Pipeline run
4f719af0-a7cb-41bd-9a42-3910f47ccec1
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
domain · Data Engineering & Analytics CASE DOMAINslug: data-engineer · id: 2 · source: db
Domain=Data Engineering & Analytics; The JD is centered on Azure-based data architecture, ETL/ELT, data integration, batch and real-time pipelines, and team leadership, which best matches a Data Engineer role.
Matched skills
Matched dimensions
Matched KRAs
Resolution:
in_db
— role exists in library; skill↔dim and role↔dim links saved when applicable.
Job description
At EY, you’ll have the chance to build a career as unique as you are, with the global scale, support, inclusive culture and technology to become the best version of you. And we’re counting on your unique voice and perspective to help EY become even better, too. Join us and build an exceptional experience for yourself, and a better working world for all. Azure Data Architect - Azure Lead Data Engineer – Consulting As part of our GDS Consulting team, you will be part of Digital & Emerging team delivering specific to Microsoft account. You will be working on latest Microsoft BI technologies and will collaborate with other teams within Consulting services. The opportunity We’re looking for resources with expertise in Azure & Data Engineering to join the group of our Data Platform team. This is a fantastic opportunity to be part of a leading firm whilst being instrumental in the growth of our service offering. Your Key Responsibilities • Design and implement scalable and efficient data architectures on Azure/Microsoft Fabric for batch and real-time processing. • Develop and optimize ETL/ELT pipelines using Azure Data Factory, Azure Synapse, and Azure Data bricks or Microsoft Fabric. • Build and maintain data integration solutions leveraging PySpark and ADLS. • Implement data models and data storage solutions in Azure SQL and Synapse for analytics and reporting. • Manage data ingestion from various sources, including APIs, databases, and streaming platforms. • Ensure data quality, consistency, and security across the platform. • Design and develop real-time data processing solutions using Azure Event Hub, Azure Stream Analytics, and Azure Functions. • Enable seamless integration for streaming data pipelines and event-driven architectures. • Work closely with data scientists, analysts, and business stakeholders to understand data requirements. • Translate business needs into technical solutions and provide data engineering expertise to the team. • Optimize data pipelines for performance, scalability, and cost-efficiency. • Implement and enforce best practices for data governance, security, and compliance. • Stay updated with the latest Azure data services and technologies to drive innovation. • Mentor junior engineers and lead technical discussions within the team. Skills And Attributes For Success • Collaborating with other members of the engagement team to plan the engagement and develop work program timelines, risk assessments and other documents/templates. • Able to manage Senior stakeholders. • Experience in leading teams to execute high quality deliverables within stipulated timeline. • Must-Have Skills: • Expertise to design and develop integration and reporting solution in Power BI/Power BI Fabric • Proficiency in Azure Databricks or Azure Synapse for data processing and analytics. • Expertise in PySpark for big data processing and transformations. • Strong experience with Azure Data Factory (ADF) for orchestration and ETL/ELT. • Solid knowledge of Azure SQL and ADLS for data storage and querying. • Good-to-Have Skills: • Familiarity with Azure Event Hub and Azure Stream Analytics for real-time data processing. • Experience with Azure Functions and Logic Apps for event-driven workflows. • Knowledge of Python for data manipulation and scripting. • Understanding of Dataverse for data integration. • Excellent Written and Communication Skills • Ability to deliver technical demonstrations • Quick learner with “can do” attitude • Demonstrating and applying strong project management skills, inspiring teamwork and responsibility with engagement team members • Certifications (Preferred): • DP-203: Data Engineering on Microsoft Azure. • DP-600/DP -700: Fabric Data Analyst/ Fabric Data engineer • Databricks Data Engineer Associate or Professional Certification. • Databricks Certified Associate Developer for Apache Spark. To qualify for the role, you must have • Bachelor’s or Master’s degree in Computer Science, Data Engineering, or a related field. • 3.5-8 years of hands-on experience in Azure data engineering. • Proven track record of designing and implementing large-scale data solutions. • Strong problem-solving skills and ability to work in a fast-paced environment. Ideally, you’ll also have • Analytical ability to manage multiple projects and prioritize tasks into manageable work products. • Can operate independently or with minimum supervision What Working At EY Offers At EY, we’re dedicated to helping our clients, from start–ups to Fortune 500 companies — and the work we do with them is as varied as they are. You get to work with inspiring and meaningful projects. Our focus is education and coaching alongside practical experience to ensure your personal development. We value our employees and you will be able to control your own development with an individual progression plan. You will quickly grow into a responsible role with challenging and stimulating assignments. Moreover, you will be part of an interdisciplinary environment that emphasizes high quality and knowledge exchange. Plus, we offer: • Support, coaching and feedback from some of the most engaging colleagues around • Opportunities to develop new skills and progress your career • The freedom and flexibility to handle your role in a way that’s right for you EY | Building a better working world EY exists to build a better working world, helping to create long-term value for clients, people and society and build trust in the capital markets. Enabled by data and technology, diverse EY teams in over 150 countries provide trust through assurance and help clients grow, transform and operate. Working across assurance, consulting, law, strategy, tax and transactions, EY teams ask better questions to find new answers for the complex issues facing our world today.
Skills from this JD
Each row merges API 1 extraction, API 2 library match / v3 orchestration (dimensions + locked dims), and API 3 persistence tags.
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) |
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Other
- Sub-category
- general
- Skill nature
- TOOL
- 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
- Other
- Sub-category
- general
- Skill nature
- TOOL
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Aliases — catalog
- Azure Synapse Analytics (CANONICAL) primary
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Service
- Sub-category
- Analytics Service
- Vendor
- Microsoft
- License
- proprietary
- Year introduced
- 2019
- Confidence
- 0.95
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Common in cloud data-platform JDs and Microsoft’s Azure analytics stack; often listed alongside Databricks/ADF for warehousing and ETL, indicating broad hiring demand.
Skill profile (library / DB)
- Skill nature
- CLOUD_SERVICE
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 11
- Sub-category id
- 117
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
Cloud Data Warehouses Catalog dimension db id 22
Library dimension (catalog)
Roles linked in library: Data Engineer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Cloud Data Warehouses
cloud-data-warehouses
|
— | — |
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
- Other
- Sub-category
- general
- Skill nature
- TOOL
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Aliases — catalog
- Apache Spark (CANONICAL)
- apache spark 3 (VERSION)
- spark (VERSION)
- spark 3 (VERSION)
- spark 3.x (VERSION)
- spark3 (VERSION)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Framework
- Sub-category
- Distributed Data Processing Framework
- Vendor
- Apache Software Foundation
- License
- apache_2
- Year introduced
- 2010
- Confidence
- 0.94
- Version strategy
- SEPARATE_ENTITY
- Version tag
- 3.x
Maturity reasoning: Apache Spark appears in many data engineering JDs and remains a standard for distributed ETL/ELT; its GitHub and vendor ecosystem activity stay strong, with Databricks and cloud platforms still promoting it.
Skill profile (library / DB)
- Skill nature
- FRAMEWORK
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 5
- Sub-category id
- 1021
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
ETL and ELT Tooling Catalog dimension db id 24
Library dimension (catalog)
Roles linked in library: Data Engineer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
ETL and ELT Tooling
etl-and-elt-tooling
|
— | — |
Skipped — no persistable v3 meta for new skill
skill_not_in_db_v3_proposed
|
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Other
- Sub-category
- general
- Skill nature
- TOOL
- 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
- Other
- Sub-category
- general
- Skill nature
- TOOL
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Aliases — catalog
- Power BI (CANONICAL) primary
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Platform
- Sub-category
- Bi Analytics Platform
- Vendor
- Microsoft
- License
- proprietary
- Year introduced
- 2015
- Confidence
- 0.96
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Power BI appears frequently in BI/data analyst job descriptions and is a standard Microsoft analytics platform in enterprise stacks, with strong vendor support and broad adoption.
Skill profile (library / DB)
- Skill nature
- PLATFORM
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 9
- Sub-category id
- 111
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
BI and Visualization Tools Catalog dimension db id 31
Library dimension (catalog)
Roles linked in library: Data Engineer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
BI and Visualization Tools
bi-and-visualization-tools
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved |
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Other
- Sub-category
- general
- Skill nature
- TOOL
- 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
- Other
- Sub-category
- general
- Skill nature
- TOOL
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Aliases — catalog
- Azure Functions (CANONICAL) primary
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Service
- Sub-category
- Serverless Compute Service
- Vendor
- Microsoft
- License
- proprietary
- Year introduced
- 2016
- Confidence
- 0.99
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Broadly listed in cloud/serverless job descriptions and Microsoft actively supports it as a core Azure service; it’s a common hiring-pipeline skill for event-driven apps and APIs.
Skill profile (library / DB)
- Skill nature
- CLOUD_SERVICE
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 11
- Sub-category id
- 1097
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
Cloud Platforms & Hosting Providers Catalog dimension db id 278
Library dimension (catalog)
Roles linked in library: .NET Backend Developer, Kotlin Backend Developer, Scala Backend Developer, Web 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
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Cloud Platforms & Hosting Providers
cloud-platforms-hosting-providers
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Cloud Platforms & Managed Services
cloud-platforms-managed-services
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Other
- Sub-category
- general
- Skill nature
- TOOL
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Aliases — catalog
- Python (CANONICAL) primary
- Python 2 (VERSION)
- Python 2.x (VERSION)
- Python 3 (VERSION)
- Python 3.10 (VERSION)
- Python 3.11 (VERSION)
- Python 3.12 (VERSION)
- Python 3.x (VERSION)
- py (VERSION)
- py2 (VERSION)
- py3 (VERSION)
- python 3 (VERSION)
- python 3.x (VERSION)
- python2 (VERSION)
- python3 (VERSION)
- python3.x (VERSION)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Language
- Sub-category
- Programming Language
- Vendor
- PSF
- License
- mit
- Year introduced
- 1991
- Confidence
- 0.99
- Version strategy
- SEPARATE_ENTITY
- Version tag
- 3
Maturity reasoning: Python appears in a very high volume of job descriptions across data, backend, automation, and ML roles, and remains a default hiring-pipeline language on major job boards and tech stacks.
Skill profile (library / DB)
- Skill nature
- LANGUAGE
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 6
- Sub-category id
- 96
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
Cloud Security Scripting & DSL Languages Catalog dimension db id 248
Library dimension (catalog)
Roles linked in library: Cloud Security Engineer
-
Programming Languages Catalog dimension db id 1
Library dimension (catalog)
Roles linked in library: Backend Developer, Fullstack Developer, Fullstack Developer
-
Programming Languages & DSLs Catalog dimension db id 475
Library dimension (catalog)
Roles linked in library: Engineering Manager
-
Programming Languages and Scripting Catalog dimension db id 59
Library dimension (catalog)
Roles linked in library: Cyber Security Engineer
-
Programming Languages for Data Work Catalog dimension db id 21
Library dimension (catalog)
Roles linked in library: Data Engineer
-
Programming Languages for ML Systems Catalog dimension db id 39
Library dimension (catalog)
Roles linked in library: ML Engineer, MLOps Engineer
-
Programming Languages for XR Catalog dimension db id 97
Library dimension (catalog)
Roles linked in library: AR/VR Engineer
-
Python Programming Catalog dimension db id 290
Library dimension (catalog)
Roles linked in library: Python Backend Developer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Cloud Security Scripting & DSL Languages
cloud-security-scripting-dsl-languages
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Programming Languages
programming-languages
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Programming Languages & DSLs
programming-languages-dsls
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Programming Languages and Scripting
programming-languages-and-scripting
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Programming Languages for Data Work
programming-languages-for-data-work
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved |
|
Programming Languages for ML Systems
programming-languages-for-ml-systems
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Programming Languages for XR
programming-languages-for-xr
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Python Programming
python-programming
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Other
- Sub-category
- general
- Skill nature
- TOOL
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Aliases — catalog
- APIs (CANONICAL)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Protocol
- Sub-category
- Application Programming Interfaces
- Confidence
- 0.93
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: APIs are a hiring-pipeline staple across backend, mobile, and platform JDs; REST/GraphQL/API design appears in large volumes of job postings and vendor docs, indicating broad adoption.
Skill profile (library / DB)
- Skill nature
- PROTOCOL
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 10
- Sub-category id
- 902
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
React Frontend Development Catalog dimension db id 96
Library dimension (catalog)
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
React Frontend Development
d_init_01
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Other
- Sub-category
- general
- Skill nature
- TOOL
- 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
- Other
- Sub-category
- general
- Skill nature
- TOOL
- 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
- Other
- Sub-category
- general
- Skill nature
- TOOL
- 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
- Other
- Sub-category
- general
- Skill nature
- TOOL
- 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
- Other
- Sub-category
- general
- Skill nature
- TOOL
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Aliases — catalog
- domain modeling (CANONICAL) primary
- Domain Modeling (CANONICAL)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Methodology
- Sub-category
- Domain Modeling
- Confidence
- 0.90
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Common in software JDs under DDD/business analysis; many roles ask for domain modeling or domain-driven design, and it remains a standard design skill rather than a niche tool.
Skill profile (library / DB)
- Skill nature
- METHODOLOGY
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 8
- Sub-category id
- 2831
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
Application Architecture Patterns Catalog dimension db id 293
Library dimension (catalog)
Roles linked in library: .NET Backend Developer, Python Backend Developer
-
Service Architecture and Design Patterns Catalog dimension db id 18
Library dimension (catalog)
Roles linked in library: Backend Developer, Java Backend Developer, Kotlin Backend Developer, Node.js Backend Developer, PHP Backend Developer, Ruby Backend Developer, Scala Backend Developer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Application Architecture Patterns
application-architecture-patterns
|
— | — |
Skipped — no persistable v3 meta for new skill
skill_not_in_db_v3_proposed
|
|
Service Architecture and Design Patterns
service-architecture-and-design-patterns
|
— | — |
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
- Other
- Sub-category
- general
- Skill nature
- TOOL
- 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
- Other
- Sub-category
- general
- Skill nature
- TOOL
- 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
- Other
- Sub-category
- general
- Skill nature
- TOOL
- 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
- Other
- Sub-category
- general
- Skill nature
- TOOL
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Aliases — catalog
- Event-Driven Architecture (CANONICAL)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Architecture
- Sub-category
- Event Driven Architecture
- Confidence
- 0.99
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Common in cloud-native JDs and vendor docs; AWS, Azure, and Confluent all market event-driven patterns with Kafka/PubSub, showing broad hiring demand.
Skill profile (library / DB)
- Skill nature
- PATTERN
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 1
- Sub-category id
- 1027
- 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
- Analytics (CANONICAL)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Domain
- Sub-category
- Analytics
- Confidence
- 0.94
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Analytics appears in a large share of data, product, and BI job descriptions, and major vendors (Google Analytics, Adobe Analytics, Power BI) continue to invest heavily in the category.
Skill profile (library / DB)
- Skill nature
- CONCEPT
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 37
- Sub-category id
- 1257
- 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) |
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Other
- Sub-category
- general
- Skill nature
- TOOL
- 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
- Other
- Sub-category
- general
- Skill nature
- TOOL
- 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
- Other
- Sub-category
- general
- Skill nature
- TOOL
- 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
- Other
- Sub-category
- general
- Skill nature
- TOOL
- 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
- Other
- Sub-category
- general
- Skill nature
- TOOL
- 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
- Other
- Sub-category
- general
- Skill nature
- TOOL
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
All API 3 persistence rows
Same grid as the skill-extractor “Persistence items” table: one row per (skill × dimension) work item.
| Skill | Tag | Dimension | Skill↔dim | Role↔dim | Outcome | Notes |
|---|---|---|---|---|---|---|
| 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) | |
| Azure Synapse | new |
Cloud Data Warehouses
cloud-data-warehouses
|
— | — | Skipped — no persistable v3 meta for new skill | skill_not_in_db_v3_proposed |
| PySpark | new |
ETL and ELT Tooling
etl-and-elt-tooling
|
— | — | Skipped — no persistable v3 meta for new skill | skill_not_in_db_v3_proposed |
| Power BI | in_db |
BI and Visualization Tools
bi-and-visualization-tools
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved | |
| Azure Functions | in_db |
Cloud Platforms & Hosting Providers
cloud-platforms-hosting-providers
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Azure Functions | in_db |
Cloud Platforms & Managed Services
cloud-platforms-managed-services
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Python | in_db |
Cloud Security Scripting & DSL Languages
cloud-security-scripting-dsl-languages
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Python | in_db |
Programming Languages
programming-languages
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Python | in_db |
Programming Languages & DSLs
programming-languages-dsls
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Python | in_db |
Programming Languages and Scripting
programming-languages-and-scripting
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Python | in_db |
Programming Languages for Data Work
programming-languages-for-data-work
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved | |
| Python | in_db |
Programming Languages for ML Systems
programming-languages-for-ml-systems
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Python | in_db |
Programming Languages for XR
programming-languages-for-xr
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Python | in_db |
Python Programming
python-programming
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| APIs | in_db |
React Frontend Development
d_init_01
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Data Modeling | new |
Application Architecture Patterns
application-architecture-patterns
|
— | — | Skipped — no persistable v3 meta for new skill | skill_not_in_db_v3_proposed |
| Data Modeling | new |
Service Architecture and Design Patterns
service-architecture-and-design-patterns
|
— | — | Skipped — no persistable v3 meta for new skill | skill_not_in_db_v3_proposed |
| Event-Driven Architecture | in_db |
React Frontend Development
d_init_01
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Analytics | in_db |
React Frontend Development
d_init_01
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Library artifacts (this run)
| Kind | Detail | DB id |
|---|---|---|
| canonical_skill_proposed | Microsoft Fabric | type=Other subtype=general nature=TOOL lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Azure Data Factory | type=Other subtype=general nature=TOOL lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Azure Databricks | type=Other subtype=general nature=TOOL lifespan=MULTI_YEAR | |
| canonical_skill_proposed | ADLS | type=Other subtype=general nature=TOOL lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Azure SQL | type=Other subtype=general nature=TOOL lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Azure Event Hub | type=Other subtype=general nature=TOOL lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Azure Stream Analytics | type=Other subtype=general nature=TOOL lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Logic Apps | type=Other subtype=general nature=TOOL lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Dataverse | type=Other subtype=general nature=TOOL lifespan=MULTI_YEAR | |
| canonical_skill_proposed | ETL | type=Other subtype=general nature=TOOL lifespan=MULTI_YEAR | |
| canonical_skill_proposed | ELT | type=Other subtype=general nature=TOOL lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Batch Processing | type=Other subtype=general nature=TOOL lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Real-time Processing | type=Other subtype=general nature=TOOL lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Data Integration | type=Other subtype=general nature=TOOL lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Data Governance | type=Other subtype=general nature=TOOL lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Data Quality | type=Other subtype=general nature=TOOL lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Data Security | type=Other subtype=general nature=TOOL lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Streaming | type=Other subtype=general nature=TOOL lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Reporting | type=Other subtype=general nature=TOOL lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Orchestration | type=Other subtype=general nature=TOOL lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Big Data | type=Other subtype=general nature=TOOL lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Data Manipulation | type=Other subtype=general nature=TOOL lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Scripting | type=Other subtype=general nature=TOOL lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Project Management | type=Other subtype=general nature=TOOL lifespan=MULTI_YEAR | |
| dimension_skill_link_proposed | Azure Synapse ↔ Cloud Data Warehouses | |
| role_dimension_link_proposed | Data Engineer ↔ Cloud Data Warehouses | |
| dimension_skill_link_proposed | PySpark ↔ ETL and ELT Tooling | |
| role_dimension_link_proposed | Data Engineer ↔ ETL and ELT Tooling | |
| dimension_skill_link_proposed | Data Modeling ↔ Application Architecture Patterns | |
| dimension_skill_link_proposed | Data Modeling ↔ Service Architecture and Design Patterns |
nano JD Parser — gpt-4.1-nano click to toggle
Certifications
Show raw JSON
{
"JD_type": "pass",
"about_company": {
"source_marker": {
"first_5_words": "EY exists to build a",
"last_5_words": "facing our world today."
},
"text": "EY exists to build a better working world, helping to create long-term value for clients, people and society and build trust in the capital markets.\n\nEnabled by data and technology, diverse EY teams in over 150 countries provide trust through assurance and help clients grow, transform and operate.\n\nWorking across assurance, consulting, law, strategy, tax and transactions, EY teams ask better questions to find new answers for the complex issues facing our world today.",
"word_count": 64
},
"certifications": [
"DP-203: Data Engineering on Microsoft Azure.",
"DP-600/DP -700: Fabric Data Analyst/ Fabric Data engineer",
"Databricks Data Engineer Associate or Professional Certification.",
"Databricks Certified Associate Developer for Apache Spark."
],
"company_name": "EY",
"ctc": null,
"domain": {
"primary": {
"aliases": [
"ITES",
"BPO",
"Management Consulting"
],
"domain": "IT Services \u0026 Consulting"
},
"secondary": null
},
"education": [
{
"level": "Bachelor\u0027s",
"qualification": "BTECH/BE/BSC - Computer Science",
"raw": "Bachelor\u2019s or Master\u2019s degree in Computer Science, Data Engineering, or a related field.",
"requirement": "required"
}
],
"experience": {
"max": 8,
"min": 3,
"raw": "3.5-8 years of hands-on experience in Azure data engineering."
},
"job_locations": [],
"role": "Azure Data Architect - Azure Lead Data Engineer \u2013 Consulting",
"role_aliases": [
"Data Architect",
"Lead Data Engineer",
"Azure Data Engineer"
],
"role_archetype": "Data",
"roles_and_responsibilities": [
{
"bullet_count": 14,
"heading": "Your Key Responsibilities",
"heading_was_present": true,
"source_marker": {
"first_5_words": "\u2022 Design and implement scalable",
"last_5_words": "and lead technical discussions within"
},
"text": "\u2022 Design and implement scalable and efficient data architectures on Azure/Microsoft Fabric for batch and real-time processing.\n\u2022 Develop and optimize ETL/ELT pipelines using Azure Data Factory, Azure Synapse, and Azure Data bricks or Microsoft Fabric.\n\u2022 Build and maintain data integration solutions leveraging PySpark and ADLS.\n\u2022 Implement data models and data storage solutions in Azure SQL and Synapse for analytics and reporting.\n\u2022 Manage data ingestion from various sources, including APIs, databases, and streaming platforms.\n\u2022 Ensure data quality, consistency, and security across the platform.\n\u2022 Design and develop real-time data processing solutions using Azure Event Hub, Azure Stream Analytics, and Azure Functions.\n\u2022 Enable seamless integration for streaming data pipelines and event-driven architectures.\n\u2022 Work closely with data scientists, analysts, and business stakeholders to understand data requirements.\n\u2022 Translate business needs into technical solutions and provide data engineering expertise to the team.\n\u2022 Optimize data pipelines for performance, scalability, and cost-efficiency.\n\u2022 Implement and enforce best practices for data governance, security, and compliance.\n\u2022 Stay updated with the latest Azure data services and technologies to drive innovation.\n\u2022 Mentor junior engineers and lead technical discussions within the team.",
"word_count": 203
},
{
"bullet_count": 16,
"heading": "Skills And Attributes For Success",
"heading_was_present": true,
"source_marker": {
"first_5_words": "\u2022 Collaborating with other members",
"last_5_words": "with engagement team members."
},
"text": "\u2022 Collaborating with other members of the engagement team to plan the engagement and develop work program timelines, risk assessments and other documents/templates.\n\u2022 Able to manage Senior stakeholders.\n\u2022 Experience in leading teams to execute high quality deliverables within stipulated timeline.\n\u2022 Must-Have Skills:\n\u2022 Expertise to design and develop integration and reporting solution in Power BI/Power BI Fabric\n\u2022 Proficiency in Azure Databricks or Azure Synapse for data processing and analytics.\n\u2022 Expertise in PySpark for big data processing and transformations.\n\u2022 Strong experience with Azure Data Factory (ADF) for orchestration and ETL/ELT.\n\u2022 Solid knowledge of Azure SQL and ADLS for data storage and querying.\n\u2022 Good-to-Have Skills:\n\u2022 Familiarity with Azure Event Hub and Azure Stream Analytics for real-time data processing.\n\u2022 Experience with Azure Functions and Logic Apps for event-driven workflows.\n\u2022 Knowledge of Python for data manipulation and scripting.\n\u2022 Understanding of Dataverse for data integration.\n\u2022 Excellent Written and Communication Skills\n\u2022 Ability to deliver technical demonstrations\n\u2022 Quick learner with \u201ccan do\u201d attitude\n\u2022 Demonstrating and applying strong project management skills, inspiring teamwork and responsibility with engagement team members.",
"word_count": 290
}
],
"urls": []
}
API 1 — extract-from-jd click to toggle
{
"final_skills": [
{
"is_primary": true,
"skill_name": "Azure"
},
{
"is_primary": true,
"skill_name": "Microsoft Fabric"
},
{
"is_primary": true,
"skill_name": "Azure Data Factory"
},
{
"is_primary": true,
"skill_name": "Azure Synapse"
},
{
"is_primary": true,
"skill_name": "Azure Databricks"
},
{
"is_primary": true,
"skill_name": "PySpark"
},
{
"is_primary": true,
"skill_name": "ADLS"
},
{
"is_primary": true,
"skill_name": "Azure SQL"
},
{
"is_primary": true,
"skill_name": "Power BI"
},
{
"is_primary": false,
"skill_name": "Azure Event Hub"
},
{
"is_primary": false,
"skill_name": "Azure Stream Analytics"
},
{
"is_primary": false,
"skill_name": "Azure Functions"
},
{
"is_primary": false,
"skill_name": "Logic Apps"
},
{
"is_primary": false,
"skill_name": "Python"
},
{
"is_primary": false,
"skill_name": "Dataverse"
},
{
"is_primary": false,
"skill_name": "APIs"
},
{
"is_primary": true,
"skill_name": "ETL"
},
{
"is_primary": true,
"skill_name": "ELT"
},
{
"is_primary": false,
"skill_name": "Batch Processing"
},
{
"is_primary": false,
"skill_name": "Real-time Processing"
},
{
"is_primary": true,
"skill_name": "Data Integration"
},
{
"is_primary": true,
"skill_name": "Data Modeling"
},
{
"is_primary": false,
"skill_name": "Data Governance"
},
{
"is_primary": false,
"skill_name": "Data Quality"
},
{
"is_primary": false,
"skill_name": "Data Security"
},
{
"is_primary": false,
"skill_name": "Streaming"
},
{
"is_primary": false,
"skill_name": "Event-Driven Architecture"
},
{
"is_primary": false,
"skill_name": "Analytics"
},
{
"is_primary": false,
"skill_name": "Reporting"
},
{
"is_primary": false,
"skill_name": "Orchestration"
},
{
"is_primary": false,
"skill_name": "Big Data"
},
{
"is_primary": false,
"skill_name": "Data Manipulation"
},
{
"is_primary": false,
"skill_name": "Scripting"
},
{
"is_primary": false,
"skill_name": "Project Management"
}
],
"jd_role": {
"display_name": "Azure Data Architect - Azure Lead Data Engineer \u2013 Consulting",
"rationale": null,
"role_aliases": [
"Data Architect",
"Lead Data Engineer",
"Azure Data Engineer"
],
"role_archetype": "Data",
"slug": ""
},
"nano_parsed": {
"JD_type": "pass",
"about_company": {
"source_marker": {
"first_5_words": "EY exists to build a",
"last_5_words": "facing our world today."
},
"text": "EY exists to build a better working world, helping to create long-term value for clients, people and society and build trust in the capital markets.\n\nEnabled by data and technology, diverse EY teams in over 150 countries provide trust through assurance and help clients grow, transform and operate.\n\nWorking across assurance, consulting, law, strategy, tax and transactions, EY teams ask better questions to find new answers for the complex issues facing our world today.",
"word_count": 64
},
"certifications": [
"DP-203: Data Engineering on Microsoft Azure.",
"DP-600/DP -700: Fabric Data Analyst/ Fabric Data engineer",
"Databricks Data Engineer Associate or Professional Certification.",
"Databricks Certified Associate Developer for Apache Spark."
],
"company_name": "EY",
"ctc": null,
"domain": {
"primary": {
"aliases": [
"ITES",
"BPO",
"Management Consulting"
],
"domain": "IT Services \u0026 Consulting"
},
"secondary": null
},
"education": [
{
"level": "Bachelor\u0027s",
"qualification": "BTECH/BE/BSC - Computer Science",
"raw": "Bachelor\u2019s or Master\u2019s degree in Computer Science, Data Engineering, or a related field.",
"requirement": "required"
}
],
"experience": {
"max": 8,
"min": 3,
"raw": "3.5-8 years of hands-on experience in Azure data engineering."
},
"job_locations": [],
"role": "Azure Data Architect - Azure Lead Data Engineer \u2013 Consulting",
"role_aliases": [
"Data Architect",
"Lead Data Engineer",
"Azure Data Engineer"
],
"role_archetype": "Data",
"roles_and_responsibilities": [
{
"bullet_count": 14,
"heading": "Your Key Responsibilities",
"heading_was_present": true,
"source_marker": {
"first_5_words": "\u2022 Design and implement scalable",
"last_5_words": "and lead technical discussions within"
},
"text": "\u2022 Design and implement scalable and efficient data architectures on Azure/Microsoft Fabric for batch and real-time processing.\n\u2022 Develop and optimize ETL/ELT pipelines using Azure Data Factory, Azure Synapse, and Azure Data bricks or Microsoft Fabric.\n\u2022 Build and maintain data integration solutions leveraging PySpark and ADLS.\n\u2022 Implement data models and data storage solutions in Azure SQL and Synapse for analytics and reporting.\n\u2022 Manage data ingestion from various sources, including APIs, databases, and streaming platforms.\n\u2022 Ensure data quality, consistency, and security across the platform.\n\u2022 Design and develop real-time data processing solutions using Azure Event Hub, Azure Stream Analytics, and Azure Functions.\n\u2022 Enable seamless integration for streaming data pipelines and event-driven architectures.\n\u2022 Work closely with data scientists, analysts, and business stakeholders to understand data requirements.\n\u2022 Translate business needs into technical solutions and provide data engineering expertise to the team.\n\u2022 Optimize data pipelines for performance, scalability, and cost-efficiency.\n\u2022 Implement and enforce best practices for data governance, security, and compliance.\n\u2022 Stay updated with the latest Azure data services and technologies to drive innovation.\n\u2022 Mentor junior engineers and lead technical discussions within the team.",
"word_count": 203
},
{
"bullet_count": 16,
"heading": "Skills And Attributes For Success",
"heading_was_present": true,
"source_marker": {
"first_5_words": "\u2022 Collaborating with other members",
"last_5_words": "with engagement team members."
},
"text": "\u2022 Collaborating with other members of the engagement team to plan the engagement and develop work program timelines, risk assessments and other documents/templates.\n\u2022 Able to manage Senior stakeholders.\n\u2022 Experience in leading teams to execute high quality deliverables within stipulated timeline.\n\u2022 Must-Have Skills:\n\u2022 Expertise to design and develop integration and reporting solution in Power BI/Power BI Fabric\n\u2022 Proficiency in Azure Databricks or Azure Synapse for data processing and analytics.\n\u2022 Expertise in PySpark for big data processing and transformations.\n\u2022 Strong experience with Azure Data Factory (ADF) for orchestration and ETL/ELT.\n\u2022 Solid knowledge of Azure SQL and ADLS for data storage and querying.\n\u2022 Good-to-Have Skills:\n\u2022 Familiarity with Azure Event Hub and Azure Stream Analytics for real-time data processing.\n\u2022 Experience with Azure Functions and Logic Apps for event-driven workflows.\n\u2022 Knowledge of Python for data manipulation and scripting.\n\u2022 Understanding of Dataverse for data integration.\n\u2022 Excellent Written and Communication Skills\n\u2022 Ability to deliver technical demonstrations\n\u2022 Quick learner with \u201ccan do\u201d attitude\n\u2022 Demonstrating and applying strong project management skills, inspiring teamwork and responsibility with engagement team members.",
"word_count": 290
}
],
"urls": []
},
"rejected": false,
"rejection_reason": null,
"run_id": "4f719af0-a7cb-41bd-9a42-3910f47ccec1",
"stage3_signals": {
"alias_found": false,
"alias_match_roles": [],
"kra_match_roles": [
{
"display_name": "Data Engineer",
"kra_matches": [
{
"kra_text": "Works with data analysts, data scientists, and business stakeholders to define data models, ingestion schedules, and data delivery requirements.",
"sentence": "Work closely with data scientists, analysts, and business stakeholders to understand data requirements.",
"similarity": 0.7773
},
{
"kra_text": "Builds data ingestion pipelines to collect data from transactional databases, third-party APIs, event streams, and file sources into centralized data platforms.",
"sentence": "Manage data ingestion from various sources, including APIs, databases, and streaming platforms.",
"similarity": 0.7568
},
{
"kra_text": "Optimizes pipeline throughput, partitioning strategies, and query performance across cloud data warehouses like Snowflake, BigQuery, or Redshift.",
"sentence": "Optimize data pipelines for performance, scalability, and cost-efficiency.",
"similarity": 0.7062
}
],
"matched_count": null,
"matched_skills": null,
"role_id": 2,
"score": 0.7467,
"slug": "data-engineer",
"total_count": null
},
{
"display_name": "Flutter Developer",
"kra_matches": [
{
"kra_text": "integrate external APIs and data sources",
"sentence": "Manage data ingestion from various sources, including APIs, databases, and streaming platforms.",
"similarity": 0.596
},
{
"kra_text": "translate product and design requirements",
"sentence": "Translate business needs into technical solutions and provide data engineering expertise to the team.",
"similarity": 0.5429
},
{
"kra_text": "integrate external APIs and data sources",
"sentence": "Enable seamless integration for streaming data pipelines and event-driven architectures.",
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}
],
"matched_count": null,
"matched_skills": null,
"role_id": 74,
"score": 0.5465,
"slug": "flutter-developer",
"total_count": null
},
{
"display_name": "Svelte Frontend Developer",
"kra_matches": [
{
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},
{
"kra_text": "backend data integration",
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},
{
"kra_text": "backend data integration",
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"similarity": 0.5345
}
],
"matched_count": null,
"matched_skills": null,
"role_id": 92,
"score": 0.5442,
"slug": "svelte-frontend-developer",
"total_count": null
},
{
"display_name": "AI Engineer",
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{
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"similarity": 0.5614
},
{
"kra_text": "Integrates AI model API responses with application business logic, database writes, event publishing, and downstream service orchestration.",
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"similarity": 0.526
},
{
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}
],
"matched_count": null,
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"role_id": 13,
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},
{
"display_name": "Backend Developer",
"kra_matches": [
{
"kra_text": "Integrates with third-party services, payment gateways, messaging queues like Kafka or RabbitMQ, and internal microservices via HTTP and event-driven patterns.",
"sentence": "Enable seamless integration for streaming data pipelines and event-driven architectures.",
"similarity": 0.569
},
{
"kra_text": "Integrates with third-party services, payment gateways, messaging queues like Kafka or RabbitMQ, and internal microservices via HTTP and event-driven patterns.",
"sentence": "Manage data ingestion from various sources, including APIs, databases, and streaming platforms.",
"similarity": 0.5248
},
{
"kra_text": "Identifies and resolves backend performance bottlenecks through query optimization, indexing strategies, connection pooling, and distributed caching with Redis.",
"sentence": "Optimize data pipelines for performance, scalability, and cost-efficiency.",
"similarity": 0.505
}
],
"matched_count": null,
"matched_skills": null,
"role_id": 1,
"score": 0.5329,
"slug": "backend-engineer",
"total_count": null
}
],
"skill_match_roles": [
{
"display_name": "Data Engineer",
"kra_matches": null,
"matched_count": 2,
"matched_skills": [
"Azure",
"Power BI"
],
"role_id": 2,
"score": 0.1538,
"slug": "data-engineer",
"total_count": 13
},
{
"display_name": "ML Engineer",
"kra_matches": null,
"matched_count": 1,
"matched_skills": [
"Azure"
],
"role_id": 3,
"score": 0.0769,
"slug": "ml-engineer",
"total_count": 13
},
{
"display_name": "Cyber Security Engineer",
"kra_matches": null,
"matched_count": 1,
"matched_skills": [
"Azure"
],
"role_id": 5,
"score": 0.0769,
"slug": "cybersecurity-engineer",
"total_count": 13
},
{
"display_name": "Cloud Architect",
"kra_matches": null,
"matched_count": 1,
"matched_skills": [
"Azure"
],
"role_id": 9,
"score": 0.0769,
"slug": "cloud-architect",
"total_count": 13
},
{
"display_name": "Backend Developer",
"kra_matches": null,
"matched_count": 1,
"matched_skills": [
"Azure"
],
"role_id": 1,
"score": 0.0769,
"slug": "backend-engineer",
"total_count": 13
}
]
},
"stage4_decision": {
"alias_collision_detected": false,
"case": "DOMAIN",
"chosen_role": {
"display_name": "Data Engineer",
"kra_matches": null,
"matched_count": null,
"matched_skills": null,
"role_id": 2,
"score": 0.98,
"slug": "data-engineer",
"total_count": null
},
"confidence": 0.98,
"is_new_role": false,
"llm2_fired": false,
"llm2_reasoning": null,
"matched_dimensions": [
"Azure data architecture",
"ETL/ELT pipeline engineering",
"Data integration and ingestion",
"Real-time and streaming data processing",
"Data modeling and analytics data storage",
"Data quality, governance, security, and compliance",
"Performance and cost optimization",
"Technical leadership and stakeholder management"
],
"matched_kras": [
"Design and implement scalable and efficient data architectures",
"Develop and optimize ETL/ELT pipelines",
"Build and maintain data integration solutions",
"Implement data models and data storage solutions",
"Manage data ingestion from various sources",
"Ensure data quality, consistency, and security",
"Design and develop real-time data processing solutions",
"Optimize data pipelines for performance, scalability, and cost-efficiency",
"Implement and enforce best practices for data governance",
"Mentor junior engineers and lead technical discussions"
],
"matched_skills": [
"Azure",
"Microsoft Fabric",
"Azure Data Factory",
"Azure Synapse",
"Azure Databricks",
"PySpark",
"ADLS",
"Azure SQL",
"Azure Event Hub",
"Azure Stream Analytics",
"Azure Functions",
"Power BI",
"Logic Apps",
"Python",
"Dataverse"
],
"new_role_display_name": null,
"new_role_slug": null,
"queued": false,
"reasoning": "Domain=Data Engineering \u0026 Analytics; The JD is centered on Azure-based data architecture, ETL/ELT, data integration, batch and real-time pipelines, and team leadership, which best matches a Data Engineer role.",
"sub_role": null
},
"stage5_updates": {
"centroid_n_after": 498,
"centroid_updated": true,
"collision_log_id": null,
"new_kra_attached": null,
"new_skills_attached": [
{
"is_primary": true,
"queue_id": 23108,
"role_display_name": "Data Engineer",
"role_slug": "data-engineer",
"skill_name": "Microsoft Fabric",
"status": "pending"
},
{
"is_primary": true,
"queue_id": 23109,
"role_display_name": "Data Engineer",
"role_slug": "data-engineer",
"skill_name": "Azure Data Factory",
"status": "pending"
},
{
"is_primary": true,
"queue_id": 23110,
"role_display_name": "Data Engineer",
"role_slug": "data-engineer",
"skill_name": "Azure Synapse",
"status": "pending"
},
{
"is_primary": true,
"queue_id": 23111,
"role_display_name": "Data Engineer",
"role_slug": "data-engineer",
"skill_name": "Azure Databricks",
"status": "pending"
},
{
"is_primary": true,
"queue_id": 23112,
"role_display_name": "Data Engineer",
"role_slug": "data-engineer",
"skill_name": "PySpark",
"status": "pending"
},
{
"is_primary": true,
"queue_id": 23113,
"role_display_name": "Data Engineer",
"role_slug": "data-engineer",
"skill_name": "ADLS",
"status": "pending"
},
{
"is_primary": true,
"queue_id": 23114,
"role_display_name": "Data Engineer",
"role_slug": "data-engineer",
"skill_name": "Azure SQL",
"status": "pending"
},
{
"is_primary": false,
"queue_id": 23115,
"role_display_name": "Data Engineer",
"role_slug": "data-engineer",
"skill_name": "Azure Event Hub",
"status": "pending"
},
{
"is_primary": false,
"queue_id": 23116,
"role_display_name": "Data Engineer",
"role_slug": "data-engineer",
"skill_name": "Azure Stream Analytics",
"status": "pending"
},
{
"is_primary": false,
"queue_id": 23117,
"role_display_name": "Data Engineer",
"role_slug": "data-engineer",
"skill_name": "Logic Apps",
"status": "pending"
},
{
"is_primary": false,
"queue_id": 23118,
"role_display_name": "Data Engineer",
"role_slug": "data-engineer",
"skill_name": "Dataverse",
"status": "pending"
},
{
"is_primary": true,
"queue_id": 23119,
"role_display_name": "Data Engineer",
"role_slug": "data-engineer",
"skill_name": "ETL",
"status": "pending"
},
{
"is_primary": true,
"queue_id": 23120,
"role_display_name": "Data Engineer",
"role_slug": "data-engineer",
"skill_name": "ELT",
"status": "pending"
},
{
"is_primary": false,
"queue_id": 23121,
"role_display_name": "Data Engineer",
"role_slug": "data-engineer",
"skill_name": "Batch Processing",
"status": "pending"
},
{
"is_primary": false,
"queue_id": 23122,
"role_display_name": "Data Engineer",
"role_slug": "data-engineer",
"skill_name": "Real-time Processing",
"status": "pending"
},
{
"is_primary": true,
"queue_id": 23123,
"role_display_name": "Data Engineer",
"role_slug": "data-engineer",
"skill_name": "Data Integration",
"status": "pending"
},
{
"is_primary": true,
"queue_id": 23124,
"role_display_name": "Data Engineer",
"role_slug": "data-engineer",
"skill_name": "Data Modeling",
"status": "pending"
},
{
"is_primary": false,
"queue_id": 23125,
"role_display_name": "Data Engineer",
"role_slug": "data-engineer",
"skill_name": "Data Governance",
"status": "pending"
},
{
"is_primary": false,
"queue_id": 23126,
"role_display_name": "Data Engineer",
"role_slug": "data-engineer",
"skill_name": "Data Quality",
"status": "pending"
},
{
"is_primary": false,
"queue_id": 23127,
"role_display_name": "Data Engineer",
"role_slug": "data-engineer",
"skill_name": "Data Security",
"status": "pending"
},
{
"is_primary": false,
"queue_id": 23128,
"role_display_name": "Data Engineer",
"role_slug": "data-engineer",
"skill_name": "Streaming",
"status": "pending"
},
{
"is_primary": false,
"queue_id": 23129,
"role_display_name": "Data Engineer",
"role_slug": "data-engineer",
"skill_name": "Reporting",
"status": "pending"
},
{
"is_primary": false,
"queue_id": 23130,
"role_display_name": "Data Engineer",
"role_slug": "data-engineer",
"skill_name": "Orchestration",
"status": "pending"
},
{
"is_primary": false,
"queue_id": 23131,
"role_display_name": "Data Engineer",
"role_slug": "data-engineer",
"skill_name": "Big Data",
"status": "pending"
},
{
"is_primary": false,
"queue_id": 23133,
"role_display_name": "Data Engineer",
"role_slug": "data-engineer",
"skill_name": "Data Manipulation",
"status": "pending"
},
{
"is_primary": false,
"queue_id": 23134,
"role_display_name": "Data Engineer",
"role_slug": "data-engineer",
"skill_name": "Scripting",
"status": "pending"
},
{
"is_primary": false,
"queue_id": 23137,
"role_display_name": "Data Engineer",
"role_slug": "data-engineer",
"skill_name": "Project Management",
"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": 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": "TODO: REMOVE AFTER TESTING \u2014 alias DB write disabled",
"alias_persisted": false,
"existing_alias_id": 302,
"existing_alias_text": "Azure Synapse Analytics",
"input_term": "Azure Synapse",
"matched_canonical": {
"category_id": 11,
"display_name": "Azure Synapse Analytics",
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"is_also_category": false,
"is_extractable": true,
"skill_nature": "CLOUD_SERVICE",
"slug": "azure-synapse-analytics",
"sub_category_id": 117,
"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"matched_via": "embedding_alias"
},
{
"alias_persist_skipped_reason": "TODO: REMOVE AFTER TESTING \u2014 alias DB write disabled",
"alias_persisted": false,
"existing_alias_id": 2004,
"existing_alias_text": "Apache Spark",
"input_term": "PySpark",
"matched_canonical": {
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"display_name": "Apache Spark",
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"is_also_category": false,
"is_extractable": true,
"skill_nature": "FRAMEWORK",
"slug": "apache-spark",
"sub_category_id": 1021,
"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": 360,
"existing_alias_text": "Power BI",
"input_term": "Power BI",
"matched_canonical": {
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"display_name": "Power BI",
"id": 151,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "PLATFORM",
"slug": "power-bi",
"sub_category_id": 111,
"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"matched_via": "alias"
},
{
"alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
"alias_persisted": false,
"existing_alias_id": 2357,
"existing_alias_text": "Azure Functions",
"input_term": "Azure Functions",
"matched_canonical": {
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"display_name": "Azure Functions",
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"is_also_category": false,
"is_extractable": true,
"skill_nature": "CLOUD_SERVICE",
"slug": "azure-functions",
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"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"matched_via": "alias"
},
{
"alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
"alias_persisted": false,
"existing_alias_id": 67,
"existing_alias_text": "Python",
"input_term": "Python",
"matched_canonical": {
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"display_name": "Python",
"id": 5,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "LANGUAGE",
"slug": "python",
"sub_category_id": 96,
"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"matched_via": "alias"
},
{
"alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
"alias_persisted": false,
"existing_alias_id": 1828,
"existing_alias_text": "APIs",
"input_term": "APIs",
"matched_canonical": {
"category_id": 10,
"display_name": "APIs",
"id": 1192,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "PROTOCOL",
"slug": "apis",
"sub_category_id": 902,
"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": 5644,
"existing_alias_text": "Domain Modeling",
"input_term": "Data Modeling",
"matched_canonical": {
"category_id": 8,
"display_name": "domain modeling",
"id": 2379,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "METHODOLOGY",
"slug": "domain-modeling",
"sub_category_id": 2831,
"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": 2019,
"existing_alias_text": "Event-Driven Architecture",
"input_term": "Event-Driven Architecture",
"matched_canonical": {
"category_id": 1,
"display_name": "Event-Driven Architecture",
"id": 1360,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "PATTERN",
"slug": "event-driven-architecture",
"sub_category_id": 1027,
"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": 2634,
"existing_alias_text": "Analytics",
"input_term": "Analytics",
"matched_canonical": {
"category_id": 37,
"display_name": "Analytics",
"id": 1664,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "CONCEPT",
"slug": "analytics",
"sub_category_id": 1257,
"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"matched_via": "alias"
}
],
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"role_archetype": "Engineering",
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},
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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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"source": "db"
},
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"source": "db"
},
{
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"slug": "data-engineer",
"source": "db"
},
{
"display_name": "DevOps Engineer",
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"role_archetype": null,
"slug": "devops-engineer",
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},
{
"display_name": "Fullstack Developer",
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"slug": "full-stack-engineer",
"source": "db"
},
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"slug": "go-backend-developer",
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},
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"role_archetype": "Engineering",
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},
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"role_archetype": "Engineering",
"slug": "kotlin-server-backend-developer",
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},
{
"display_name": "ML Engineer",
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"role_archetype": null,
"slug": "ml-engineer",
"source": "db"
},
{
"display_name": "MLOps Engineer",
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"rationale": null,
"role_archetype": null,
"slug": "ml-ops-engineer",
"source": "db"
},
{
"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"
},
{
"display_name": "AI Engineer",
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"role_archetype": null,
"slug": "ai-engineer",
"source": "db"
},
{
"display_name": "Cloud Architect",
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"slug": "cloud-architect",
"source": "db"
},
{
"display_name": "Cloud Security Engineer",
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"slug": "cloud-security-engineer",
"source": "db"
},
{
"display_name": "Engineering Manager",
"id": 121,
"rationale": null,
"role_archetype": null,
"slug": "engineering-manager",
"source": "db"
},
{
"display_name": "Web Developer",
"id": 25,
"rationale": null,
"role_archetype": null,
"slug": "web-developer",
"source": "db"
},
{
"display_name": "Fullstack Developer",
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"role_archetype": "Engineering",
"slug": "fullstack-developer",
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},
{
"display_name": "AR/VR Engineer",
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"role_archetype": null,
"slug": "ar-vr-engineer",
"source": "db"
},
{
"display_name": "PHP Backend Developer",
"id": 86,
"rationale": null,
"role_archetype": "Engineering",
"slug": "php-backend-developer",
"source": "db"
},
{
"display_name": "Ruby Backend Developer",
"id": 85,
"rationale": null,
"role_archetype": "Engineering",
"slug": "ruby-backend-developer",
"source": "db"
}
],
"chosen_role": {
"display_name": "Data Engineer",
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"rationale": "Domain=Data Engineering \u0026 Analytics; The JD is centered on Azure-based data architecture, ETL/ELT, data integration, batch and real-time pipelines, and team leadership, which best matches a Data Engineer role.",
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"slug": "data-engineer",
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},
"dimensions": [
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Cloud Platforms",
"id": 20,
"rationale": "Underlying cloud providers that host the managed services or infrastructure used by the role, such as AWS, Azure, and GCP.",
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"source": "db"
},
"input_skill": "Azure",
"llm_role": null,
"roles_from_db": [
{
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"role_archetype": "Engineering",
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},
{
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},
{
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},
{
"display_name": "Data Engineer",
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"slug": "data-engineer",
"source": "db"
},
{
"display_name": "DevOps Engineer",
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},
{
"display_name": "Fullstack Developer",
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},
{
"display_name": "Go Backend Developer",
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"role_archetype": "Engineering",
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"source": "db"
},
{
"display_name": "Java Backend Developer",
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"role_archetype": "Engineering",
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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"
},
{
"display_name": "ML Engineer",
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"rationale": null,
"role_archetype": null,
"slug": "ml-engineer",
"source": "db"
},
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"skill_nature": "TOOL",
"sub_category": "general",
"typical_lifespan": "MULTI_YEAR",
"version_strategy": "UNVERSIONED",
"volatility": "MEDIUM"
},
"enrichment": null,
"keep_log": [],
"locked_dimensions": [],
"merge_log": [],
"placed": null,
"relationships": null,
"skill_id": "scripting",
"split_log": [],
"typed": null,
"warnings": []
},
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [],
"input_skill": "Project Management",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": {
"derived": {
"category": "Other",
"skill_nature": "TOOL",
"sub_category": "general",
"typical_lifespan": "MULTI_YEAR",
"version_strategy": "UNVERSIONED",
"volatility": "MEDIUM"
},
"enrichment": null,
"keep_log": [],
"locked_dimensions": [],
"merge_log": [],
"placed": null,
"relationships": null,
"skill_id": "project-management",
"split_log": [],
"typed": null,
"warnings": []
},
"source_tag": "llm",
"was_in_llm_skills": true
}
],
"unmatched_skills": [
"Microsoft Fabric",
"Azure Data Factory",
"Azure Databricks",
"ADLS",
"Azure SQL",
"Azure Event Hub",
"Azure Stream Analytics",
"Logic Apps",
"Dataverse",
"ETL",
"ELT",
"Batch Processing",
"Real-time Processing",
"Data Integration",
"Data Governance",
"Data Quality",
"Data Security",
"Streaming",
"Reporting",
"Orchestration",
"Big Data",
"Data Manipulation",
"Scripting",
"Project Management"
]
}
API 3 — final-role-output
{
"chosen_role": {
"display_name": "Data Engineer",
"id": 2,
"rationale": "Domain=Data Engineering \u0026 Analytics; The JD is centered on Azure-based data architecture, ETL/ELT, data integration, batch and real-time pipelines, and team leadership, which best matches a Data Engineer role.",
"role_archetype": null,
"slug": "data-engineer",
"source": "db"
},
"chosen_role_resolution": "in_db",
"final_input_skills": [
{
"skill": "Azure",
"tag": "in_db"
},
{
"skill": "Microsoft Fabric",
"tag": "new"
},
{
"skill": "Azure Data Factory",
"tag": "new"
},
{
"skill": "Azure Synapse",
"tag": "in_db"
},
{
"skill": "Azure Databricks",
"tag": "new"
},
{
"skill": "PySpark",
"tag": "in_db"
},
{
"skill": "ADLS",
"tag": "new"
},
{
"skill": "Azure SQL",
"tag": "new"
},
{
"skill": "Power BI",
"tag": "in_db"
},
{
"skill": "Azure Event Hub",
"tag": "new"
},
{
"skill": "Azure Stream Analytics",
"tag": "new"
},
{
"skill": "Azure Functions",
"tag": "in_db"
},
{
"skill": "Logic Apps",
"tag": "new"
},
{
"skill": "Python",
"tag": "in_db"
},
{
"skill": "Dataverse",
"tag": "new"
},
{
"skill": "APIs",
"tag": "in_db"
},
{
"skill": "ETL",
"tag": "new"
},
{
"skill": "ELT",
"tag": "new"
},
{
"skill": "Batch Processing",
"tag": "new"
},
{
"skill": "Real-time Processing",
"tag": "new"
},
{
"skill": "Data Integration",
"tag": "new"
},
{
"skill": "Data Modeling",
"tag": "in_db"
},
{
"skill": "Data Governance",
"tag": "new"
},
{
"skill": "Data Quality",
"tag": "new"
},
{
"skill": "Data Security",
"tag": "new"
},
{
"skill": "Streaming",
"tag": "new"
},
{
"skill": "Event-Driven Architecture",
"tag": "in_db"
},
{
"skill": "Analytics",
"tag": "in_db"
},
{
"skill": "Reporting",
"tag": "new"
},
{
"skill": "Orchestration",
"tag": "new"
},
{
"skill": "Big Data",
"tag": "new"
},
{
"skill": "Data Manipulation",
"tag": "new"
},
{
"skill": "Scripting",
"tag": "new"
},
{
"skill": "Project Management",
"tag": "new"
}
],
"llm_cost_api1_usd": null,
"llm_cost_api2_usd": null,
"llm_cost_api3_usd": null,
"llm_cost_total_usd": null,
"persistence": {
"items": [
{
"chosen_role_id": 2,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Cloud Platforms",
"id": 20,
"rationale": "Underlying cloud providers that host the managed services or infrastructure used by the role, such as AWS, Azure, and GCP.",
"slug": "cloud-platforms",
"source": "db"
},
"dimension_id": 20,
"input_skill": "Azure",
"llm_role": null,
"matched_chosen_role": true,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension saved",
"role_dimension_saved": true,
"roles_from_db": [
{
"display_name": ".NET Backend Developer",
"id": 83,
"rationale": null,
"role_archetype": "Engineering",
"slug": "dotnet-backend-developer",
"source": "db"
},
{
"display_name": "Backend Developer",
"id": 1,
"rationale": null,
"role_archetype": "A Backend Engineer designs, builds, and maintains the server-side logic and data handling that power applications and services. They focus on implementing reliable business functionality, integrating with other systems, and ensuring the backend is scalable, maintainable, and observable.",
"slug": "backend-engineer",
"source": "db"
},
{
"display_name": "Cyber Security Engineer",
"id": 5,
"rationale": null,
"role_archetype": null,
"slug": "cybersecurity-engineer",
"source": "db"
},
{
"display_name": "Data Engineer",
"id": 2,
"rationale": null,
"role_archetype": null,
"slug": "data-engineer",
"source": "db"
},
{
"display_name": "DevOps Engineer",
"id": 10,
"rationale": null,
"role_archetype": null,
"slug": "devops-engineer",
"source": "db"
},
{
"display_name": "Fullstack Developer",
"id": 15,
"rationale": null,
"role_archetype": null,
"slug": "full-stack-engineer",
"source": "db"
},
{
"display_name": "Go Backend Developer",
"id": 81,
"rationale": null,
"role_archetype": "Engineering",
"slug": "go-backend-developer",
"source": "db"
},
{
"display_name": "Java Backend Developer",
"id": 79,
"rationale": null,
"role_archetype": "Engineering",
"slug": "java-backend-developer",
"source": "db"
},
{
"display_name": "Kotlin Backend Developer",
"id": 84,
"rationale": null,
"role_archetype": "Engineering",
"slug": "kotlin-server-backend-developer",
"source": "db"
},
{
"display_name": "ML Engineer",
"id": 3,
"rationale": null,
"role_archetype": null,
"slug": "ml-engineer",
"source": "db"
},
{
"display_name": "MLOps Engineer",
"id": 16,
"rationale": null,
"role_archetype": null,
"slug": "ml-ops-engineer",
"source": "db"
},
{
"display_name": "Node.js Backend Developer",
"id": 82,
"rationale": null,
"role_archetype": "Engineering",
"slug": "node-backend-developer",
"source": "db"
},
{
"display_name": "Python Backend Developer",
"id": 80,
"rationale": null,
"role_archetype": "Engineering",
"slug": "python-backend-developer",
"source": "db"
},
{
"display_name": "Scala Backend Developer",
"id": 87,
"rationale": null,
"role_archetype": "Engineering",
"slug": "scala-backend-developer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 188,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 2,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Cloud Platforms \u0026 Managed Services",
"id": 221,
"rationale": "Operates and integrates vendor-specific cloud compute, storage, and hosting services.",
"slug": "cloud-platforms-managed-services",
"source": "db"
},
"dimension_id": 221,
"input_skill": "Azure",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "Fullstack Developer",
"id": 15,
"rationale": null,
"role_archetype": null,
"slug": "full-stack-engineer",
"source": "db"
},
{
"display_name": "Go Backend Developer",
"id": 81,
"rationale": null,
"role_archetype": "Engineering",
"slug": "go-backend-developer",
"source": "db"
},
{
"display_name": "Node.js Backend Developer",
"id": 82,
"rationale": null,
"role_archetype": "Engineering",
"slug": "node-backend-developer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 188,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 2,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Cloud Platforms for AI Deployment",
"id": 211,
"rationale": "Major cloud services that provide infrastructure and managed services for AI workloads.",
"slug": "cloud-platforms-for-ai-deployment",
"source": "db"
},
"dimension_id": 211,
"input_skill": "Azure",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "AI Engineer",
"id": 13,
"rationale": null,
"role_archetype": null,
"slug": "ai-engineer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 188,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 2,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Cloud Provider Platforms",
"id": 131,
"rationale": "Major cloud platforms and their core service ecosystems used to design target-state architectures, choose deployment boundaries, and evaluate managed capabilities. This is the primary substrate for cloud architecture decisions.",
"slug": "cloud-provider-platforms",
"source": "db"
},
"dimension_id": 131,
"input_skill": "Azure",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "Cloud Architect",
"id": 9,
"rationale": null,
"role_archetype": null,
"slug": "cloud-architect",
"source": "db"
},
{
"display_name": "Cloud Security Engineer",
"id": 23,
"rationale": null,
"role_archetype": null,
"slug": "cloud-security-engineer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 188,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 2,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Cloud Security Posture Tools",
"id": 64,
"rationale": "Cloud-native security platforms used to assess misconfiguration, workload exposure, and cloud control coverage. This dimension includes the major CNAPP/CSPM/CWPP vendors and cloud security services the role reviews and tunes.",
"slug": "cloud-security-posture-tools",
"source": "db"
},
"dimension_id": 64,
"input_skill": "Azure",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "Cloud Security Engineer",
"id": 23,
"rationale": null,
"role_archetype": null,
"slug": "cloud-security-engineer",
"source": "db"
},
{
"display_name": "Cyber Security Engineer",
"id": 5,
"rationale": null,
"role_archetype": null,
"slug": "cybersecurity-engineer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 188,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 2,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "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 Data Warehouses",
"id": 22,
"rationale": "Managed analytical storage and compute platforms used for curated datasets, reporting, and downstream analytics. These systems are central to data modeling, performance tuning, and cost-aware query design.",
"slug": "cloud-data-warehouses",
"source": "db"
},
"dimension_id": 22,
"input_skill": "Azure Synapse",
"llm_role": null,
"matched_chosen_role": true,
"outcome_line": "Skipped \u2014 no persistable v3 meta for new skill",
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "Data Engineer",
"id": 2,
"rationale": null,
"role_archetype": null,
"slug": "data-engineer",
"source": "db"
}
],
"skill_dimension_saved": false,
"skill_id": null,
"skill_tag": "new",
"skipped_reason": "skill_not_in_db_v3_proposed"
},
{
"chosen_role_id": 2,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "ETL and ELT Tooling",
"id": 24,
"rationale": "Packaged tools for extracting, loading, and transforming data across systems. This dimension covers connector-based ingestion, transformation frameworks, and managed integration products.",
"slug": "etl-and-elt-tooling",
"source": "db"
},
"dimension_id": 24,
"input_skill": "PySpark",
"llm_role": null,
"matched_chosen_role": true,
"outcome_line": "Skipped \u2014 no persistable v3 meta for new skill",
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "Data Engineer",
"id": 2,
"rationale": null,
"role_archetype": null,
"slug": "data-engineer",
"source": "db"
}
],
"skill_dimension_saved": false,
"skill_id": null,
"skill_tag": "new",
"skipped_reason": "skill_not_in_db_v3_proposed"
},
{
"chosen_role_id": 2,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "BI and Visualization Tools",
"id": 31,
"rationale": "Tools used to expose curated data to analysts and business users through dashboards, reports, and semantic exploration. Data engineers support these tools by shaping reliable datasets and performant models.",
"slug": "bi-and-visualization-tools",
"source": "db"
},
"dimension_id": 31,
"input_skill": "Power BI",
"llm_role": null,
"matched_chosen_role": true,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension saved",
"role_dimension_saved": true,
"roles_from_db": [
{
"display_name": "Data Engineer",
"id": 2,
"rationale": null,
"role_archetype": null,
"slug": "data-engineer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 151,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 2,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Cloud Platforms \u0026 Hosting Providers",
"id": 278,
"rationale": "Familiarity with vendor-specific hosting and backend services for deploying and scaling web applications.",
"slug": "cloud-platforms-hosting-providers",
"source": "db"
},
"dimension_id": 278,
"input_skill": "Azure Functions",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": ".NET Backend Developer",
"id": 83,
"rationale": null,
"role_archetype": "Engineering",
"slug": "dotnet-backend-developer",
"source": "db"
},
{
"display_name": "Kotlin Backend Developer",
"id": 84,
"rationale": null,
"role_archetype": "Engineering",
"slug": "kotlin-server-backend-developer",
"source": "db"
},
{
"display_name": "Scala Backend Developer",
"id": 87,
"rationale": null,
"role_archetype": "Engineering",
"slug": "scala-backend-developer",
"source": "db"
},
{
"display_name": "Web Developer",
"id": 25,
"rationale": null,
"role_archetype": null,
"slug": "web-developer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 1462,
"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 Functions",
"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": 1462,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 2,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Cloud Security Scripting \u0026 DSL Languages",
"id": 248,
"rationale": "Proficiency in programming and domain-specific languages used to automate and script cloud security controls.",
"slug": "cloud-security-scripting-dsl-languages",
"source": "db"
},
"dimension_id": 248,
"input_skill": "Python",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "Cloud Security Engineer",
"id": 23,
"rationale": null,
"role_archetype": null,
"slug": "cloud-security-engineer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 5,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 2,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Programming Languages",
"id": 1,
"rationale": "Primary implementation languages used to build client and server feature code. Full stack engineers need enough fluency to move across layers and implement product behavior end to end.",
"slug": "programming-languages",
"source": "db"
},
"dimension_id": 1,
"input_skill": "Python",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "Backend Developer",
"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"
}
],
"skill_dimension_saved": true,
"skill_id": 5,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 2,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Programming Languages \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": "Python",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "Engineering Manager",
"id": 121,
"rationale": null,
"role_archetype": null,
"slug": "engineering-manager",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 5,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 2,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Programming Languages and Scripting",
"id": 59,
"rationale": "Languages used to write security automation, analysis scripts, detection logic, and remediation helpers. This is the primary implementation surface for a cybersecurity engineer across tooling and response workflows.",
"slug": "programming-languages-and-scripting",
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