Pipeline run
4b36e8e4-28d7-4c2a-ac67-6dbc4b17a937
Client output enrichment
v2 Skill cluster · Nature of work · AI index · Tech stack maturity · Evidence · KRA descriptionvocab breakdown (legacy)
Signals
Post-classification
Captured for admin review
1 POST /skills/extract-from-jd
2 POST /skills/extract-details
3 POST /skills/final-role-output
Data Engineer
CASE Aslug: 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.
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.
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Data Engineering Tools
- Sub-category
- general
- Skill nature
- TOOL
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Aliases — catalog
- SQL (CANONICAL) primary
Context tags (catalog)
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 |
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Cloud Platforms
- Sub-category
- Data Integration
- Skill nature
- PLATFORM
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
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)
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
|
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Programming Languages
- Sub-category
- general
- Skill nature
- LANGUAGE
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Version strategy
- UNVERSIONED
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Data Engineering Tools
- Sub-category
- general
- Skill nature
- CONCEPT
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Data Engineering Tools
- Sub-category
- general
- Skill nature
- CONCEPT
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Data Engineering Tools
- Sub-category
- general
- Skill nature
- CONCEPT
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Aliases — catalog
- Star schema (CANONICAL) primary
Context tags (catalog)
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 |
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Data Engineering Tools
- Sub-category
- general
- Skill nature
- CONCEPT
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Programming Languages
- Sub-category
- general
- Skill nature
- CONCEPT
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Version strategy
- UNVERSIONED
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Programming Languages
- Sub-category
- general
- Skill nature
- CONCEPT
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Version strategy
- UNVERSIONED
Aliases — catalog
- Relational Databases (CANONICAL)
Context tags (catalog)
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
|
Aliases — catalog
- query tuning (CANONICAL) primary
- Query Tuning (CANONICAL)
Context tags (catalog)
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
|
Aliases — catalog
- Lakehouse (CANONICAL)
Context tags (catalog)
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) |
Aliases — catalog
- Azure (CANONICAL) primary
Context tags (catalog)
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) |
Aliases — catalog
- AWS (CANONICAL) primary
Context tags (catalog)
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) |
Aliases — catalog
- Snowflake (CANONICAL) primary
Context tags (catalog)
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 |
Aliases — catalog
- Data Lakes (CANONICAL)
Context tags (catalog)
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
|
Aliases — catalog
- REST (CANONICAL) primary
Context tags (catalog)
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) |
Aliases — catalog
- JSON (CANONICAL) primary
Context tags (catalog)
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
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": {
"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": []
},
"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
{
"alias_matches": [
{
"alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
"alias_persisted": false,
"existing_alias_id": 271,
"existing_alias_text": "SQL",
"input_term": "SQL",
"matched_canonical": {
"category_id": 6,
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"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",
"id": 2,
"rationale": null,
"role_archetype": null,
"slug": "data-engineer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 101,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 2,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Relational Database Design",
"id": 4,
"rationale": "Modeling and operating relational persistence for backend services. Includes schema design, normalization, indexing, transactions, and query tuning for operational data stores.",
"slug": "relational-database-design",
"source": "db"
},
"dimension_id": 4,
"input_skill": "Microsoft SQL Server",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "Skipped \u2014 no persistable v3 meta for new skill",
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": ".NET Backend Developer",
"id": 83,
"rationale": null,
"role_archetype": "Engineering",
"slug": "dotnet-backend-developer",
"source": "db"
},
{
"display_name": "Backend Developer",
"id": 1,
"rationale": null,
"role_archetype": "A Backend Engineer designs, builds, and maintains the server-side logic and data handling that power applications and services. They focus on implementing reliable business functionality, integrating with other systems, and ensuring the backend is scalable, maintainable, and observable.",
"slug": "backend-engineer",
"source": "db"
},
{
"display_name": "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": "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"
}
],
"skill_dimension_saved": false,
"skill_id": null,
"skill_tag": "new",
"skipped_reason": "skill_not_in_db_v3_proposed"
},
{
"chosen_role_id": 2,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Data Modeling and Schema Design",
"id": 26,
"rationale": "Designing curated data structures for analytics and downstream consumption. Covers dimensional modeling, normalization tradeoffs, slowly changing dimensions, and schema evolution for durable datasets.",
"slug": "data-modeling-and-schema-design",
"source": "db"
},
"dimension_id": 26,
"input_skill": "Star Schema",
"llm_role": null,
"matched_chosen_role": true,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension saved",
"role_dimension_saved": true,
"roles_from_db": [
{
"display_name": "Data Engineer",
"id": 2,
"rationale": null,
"role_archetype": null,
"slug": "data-engineer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 126,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 2,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "React Frontend Development",
"id": 96,
"rationale": "Building interactive web user interfaces with React.js, including component composition, state management, hooks, and rendering patterns. React.js belongs here because it is a core library for client-side UI development in modern web applications.",
"slug": "d_init_01",
"source": "db"
},
"dimension_id": 96,
"input_skill": "Relational Database Design",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "Skipped \u2014 no persistable v3 meta for new skill",
"role_dimension_saved": false,
"roles_from_db": [],
"skill_dimension_saved": false,
"skill_id": null,
"skill_tag": "new",
"skipped_reason": "skill_not_in_db_v3_proposed"
},
{
"chosen_role_id": 2,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Relational Database Design",
"id": 4,
"rationale": "Modeling and operating relational persistence for backend services. Includes schema design, normalization, indexing, transactions, and query tuning for operational data stores.",
"slug": "relational-database-design",
"source": "db"
},
"dimension_id": 4,
"input_skill": "Query Performance Tuning",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "Skipped \u2014 no persistable v3 meta for new skill",
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": ".NET Backend Developer",
"id": 83,
"rationale": null,
"role_archetype": "Engineering",
"slug": "dotnet-backend-developer",
"source": "db"
},
{
"display_name": "Backend Developer",
"id": 1,
"rationale": null,
"role_archetype": "A Backend Engineer designs, builds, and maintains the server-side logic and data handling that power applications and services. They focus on implementing reliable business functionality, integrating with other systems, and ensuring the backend is scalable, maintainable, and observable.",
"slug": "backend-engineer",
"source": "db"
},
{
"display_name": "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": "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"
}
],
"skill_dimension_saved": false,
"skill_id": null,
"skill_tag": "new",
"skipped_reason": "skill_not_in_db_v3_proposed"
},
{
"chosen_role_id": 2,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "React Frontend Development",
"id": 96,
"rationale": "Building interactive web user interfaces with React.js, including component composition, state management, hooks, and rendering patterns. React.js belongs here because it is a core library for client-side UI development in modern web applications.",
"slug": "d_init_01",
"source": "db"
},
"dimension_id": 96,
"input_skill": "Lakehouse",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [],
"skill_dimension_saved": true,
"skill_id": 1359,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 2,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Cloud Platforms",
"id": 20,
"rationale": "Underlying cloud providers that host the managed services or infrastructure used by the role, such as AWS, Azure, and GCP.",
"slug": "cloud-platforms",
"source": "db"
},
"dimension_id": 20,
"input_skill": "Azure",
"llm_role": null,
"matched_chosen_role": true,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension saved",
"role_dimension_saved": true,
"roles_from_db": [
{
"display_name": ".NET Backend Developer",
"id": 83,
"rationale": null,
"role_archetype": "Engineering",
"slug": "dotnet-backend-developer",
"source": "db"
},
{
"display_name": "Backend Developer",
"id": 1,
"rationale": null,
"role_archetype": "A Backend Engineer designs, builds, and maintains the server-side logic and data handling that power applications and services. They focus on implementing reliable business functionality, integrating with other systems, and ensuring the backend is scalable, maintainable, and observable.",
"slug": "backend-engineer",
"source": "db"
},
{
"display_name": "Cyber Security Engineer",
"id": 5,
"rationale": null,
"role_archetype": null,
"slug": "cybersecurity-engineer",
"source": "db"
},
{
"display_name": "Data Engineer",
"id": 2,
"rationale": null,
"role_archetype": null,
"slug": "data-engineer",
"source": "db"
},
{
"display_name": "DevOps Engineer",
"id": 10,
"rationale": null,
"role_archetype": null,
"slug": "devops-engineer",
"source": "db"
},
{
"display_name": "Fullstack Developer",
"id": 15,
"rationale": null,
"role_archetype": null,
"slug": "full-stack-engineer",
"source": "db"
},
{
"display_name": "Go Backend Developer",
"id": 81,
"rationale": null,
"role_archetype": "Engineering",
"slug": "go-backend-developer",
"source": "db"
},
{
"display_name": "Java Backend Developer",
"id": 79,
"rationale": null,
"role_archetype": "Engineering",
"slug": "java-backend-developer",
"source": "db"
},
{
"display_name": "Kotlin Backend Developer",
"id": 84,
"rationale": null,
"role_archetype": "Engineering",
"slug": "kotlin-server-backend-developer",
"source": "db"
},
{
"display_name": "ML Engineer",
"id": 3,
"rationale": null,
"role_archetype": null,
"slug": "ml-engineer",
"source": "db"
},
{
"display_name": "MLOps Engineer",
"id": 16,
"rationale": null,
"role_archetype": null,
"slug": "ml-ops-engineer",
"source": "db"
},
{
"display_name": "Node.js Backend Developer",
"id": 82,
"rationale": null,
"role_archetype": "Engineering",
"slug": "node-backend-developer",
"source": "db"
},
{
"display_name": "Python Backend Developer",
"id": 80,
"rationale": null,
"role_archetype": "Engineering",
"slug": "python-backend-developer",
"source": "db"
},
{
"display_name": "Scala Backend Developer",
"id": 87,
"rationale": null,
"role_archetype": "Engineering",
"slug": "scala-backend-developer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 188,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 2,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Cloud Platforms \u0026 Managed Services",
"id": 221,
"rationale": "Operates and integrates vendor-specific cloud compute, storage, and hosting services.",
"slug": "cloud-platforms-managed-services",
"source": "db"
},
"dimension_id": 221,
"input_skill": "Azure",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "Fullstack Developer",
"id": 15,
"rationale": null,
"role_archetype": null,
"slug": "full-stack-engineer",
"source": "db"
},
{
"display_name": "Go Backend Developer",
"id": 81,
"rationale": null,
"role_archetype": "Engineering",
"slug": "go-backend-developer",
"source": "db"
},
{
"display_name": "Node.js Backend Developer",
"id": 82,
"rationale": null,
"role_archetype": "Engineering",
"slug": "node-backend-developer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 188,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 2,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Cloud Platforms for AI Deployment",
"id": 211,
"rationale": "Major cloud services that provide infrastructure and managed services for AI workloads.",
"slug": "cloud-platforms-for-ai-deployment",
"source": "db"
},
"dimension_id": 211,
"input_skill": "Azure",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "AI Engineer",
"id": 13,
"rationale": null,
"role_archetype": null,
"slug": "ai-engineer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 188,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 2,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Cloud Provider Platforms",
"id": 131,
"rationale": "Major cloud platforms and their core service ecosystems used to design target-state architectures, choose deployment boundaries, and evaluate managed capabilities. This is the primary substrate for cloud architecture decisions.",
"slug": "cloud-provider-platforms",
"source": "db"
},
"dimension_id": 131,
"input_skill": "Azure",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "Cloud Architect",
"id": 9,
"rationale": null,
"role_archetype": null,
"slug": "cloud-architect",
"source": "db"
},
{
"display_name": "Cloud Security Engineer",
"id": 23,
"rationale": null,
"role_archetype": null,
"slug": "cloud-security-engineer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 188,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 2,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Cloud Security Posture Tools",
"id": 64,
"rationale": "Cloud-native security platforms used to assess misconfiguration, workload exposure, and cloud control coverage. This dimension includes the major CNAPP/CSPM/CWPP vendors and cloud security services the role reviews and tunes.",
"slug": "cloud-security-posture-tools",
"source": "db"
},
"dimension_id": 64,
"input_skill": "Azure",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "Cloud Security Engineer",
"id": 23,
"rationale": null,
"role_archetype": null,
"slug": "cloud-security-engineer",
"source": "db"
},
{
"display_name": "Cyber Security Engineer",
"id": 5,
"rationale": null,
"role_archetype": null,
"slug": "cybersecurity-engineer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 188,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 2,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Vendor Product Families",
"id": 477,
"rationale": "Coordinate usage, licensing, and architecture decisions for major vendor software and cloud product families.",
"slug": "vendor-product-families",
"source": "db"
},
"dimension_id": 477,
"input_skill": "Azure",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "Engineering Manager",
"id": 121,
"rationale": null,
"role_archetype": null,
"slug": "engineering-manager",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 188,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 2,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Cloud Platforms",
"id": 20,
"rationale": "Underlying cloud providers that host the managed services or infrastructure used by the role, such as AWS, Azure, and GCP.",
"slug": "cloud-platforms",
"source": "db"
},
"dimension_id": 20,
"input_skill": "AWS",
"llm_role": null,
"matched_chosen_role": true,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension saved",
"role_dimension_saved": true,
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