,
          "rationale": "Oversee and guide the selection and effective use of programming and domain\u2010specific languages in software projects.",
          "slug": "programming-languages-dsls",
          "source": "db"
        },
        "dimension_id": 475,
        "input_skill": "SQL",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "Engineering Manager",
            "id": 121,
            "rationale": null,
            "role_archetype": null,
            "slug": "engineering-manager",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 101,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 2,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Programming Languages for Data Work",
          "id": 21,
          "rationale": "Languages used to implement data pipelines, transformations, and operational glue. This is the primary coding surface for building ingestion, enrichment, and automation logic in data engineering.",
          "slug": "programming-languages-for-data-work",
          "source": "db"
        },
        "dimension_id": 21,
        "input_skill": "SQL",
        "llm_role": null,
        "matched_chosen_role": true,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension saved",
        "role_dimension_saved": true,
        "roles_from_db": [
          {
            "display_name": "Data Engineer",
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