Pipeline run
e195a597-da07-4d63-974a-d3e533f92e4d
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 0.40 does not contradict
Resolution:
in_db
— role exists in library; skill↔dim and role↔dim links saved when applicable.
Job description
Line of Service
Advisory
Industry/Sector
Not Applicable
Specialism
Data, Analytics & AI
Management Level
Manager
Job Description & Summary
At PwC, our people in data and analytics engineering focus on leveraging advanced technologies and techniques to design and develop robust data solutions for clients. They play a crucial role in transforming raw data into actionable insights, enabling informed decision-making and driving business growth.
In data engineering at PwC, you will focus on designing and building data infrastructure and systems to enable efficient data processing and analysis. You will be responsible for developing and implementing data pipelines, data integration, and data transformation solutions.
Why PWC
At PwC, you will be part of a vibrant community of solvers that leads with trust and creates distinctive outcomes for our clients and communities. This purpose-led and values-driven work, powered by technology in an environment that drives innovation, will enable you to make a tangible impact in the real world. We reward your contributions, support your wellbeing, and offer inclusive benefits, flexibility programmes and mentorship that will help you thrive in work and life. Together, we grow, learn, care, collaborate, and create a future of infinite experiences for each other. Learn more about us.
At PwC, we believe in providing equal employment opportunities, without any discrimination on the grounds of gender, ethnic background, age, disability, marital status, sexual orientation, pregnancy, gender identity or expression, religion or other beliefs, perceived differences and status protected by law. We strive to create an environment where each one of our people can bring their true selves and contribute to their personal growth and the firm’s growth. To enable this, we have zero tolerance for any discrimination and harassment based on the above considerations.
Responsibilities:
We are seeking a talented Data Engineer with strong expertise in Databricks, specifically in Unity Catalog, PySpark, and SQL, to join our data team. You’ll play a key role in building secure, scalable data pipelines and implementing robust data governance strategies using Unity Catalog.
Design and implement ETL/ELT pipelines using Databricks and PySpark. Work with Unity Catalog to manage data governance, access controls, lineage, and auditing across data assets. Develop high-performance SQL queries and optimize Spark jobs. Collaborate with data scientists, analysts, and business stakeholders to understand data needs. Ensure data quality and compliance across all stages of the data lifecycle. Implement best practices for data security and lineage within the Databricks ecosystem. Participate in CI/CD, version control, and testing practices for data pipelines. Required Skills:
Proven experience with Databricks and Unity Catalog (data permissions, lineage, audits). Strong hands-on skills with PySpark and Spark SQL. Solid experience writing and optimizing complex SQL queries. Familiarity with Delta Lake, data lakehouse architecture, and data partitioning. Experience with cloud platforms like Azure or AWS. Understanding of data governance, RBAC, and data security standards. Preferred Qualifications: Databricks Certified Data Engineer Associate or Professional. Experience with tools like Airflow, Git, Azure Data Factory, or dbt. Exposure to streaming data and real-time processing. Knowledge of DevOps practices for data engineering.
Mandatory skill sets:
Databricks
Preferred skill sets:
Databricks
Years of experience required:
7-14 years
Education qualification:
BE/BTECH, ME/MTECH, MBA, MCA
Education (if blank, degree and/or field of study not specified)
Degrees/Field of Study required: Bachelor of Technology, Bachelor of Engineering, MBA (Master of Business Administration)
Degrees/Field of Study preferred:
Certifications (if blank, certifications not specified)
Required Skills
Databricks Platform
Optional Skills
Accepting Feedback, Accepting Feedback, Active Listening, Agile Scalability, Amazon Web Services (AWS), Analytical Thinking, Apache Airflow, Apache Hadoop, Azure Data Factory, Coaching and Feedback, Communication, Creativity, Data Anonymization, Data Architecture, Database Administration, Database Management System (DBMS), Database Optimization, Database Security Best Practices, Databricks Unified Data Analytics Platform, Data Engineering, Data Engineering Platforms, Data Infrastructure, Data Integration, Data Lake, Data Modeling {+ 33 more}
Desired Languages (If blank, desired languages not specified)
Travel Requirements
Available for Work Visa Sponsorship?
Government Clearance Required?
Job Posting End Date
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
- Databricks (CANONICAL)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Platform
- Sub-category
- Data Analytics Platform
- Vendor
- Databricks, Inc.
- License
- other_open
- Year introduced
- 2013
- Confidence
- 0.97
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Databricks appears frequently in data engineering and analytics job postings, especially alongside Spark, Delta Lake, and lakehouse stacks; strong vendor adoption and broad enterprise usage signal mainstream demand.
Skill profile (library / DB)
- Skill nature
- PLATFORM
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 9
- Sub-category id
- 911
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
React Frontend Development Catalog dimension db id 96
Library dimension (catalog)
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
React Frontend Development
d_init_01
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Aliases — catalog
- Unity Catalog (CANONICAL)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Platform
- Sub-category
- Data Governance Platform
- Vendor
- Databricks
- License
- unknown
- Year introduced
- 2021
- Confidence
- 0.90
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Appears increasingly in Databricks and data-governance job postings, but JD volume is still far below core platforms like AWS or PostgreSQL; adoption is growing with Unity Catalog positioned as Databricks’ unified governance layer.
Skill profile (library / DB)
- Skill nature
- PLATFORM
- Volatility
- EMERGING
- Typical lifespan
- EVERGREEN
- Category id
- 9
- Sub-category id
- 250
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
Data Lineage and Metadata Catalog dimension db id 28
Library dimension (catalog)
Roles linked in library: Data Engineer
-
React Frontend Development Catalog dimension db id 96
Library dimension (catalog)
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Data Lineage and Metadata
data-lineage-and-metadata
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved |
|
React Frontend Development
d_init_01
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
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
|
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 for Data Work Catalog dimension db id 21
Library dimension (catalog)
Roles linked in library: Data Engineer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Pega Programming Languages & DSLs
pega-programming-languages-dsls
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Programming Languages for Data Work
programming-languages-for-data-work
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved |
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
- Delta Lake (CANONICAL) primary
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Tool
- Sub-category
- Table Format Tool
- Vendor
- Databricks
- License
- apache_2
- Year introduced
- 2017
- Confidence
- 0.72
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Delta Lake appears frequently in data engineering JDs and cloud vendor docs, especially alongside Databricks/Spark for lakehouse stacks; it’s a common hiring-pipeline skill rather than a niche tool.
Skill profile (library / DB)
- Skill nature
- TOOL
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 13
- Sub-category id
- 1170
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
Model and Data Versioning Catalog dimension db id 48
Library dimension (catalog)
Roles linked in library: ML Engineer, MLOps Engineer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Model and Data Versioning
model-and-data-versioning
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
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
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) |
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
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) |
Aliases — catalog
- Airflow (CANONICAL) primary
- airflow 2 (VERSION)
- airflow-2 (VERSION)
- airflow2 (VERSION)
- airflow2.x (VERSION)
- apache airflow 2 (VERSION)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Tool
- Sub-category
- Workflow Orchestration Tool
- Vendor
- Apache Software Foundation
- License
- apache_2
- Year introduced
- 2014
- Confidence
- 0.95
- Version strategy
- SEPARATE_ENTITY
- Version tag
- 2.x
Maturity reasoning: Apache Airflow appears in many data engineering job postings and is a common orchestration choice in production stacks; its GitHub activity and ecosystem remain strong, with no vendor sunset or clear replacement dominating JDs.
Skill profile (library / DB)
- Skill nature
- TOOL
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 13
- Sub-category id
- 130
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
Workflow Orchestration for ML Pipelines Catalog dimension db id 54
Library dimension (catalog)
Roles linked in library: ML Engineer, MLOps Engineer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Workflow Orchestration for ML Pipelines
workflow-orchestration-for-ml-pipelines
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Aliases — catalog
- Git (CANONICAL)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Tool
- Sub-category
- Version Control Tool
- Vendor
- Linus Torvalds
- License
- gpl_v2
- Year introduced
- 2005
- Confidence
- 0.99
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Git is a hiring-pipeline staple: it appears in the vast majority of software engineering job descriptions and is the default VCS on GitHub/GitLab/Bitbucket.
Skill profile (library / DB)
- Skill nature
- TOOL
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 13
- Sub-category id
- 730
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
React Frontend Development Catalog dimension db id 96
Library dimension (catalog)
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
React Frontend Development
d_init_01
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Cloud Platforms
- Sub-category
- general
- Skill nature
- PLATFORM
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Aliases — catalog
- dbt (CANONICAL) primary
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Framework
- Sub-category
- Analytics Engineering Framework
- Vendor
- dbt Labs
- License
- apache_2
- Year introduced
- 2016
- Confidence
- 0.97
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: dbt appears in many analytics engineer and data platform job descriptions, and its GitHub repo has strong adoption signals with widespread ecosystem support from major cloud/data vendors.
Skill profile (library / DB)
- Skill nature
- FRAMEWORK
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 5
- Sub-category id
- 89
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
ETL and ELT Tooling Catalog dimension db id 24
Library dimension (catalog)
Roles linked in library: Data Engineer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
ETL and ELT Tooling
etl-and-elt-tooling
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved |
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Concepts
- 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
- Concepts
- Sub-category
- general
- Skill nature
- CONCEPT
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Aliases — catalog
- DevOps (CANONICAL)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Methodology
- Sub-category
- Devops Methodology
- Confidence
- 0.97
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: DevOps appears in a large share of software and platform engineering job descriptions, often alongside CI/CD, Kubernetes, and cloud tooling; it is a standard hiring-pipeline keyword rather than a niche specialty.
Skill profile (library / DB)
- Skill nature
- METHODOLOGY
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 8
- Sub-category id
- 922
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
CI/CD Pipeline Platforms Catalog dimension db id 150
Library dimension (catalog)
Roles linked in library: DevOps Engineer
-
Deployment and Release Patterns Catalog dimension db id 140
Library dimension (catalog)
Roles linked in library: Cloud Architect
-
Infrastructure as Code Catalog dimension db id 132
Library dimension (catalog)
Roles linked in library: Cloud Architect, DevOps Engineer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
CI/CD Pipeline Platforms
ci-cd-pipeline-platforms
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Deployment and Release Patterns
deployment-and-release-patterns
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Infrastructure as Code
infrastructure-as-code
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Aliases — catalog
- CI/CD (CANONICAL)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Methodology
- Sub-category
- Ci Cd Process
- Confidence
- 0.93
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: CI/CD appears in a large share of software engineering JDs and is a standard requirement across DevOps, platform, and backend roles; major vendors like GitHub, GitLab, and AWS all center product roadmaps on CI/CD pipelines.
Skill profile (library / DB)
- Skill nature
- METHODOLOGY
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 8
- Sub-category id
- 900
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
CI/CD Pipeline Platforms Catalog dimension db id 150
Library dimension (catalog)
Roles linked in library: DevOps Engineer
-
CI/CD for Machine Learning Catalog dimension db id 56
Library dimension (catalog)
Roles linked in library: ML Engineer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
CI/CD Pipeline Platforms
ci-cd-pipeline-platforms
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
CI/CD for Machine Learning
ci-cd-for-machine-learning
|
✓ | — | 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
- Practices
- Sub-category
- general
- Skill nature
- PRACTICE
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Version strategy
- UNVERSIONED
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Practices
- Sub-category
- general
- Skill nature
- PRACTICE
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Version strategy
- UNVERSIONED
Aliases — catalog
- RBAC (CANONICAL) primary
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Concept
- Sub-category
- Access Control Concept
- Confidence
- 0.97
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: RBAC is a standard access-control model widely listed in security and IAM job descriptions, and it underpins common products like AWS IAM and Kubernetes RBAC.
Skill profile (library / DB)
- Skill nature
- CONCEPT
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 2
- Sub-category id
- 5
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
Authentication and Authorization Catalog dimension db id 6
Library dimension (catalog)
Roles linked in library: .NET Backend Developer, Backend Developer, Go Backend Developer, Java Backend Developer, Node.js Backend Developer, PHP Backend Developer, Python Backend Developer
-
Backend Authentication and Authorization Catalog dimension db id 386
Library dimension (catalog)
Roles linked in library: Kotlin Backend Developer, Ruby Backend Developer, Scala Backend Developer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Authentication and Authorization
authentication-and-authorization
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Backend Authentication and Authorization
backend-authentication-and-authorization
|
✓ | — | 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 |
|---|---|---|---|---|---|---|
| Databricks | in_db |
React Frontend Development
d_init_01
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Unity Catalog | in_db |
Data Lineage and Metadata
data-lineage-and-metadata
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved | |
| Unity Catalog | in_db |
React Frontend Development
d_init_01
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| PySpark | new |
ETL and ELT Tooling
etl-and-elt-tooling
|
— | — | Skipped — no persistable v3 meta for new skill | skill_not_in_db_v3_proposed |
| SQL | in_db |
Pega Programming Languages & DSLs
pega-programming-languages-dsls
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| SQL | in_db |
Programming Languages for Data Work
programming-languages-for-data-work
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved | |
| Delta Lake | in_db |
Model and Data Versioning
model-and-data-versioning
|
✓ | — | 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) | |
| 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) | |
| Airflow | in_db |
Workflow Orchestration for ML Pipelines
workflow-orchestration-for-ml-pipelines
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Git | in_db |
React Frontend Development
d_init_01
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| dbt | in_db |
ETL and ELT Tooling
etl-and-elt-tooling
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved | |
| DevOps | in_db |
CI/CD Pipeline Platforms
ci-cd-pipeline-platforms
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| DevOps | in_db |
Deployment and Release Patterns
deployment-and-release-patterns
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| DevOps | in_db |
Infrastructure as Code
infrastructure-as-code
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| CI/CD | in_db |
CI/CD Pipeline Platforms
ci-cd-pipeline-platforms
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| CI/CD | in_db |
CI/CD for Machine Learning
ci-cd-for-machine-learning
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| RBAC | in_db |
Authentication and Authorization
authentication-and-authorization
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| RBAC | in_db |
Backend Authentication and Authorization
backend-authentication-and-authorization
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Library artifacts (this run)
| Kind | Detail | DB id |
|---|---|---|
| canonical_skill_proposed | Spark SQL | type=Data Engineering Tools subtype=general nature=TOOL lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Azure Data Factory | type=Cloud Platforms subtype=general nature=PLATFORM lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Streaming | type=Concepts subtype=general nature=CONCEPT lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Real-time Processing | type=Concepts subtype=general nature=CONCEPT lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Version Control | type=Practices subtype=general nature=PRACTICE lifespan=EVERGREEN | |
| canonical_skill_proposed | Testing | type=Practices subtype=general nature=PRACTICE lifespan=EVERGREEN | |
| dimension_skill_link_proposed | PySpark ↔ ETL and ELT Tooling | |
| role_dimension_link_proposed | Data Engineer ↔ ETL and ELT Tooling |
nano JD Parser — gpt-4.1-nano click to toggle
Certifications
Show raw JSON
{
"JD_type": "pass",
"about_company": {
"source_marker": {
"first_5_words": "At PwC, our people in",
"last_5_words": "informed decision-making and driving"
},
"text": "At PwC, our people in data and analytics engineering focus on leveraging advanced technologies and techniques to design and develop robust data solutions for clients. They play a crucial role in transforming raw data into actionable insights, enabling informed decision-making and driving business growth.",
"word_count": 50
},
"certifications": [
"Databricks Certified Data Engineer Associate",
"Databricks Certified Data Engineer Professional"
],
"company_name": "PwC",
"ctc": null,
"domain": {
"primary": {
"aliases": [
"ITES",
"BPO",
"Tech Consulting"
],
"domain": "IT Services \u0026 Consulting"
},
"secondary": null
},
"education": [
{
"level": "Bachelor\u0027s",
"qualification": "BTECH/BE - Technology",
"raw": "Bachelor of Technology, Bachelor of Engineering",
"requirement": "required"
},
{
"level": "Master\u0027s",
"qualification": "MBA - Business Administration",
"raw": "MBA (Master of Business Administration)",
"requirement": "required"
}
],
"experience": {
"max": 14,
"min": 7,
"raw": "7-14 years"
},
"job_locations": [],
"role": "Data Engineer",
"role_aliases": [
"Data Engineer",
"Data Analytics Engineer",
"Data Solutions Engineer"
],
"role_archetype": "Data",
"roles_and_responsibilities": [
{
"bullet_count": 0,
"heading": "Responsibilities",
"heading_was_present": true,
"source_marker": {
"first_5_words": "We are seeking a talented",
"last_5_words": "version control, and testing practices"
},
"text": "We are seeking a talented Data Engineer with strong expertise in Databricks, specifically in Unity Catalog, PySpark, and SQL, to join our data team. You\u2019ll play a key role in building secure, scalable data pipelines and implementing robust data governance strategies using Unity Catalog.\n\nDesign and implement ETL/ELT pipelines using Databricks and PySpark. Work with Unity Catalog to manage data governance, access controls, lineage, and auditing across data assets. Develop high-performance SQL queries and optimize Spark jobs. Collaborate with data scientists, analysts, and business stakeholders to understand data needs. Ensure data quality and compliance across all stages of the data lifecycle. Implement best practices for data security and lineage within the Databricks ecosystem. Participate in CI/CD, version control, and testing practices for data pipelines.",
"word_count": 139
},
{
"bullet_count": 0,
"heading": "Required Skills",
"heading_was_present": true,
"source_marker": {
"first_5_words": "Proven experience with Databricks",
"last_5_words": "governance, RBAC, and data security"
},
"text": "Proven experience with Databricks and Unity Catalog (data permissions, lineage, audits). Strong hands-on skills with PySpark and Spark SQL. Solid experience writing and optimizing complex SQL queries. Familiarity with Delta Lake, data lakehouse architecture, and data partitioning. Experience with cloud platforms like Azure or AWS. Understanding of data governance, RBAC, and data security standards.",
"word_count": 56
},
{
"bullet_count": 0,
"heading": "Preferred Qualifications",
"heading_was_present": true,
"source_marker": {
"first_5_words": "Databricks Certified Data Engineer",
"last_5_words": "practices for data engineering."
},
"text": "Databricks Certified Data Engineer Associate or Professional. Experience with tools like Airflow, Git, Azure Data Factory, or dbt. Exposure to streaming data and real-time processing. Knowledge of DevOps practices for data engineering.",
"word_count": 42
},
{
"bullet_count": 0,
"heading": "Mandatory skill sets",
"heading_was_present": true,
"source_marker": {
"first_5_words": "Databricks",
"last_5_words": "Databricks"
},
"text": "Databricks",
"word_count": 1
},
{
"bullet_count": 0,
"heading": "Preferred skill sets",
"heading_was_present": true,
"source_marker": {
"first_5_words": "Databricks",
"last_5_words": "Databricks"
},
"text": "Databricks",
"word_count": 1
}
],
"urls": []
}
API 1 — extract-from-jd click to toggle
{
"final_skills": [
{
"is_primary": true,
"skill_name": "Databricks"
},
{
"is_primary": true,
"skill_name": "Unity Catalog"
},
{
"is_primary": true,
"skill_name": "PySpark"
},
{
"is_primary": true,
"skill_name": "SQL"
},
{
"is_primary": true,
"skill_name": "Spark SQL"
},
{
"is_primary": false,
"skill_name": "Delta Lake"
},
{
"is_primary": false,
"skill_name": "Azure"
},
{
"is_primary": false,
"skill_name": "AWS"
},
{
"is_primary": false,
"skill_name": "Airflow"
},
{
"is_primary": false,
"skill_name": "Git"
},
{
"is_primary": false,
"skill_name": "Azure Data Factory"
},
{
"is_primary": false,
"skill_name": "dbt"
},
{
"is_primary": false,
"skill_name": "Streaming"
},
{
"is_primary": false,
"skill_name": "Real-time Processing"
},
{
"is_primary": false,
"skill_name": "DevOps"
},
{
"is_primary": false,
"skill_name": "CI/CD"
},
{
"is_primary": false,
"skill_name": "Version Control"
},
{
"is_primary": false,
"skill_name": "Testing"
},
{
"is_primary": false,
"skill_name": "RBAC"
}
],
"jd_role": {
"display_name": "Data Engineer",
"rationale": null,
"role_aliases": [
"Data Engineer",
"Data Analytics Engineer",
"Data Solutions Engineer"
],
"role_archetype": "Data",
"slug": ""
},
"nano_parsed": {
"JD_type": "pass",
"about_company": {
"source_marker": {
"first_5_words": "At PwC, our people in",
"last_5_words": "informed decision-making and driving"
},
"text": "At PwC, our people in data and analytics engineering focus on leveraging advanced technologies and techniques to design and develop robust data solutions for clients. They play a crucial role in transforming raw data into actionable insights, enabling informed decision-making and driving business growth.",
"word_count": 50
},
"certifications": [
"Databricks Certified Data Engineer Associate",
"Databricks Certified Data Engineer Professional"
],
"company_name": "PwC",
"ctc": null,
"domain": {
"primary": {
"aliases": [
"ITES",
"BPO",
"Tech Consulting"
],
"domain": "IT Services \u0026 Consulting"
},
"secondary": null
},
"education": [
{
"level": "Bachelor\u0027s",
"qualification": "BTECH/BE - Technology",
"raw": "Bachelor of Technology, Bachelor of Engineering",
"requirement": "required"
},
{
"level": "Master\u0027s",
"qualification": "MBA - Business Administration",
"raw": "MBA (Master of Business Administration)",
"requirement": "required"
}
],
"experience": {
"max": 14,
"min": 7,
"raw": "7-14 years"
},
"job_locations": [],
"role": "Data Engineer",
"role_aliases": [
"Data Engineer",
"Data Analytics Engineer",
"Data Solutions Engineer"
],
"role_archetype": "Data",
"roles_and_responsibilities": [
{
"bullet_count": 0,
"heading": "Responsibilities",
"heading_was_present": true,
"source_marker": {
"first_5_words": "We are seeking a talented",
"last_5_words": "version control, and testing practices"
},
"text": "We are seeking a talented Data Engineer with strong expertise in Databricks, specifically in Unity Catalog, PySpark, and SQL, to join our data team. You\u2019ll play a key role in building secure, scalable data pipelines and implementing robust data governance strategies using Unity Catalog.\n\nDesign and implement ETL/ELT pipelines using Databricks and PySpark. Work with Unity Catalog to manage data governance, access controls, lineage, and auditing across data assets. Develop high-performance SQL queries and optimize Spark jobs. Collaborate with data scientists, analysts, and business stakeholders to understand data needs. Ensure data quality and compliance across all stages of the data lifecycle. Implement best practices for data security and lineage within the Databricks ecosystem. Participate in CI/CD, version control, and testing practices for data pipelines.",
"word_count": 139
},
{
"bullet_count": 0,
"heading": "Required Skills",
"heading_was_present": true,
"source_marker": {
"first_5_words": "Proven experience with Databricks",
"last_5_words": "governance, RBAC, and data security"
},
"text": "Proven experience with Databricks and Unity Catalog (data permissions, lineage, audits). Strong hands-on skills with PySpark and Spark SQL. Solid experience writing and optimizing complex SQL queries. Familiarity with Delta Lake, data lakehouse architecture, and data partitioning. Experience with cloud platforms like Azure or AWS. Understanding of data governance, RBAC, and data security standards.",
"word_count": 56
},
{
"bullet_count": 0,
"heading": "Preferred Qualifications",
"heading_was_present": true,
"source_marker": {
"first_5_words": "Databricks Certified Data Engineer",
"last_5_words": "practices for data engineering."
},
"text": "Databricks Certified Data Engineer Associate or Professional. Experience with tools like Airflow, Git, Azure Data Factory, or dbt. Exposure to streaming data and real-time processing. Knowledge of DevOps practices for data engineering.",
"word_count": 42
},
{
"bullet_count": 0,
"heading": "Mandatory skill sets",
"heading_was_present": true,
"source_marker": {
"first_5_words": "Databricks",
"last_5_words": "Databricks"
},
"text": "Databricks",
"word_count": 1
},
{
"bullet_count": 0,
"heading": "Preferred skill sets",
"heading_was_present": true,
"source_marker": {
"first_5_words": "Databricks",
"last_5_words": "Databricks"
},
"text": "Databricks",
"word_count": 1
}
],
"urls": []
},
"rejected": false,
"rejection_reason": null,
"run_id": "e195a597-da07-4d63-974a-d3e533f92e4d",
"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 data scientists, analysts, and business stakeholders to understand data needs.",
"similarity": 0.7721
},
{
"kra_text": "Implements data quality validation rules, reconciliation checks, and anomaly detection to ensure data completeness, accuracy, and consistency.",
"sentence": "Ensure data quality and compliance across all stages of the data lifecycle.",
"similarity": 0.6465
},
{
"kra_text": "Develops batch and real-time streaming data pipelines using Apache Spark, Apache Kafka, Apache Flink, or Airflow for data movement and processing at scale.",
"sentence": "Design and implement ETL/ELT pipelines using Databricks and PySpark.",
"similarity": 0.6303
}
],
"matched_count": null,
"matched_skills": null,
"role_id": 2,
"score": 0.683,
"slug": "data-engineer",
"total_count": null
},
{
"display_name": "Fullstack Developer",
"kra_matches": [
{
"kra_text": "Delivers features through CI/CD pipelines using automated tests, staged rollouts, feature flags, and incremental deployments.",
"sentence": "Participate in CI/CD, version control, and testing practices for data pipelines.",
"similarity": 0.5856
},
{
"kra_text": "Designs and queries relational databases like PostgreSQL and document stores like MongoDB, writing migrations, indexes, and optimized queries.",
"sentence": "Solid experience writing and optimizing complex SQL queries.",
"similarity": 0.5477
},
{
"kra_text": "Designs and queries relational databases like PostgreSQL and document stores like MongoDB, writing migrations, indexes, and optimized queries.",
"sentence": "Develop high-performance SQL queries and optimize Spark jobs.",
"similarity": 0.5321
}
],
"matched_count": null,
"matched_skills": null,
"role_id": 15,
"score": 0.5551,
"slug": "full-stack-engineer",
"total_count": null
},
{
"display_name": "Backend Developer",
"kra_matches": [
{
"kra_text": "Identifies and resolves backend performance bottlenecks through query optimization, indexing strategies, connection pooling, and distributed caching with Redis.",
"sentence": "Develop high-performance SQL queries and optimize Spark jobs.",
"similarity": 0.5391
},
{
"kra_text": "Configures Docker containers, deployment descriptors, environment variables, and CI/CD pipeline stages for backend service releases.",
"sentence": "Participate in CI/CD, version control, and testing practices for data pipelines.",
"similarity": 0.5101
},
{
"kra_text": "Identifies and resolves backend performance bottlenecks through query optimization, indexing strategies, connection pooling, and distributed caching with Redis.",
"sentence": "Solid experience writing and optimizing complex SQL queries.",
"similarity": 0.4992
}
],
"matched_count": null,
"matched_skills": null,
"role_id": 1,
"score": 0.5161,
"slug": "backend-engineer",
"total_count": null
},
{
"display_name": "MLOps Engineer",
"kra_matches": [
{
"kra_text": "Validates model performance benchmarks, data schema contracts, and system integration health before signing off on production release readiness.",
"sentence": "Ensure data quality and compliance across all stages of the data lifecycle.",
"similarity": 0.5412
},
{
"kra_text": "Maintains model versioning, experiment lineage, and artifact tracking using MLflow, DVC, or Weights \u0026 Biases for reproducibility and auditability.",
"sentence": "Participate in CI/CD, version control, and testing practices for data pipelines.",
"similarity": 0.4733
},
{
"kra_text": "Maintains model versioning, experiment lineage, and artifact tracking using MLflow, DVC, or Weights \u0026 Biases for reproducibility and auditability.",
"sentence": "Implement best practices for data security and lineage within the Databricks ecosystem.",
"similarity": 0.435
}
],
"matched_count": null,
"matched_skills": null,
"role_id": 16,
"score": 0.4831,
"slug": "ml-ops-engineer",
"total_count": null
},
{
"display_name": "DevOps Engineer",
"kra_matches": [
{
"kra_text": "Builds and maintains CI/CD pipelines using Jenkins, GitHub Actions, GitLab CI, or CircleCI to automate build, test, security scanning, and deployment workflows.",
"sentence": "Participate in CI/CD, version control, and testing practices for data pipelines.",
"similarity": 0.6002
},
{
"kra_text": "Monitors CI/CD pipeline reliability, identifies bottlenecks in delivery workflows, and improves deployment frequency, lead time, and failure recovery rate.",
"sentence": "Ensure data quality and compliance across all stages of the data lifecycle.",
"similarity": 0.4221
},
{
"kra_text": "Collaborates with development teams to improve build processes, reduce deployment friction, containerize applications, and adopt DevOps best practices.",
"sentence": "Collaborate with data scientists, analysts, and business stakeholders to understand data needs.",
"similarity": 0.404
}
],
"matched_count": null,
"matched_skills": null,
"role_id": 10,
"score": 0.4754,
"slug": "devops-engineer",
"total_count": null
}
],
"skill_match_roles": [
{
"display_name": "Data Engineer",
"kra_matches": null,
"matched_count": 2,
"matched_skills": [
"SQL",
"Unity Catalog"
],
"role_id": 2,
"score": 0.4,
"slug": "data-engineer",
"total_count": 5
},
{
"display_name": "Pega Developer",
"kra_matches": null,
"matched_count": 1,
"matched_skills": [
"SQL"
],
"role_id": 24,
"score": 0.2,
"slug": "pega-developer",
"total_count": 5
}
]
},
"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 0.40 does not contradict",
"sub_role": null
},
"stage5_updates": {
"centroid_n_after": 120,
"centroid_updated": true,
"collision_log_id": null,
"new_kra_attached": null,
"new_skills_attached": [
{
"is_primary": true,
"queue_id": 6755,
"role_display_name": "Data Engineer",
"role_slug": "data-engineer",
"skill_name": "PySpark",
"status": "pending"
},
{
"is_primary": true,
"queue_id": 6757,
"role_display_name": "Data Engineer",
"role_slug": "data-engineer",
"skill_name": "Spark SQL",
"status": "pending"
},
{
"is_primary": false,
"queue_id": 6759,
"role_display_name": "Data Engineer",
"role_slug": "data-engineer",
"skill_name": "Azure Data Factory",
"status": "pending"
},
{
"is_primary": false,
"queue_id": 6761,
"role_display_name": "Data Engineer",
"role_slug": "data-engineer",
"skill_name": "Streaming",
"status": "pending"
},
{
"is_primary": false,
"queue_id": 6763,
"role_display_name": "Data Engineer",
"role_slug": "data-engineer",
"skill_name": "Real-time Processing",
"status": "pending"
},
{
"is_primary": false,
"queue_id": 6765,
"role_display_name": "Data Engineer",
"role_slug": "data-engineer",
"skill_name": "Version Control",
"status": "pending"
},
{
"is_primary": false,
"queue_id": 6767,
"role_display_name": "Data Engineer",
"role_slug": "data-engineer",
"skill_name": "Testing",
"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": 1838,
"existing_alias_text": "Databricks",
"input_term": "Databricks",
"matched_canonical": {
"category_id": 9,
"display_name": "Databricks",
"id": 1202,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "PLATFORM",
"slug": "databricks",
"sub_category_id": 911,
"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": 1839,
"existing_alias_text": "Unity Catalog",
"input_term": "Unity Catalog",
"matched_canonical": {
"category_id": 9,
"display_name": "Unity Catalog",
"id": 1203,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "PLATFORM",
"slug": "unity-catalog",
"sub_category_id": 250,
"typical_lifespan": "EVERGREEN",
"volatility": "EMERGING"
},
"matched_via": "alias"
},
{
"alias_persist_skipped_reason": "TODO: REMOVE AFTER TESTING \u2014 alias DB write disabled",
"alias_persisted": false,
"existing_alias_id": 2004,
"existing_alias_text": "Apache Spark",
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"difficulty_hint": "well_known",
"display_name": "CI/CD Pipeline Platforms",
"id": 150,
"rationale": "Systems used to define, run, and maintain automated build and deployment workflows. This cluster is coherent because the role owns delivery automation end to end, including pipeline reliability and promotion logic.",
"slug": "ci-cd-pipeline-platforms",
"source": "db"
},
"input_skill": "CI/CD",
"llm_role": null,
"roles_from_db": [
{
"display_name": "DevOps Engineer",
"id": 10,
"rationale": null,
"role_archetype": null,
"slug": "devops-engineer",
"source": "db"
}
]
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "CI/CD for Machine Learning",
"id": 56,
"rationale": "Tools and platforms for automating ML model integration, testing, and deployment pipelines.",
"slug": "ci-cd-for-machine-learning",
"source": "db"
},
"input_skill": "CI/CD",
"llm_role": null,
"roles_from_db": [
{
"display_name": "ML Engineer",
"id": 3,
"rationale": null,
"role_archetype": null,
"slug": "ml-engineer",
"source": "db"
}
]
}
],
"input_skill": "CI/CD",
"matched_via": "alias",
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": null,
"source_tag": "db",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [],
"input_skill": "Version Control",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": {
"derived": {
"category": "Practices",
"skill_nature": "PRACTICE",
"sub_category": "general",
"typical_lifespan": "EVERGREEN",
"version_strategy": "UNVERSIONED",
"volatility": "STABLE"
},
"enrichment": null,
"keep_log": [],
"locked_dimensions": [],
"merge_log": [],
"placed": null,
"relationships": null,
"skill_id": "version-control",
"split_log": [],
"typed": null,
"warnings": []
},
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [],
"input_skill": "Testing",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": {
"derived": {
"category": "Practices",
"skill_nature": "PRACTICE",
"sub_category": "general",
"typical_lifespan": "EVERGREEN",
"version_strategy": "UNVERSIONED",
"volatility": "STABLE"
},
"enrichment": null,
"keep_log": [],
"locked_dimensions": [],
"merge_log": [],
"placed": null,
"relationships": null,
"skill_id": "testing",
"split_log": [],
"typed": null,
"warnings": []
},
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [
{
"alias_text": "RBAC",
"alias_type": "CANONICAL",
"id": 166,
"is_primary": true,
"match_strategy": "CASE_INSENSITIVE"
}
],
"canonical": {
"category_id": 2,
"display_name": "RBAC",
"id": 29,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "CONCEPT",
"slug": "rbac",
"sub_category_id": 5,
"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"dimensions": [
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Authentication and Authorization",
"id": 6,
"rationale": "Identity, session, and access-control mechanisms used to protect PHP backend resources. This includes login flows, token handling, role checks, permissions, and policy enforcement in server-side code.",
"slug": "authentication-and-authorization",
"source": "db"
},
"input_skill": "RBAC",
"llm_role": null,
"roles_from_db": [
{
"display_name": ".NET Backend Developer",
"id": 83,
"rationale": null,
"role_archetype": "Engineering",
"slug": "dotnet-backend-developer",
"source": "db"
},
{
"display_name": "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": "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": "Node.js Backend Developer",
"id": 82,
"rationale": null,
"role_archetype": "Engineering",
"slug": "node-backend-developer",
"source": "db"
},
{
"display_name": "PHP Backend Developer",
"id": 86,
"rationale": null,
"role_archetype": "Engineering",
"slug": "php-backend-developer",
"source": "db"
},
{
"display_name": "Python Backend Developer",
"id": 80,
"rationale": null,
"role_archetype": "Engineering",
"slug": "python-backend-developer",
"source": "db"
}
]
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Backend Authentication and Authorization",
"id": 386,
"rationale": "Identity and access control mechanisms used by backend services to authenticate users or clients and enforce permissions on protected resources. This includes token and session handling, OAuth 2.0 / OpenID Connect / SAML flows, JWT and access tokens, API keys, MFA, role- or claim-based authorization, RBAC/ABAC, scopes, policies, Spring Security, and authorization filters.",
"slug": "backend-authentication-and-authorization",
"source": "db"
},
"input_skill": "RBAC",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Kotlin Backend Developer",
"id": 84,
"rationale": null,
"role_archetype": "Engineering",
"slug": "kotlin-server-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"
}
]
}
],
"input_skill": "RBAC",
"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": [
"Spark SQL",
"Azure Data Factory",
"Streaming",
"Real-time Processing",
"Version Control",
"Testing"
]
}
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 0.40 does not contradict",
"role_archetype": null,
"slug": "data-engineer",
"source": "db"
},
"chosen_role_resolution": "in_db",
"final_input_skills": [
{
"skill": "Databricks",
"tag": "in_db"
},
{
"skill": "Unity Catalog",
"tag": "in_db"
},
{
"skill": "PySpark",
"tag": "in_db"
},
{
"skill": "SQL",
"tag": "in_db"
},
{
"skill": "Spark SQL",
"tag": "new"
},
{
"skill": "Delta Lake",
"tag": "in_db"
},
{
"skill": "Azure",
"tag": "in_db"
},
{
"skill": "AWS",
"tag": "in_db"
},
{
"skill": "Airflow",
"tag": "in_db"
},
{
"skill": "Git",
"tag": "in_db"
},
{
"skill": "Azure Data Factory",
"tag": "new"
},
{
"skill": "dbt",
"tag": "in_db"
},
{
"skill": "Streaming",
"tag": "new"
},
{
"skill": "Real-time Processing",
"tag": "new"
},
{
"skill": "DevOps",
"tag": "in_db"
},
{
"skill": "CI/CD",
"tag": "in_db"
},
{
"skill": "Version Control",
"tag": "new"
},
{
"skill": "Testing",
"tag": "new"
},
{
"skill": "RBAC",
"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": "React Frontend Development",
"id": 96,
"rationale": "Building interactive web user interfaces with React.js, including component composition, state management, hooks, and rendering patterns. React.js belongs here because it is a core library for client-side UI development in modern web applications.",
"slug": "d_init_01",
"source": "db"
},
"dimension_id": 96,
"input_skill": "Databricks",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [],
"skill_dimension_saved": true,
"skill_id": 1202,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 2,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Data Lineage and Metadata",
"id": 28,
"rationale": "Cataloging, documenting, and tracing how data moves and changes across systems. This dimension supports impact analysis, governance, discoverability, and operational understanding of datasets.",
"slug": "data-lineage-and-metadata",
"source": "db"
},
"dimension_id": 28,
"input_skill": "Unity Catalog",
"llm_role": null,
"matched_chosen_role": true,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension saved",
"role_dimension_saved": true,
"roles_from_db": [
{
"display_name": "Data Engineer",
"id": 2,
"rationale": null,
"role_archetype": null,
"slug": "data-engineer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 1203,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 2,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "React Frontend Development",
"id": 96,
"rationale": "Building interactive web user interfaces with React.js, including component composition, state management, hooks, and rendering patterns. React.js belongs here because it is a core library for client-side UI development in modern web applications.",
"slug": "d_init_01",
"source": "db"
},
"dimension_id": 96,
"input_skill": "Unity Catalog",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [],
"skill_dimension_saved": true,
"skill_id": 1203,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 2,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "ETL and ELT Tooling",
"id": 24,
"rationale": "Packaged tools for extracting, loading, and transforming data across systems. This dimension covers connector-based ingestion, transformation frameworks, and managed integration products.",
"slug": "etl-and-elt-tooling",
"source": "db"
},
"dimension_id": 24,
"input_skill": "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": "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 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": "Model and Data Versioning",
"id": 48,
"rationale": "Versioning systems for datasets, features, and model artifacts at the storage layer. This enables reproducible training, rollback, lineage of artifacts, and controlled promotion of model assets.",
"slug": "model-and-data-versioning",
"source": "db"
},
"dimension_id": 48,
"input_skill": "Delta Lake",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "ML Engineer",
"id": 3,
"rationale": null,
"role_archetype": null,
"slug": "ml-engineer",
"source": "db"
},
{
"display_name": "MLOps Engineer",
"id": 16,
"rationale": null,
"role_archetype": null,
"slug": "ml-ops-engineer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 237,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 2,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "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"
}
],
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"skill_id": 188,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 2,
"dimension": {
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"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",
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"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": 187,
"skill_tag": "in_db",
"skipped_reason": null
},
{
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"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"
},
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"matched_chosen_role": false,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "AI Engineer",
"id": 13,
"rationale": null,
"role_archetype": null,
"slug": "ai-engineer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 187,
"skill_tag": "in_db",
"skipped_reason": null
},
{
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"display_name": "Cloud Provider Platforms",
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"source": "db"
},
"dimension_id": 131,
"input_skill": "AWS",
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"matched_chosen_role": false,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "Cloud Architect",
"id": 9,
"rationale": null,
"role_archetype": null,
"slug": "cloud-architect",
"source": "db"
},
{
"display_name": "Cloud Security Engineer",
"id": 23,
"rationale": null,
"role_archetype": null,
"slug": "cloud-security-engineer",
"source": "db"
}
],
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"skill_tag": "in_db",
"skipped_reason": null
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{
"chosen_role_id": 2,
"dimension": {
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"display_name": "Cloud Security Posture Tools",
"id": 64,
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"slug": "cloud-security-posture-tools",
"source": "db"
},
"dimension_id": 64,
"input_skill": "AWS",
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"matched_chosen_role": false,
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"role_dimension_saved": false,
"roles_from_db": [
{
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"rationale": null,
"role_archetype": null,
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"source": "db"
},
{
"display_name": "Cyber Security Engineer",
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"rationale": null,
"role_archetype": null,
"slug": "cybersecurity-engineer",
"source": "db"
}
],
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"skill_tag": "in_db",
"skipped_reason": null
},
{
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"source": "db"
},
"dimension_id": 54,
"input_skill": "Airflow",
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"matched_chosen_role": false,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [
{
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"slug": "ml-engineer",
"source": "db"
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{
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"role_archetype": null,
"slug": "ml-ops-engineer",
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}
],
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"skill_id": 265,
"skill_tag": "in_db",
"skipped_reason": null
},
{
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"source": "db"
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"dimension_id": 96,
"input_skill": "Git",
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"matched_chosen_role": false,
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"roles_from_db": [],
"skill_dimension_saved": true,
"skill_id": 1002,
"skill_tag": "in_db",
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{
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"matched_chosen_role": true,
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"role_dimension_saved": true,
"roles_from_db": [
{
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"role_archetype": null,
"slug": "data-engineer",
"source": "db"
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],
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"skill_id": 115,
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{
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"matched_chosen_role": false,
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"roles_from_db": [
{
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],
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{
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"source": "db"
},
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"roles_from_db": [
{
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],
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{
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"source": "db"
},
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"role_dimension_saved": false,
"roles_from_db": [
{
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},
{
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}
],
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},
{
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"roles_from_db": [
{
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],
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"skill_id": 1190,
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{
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},
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"matched_chosen_role": false,
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"roles_from_db": [
{
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],
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{
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"source": "db"
},
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"matched_chosen_role": false,
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{
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},
{
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},
{
"display_name": "Go Backend Developer",
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{
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{
"display_name": "Node.js Backend Developer",
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"rationale": null,
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},
{
"display_name": "PHP Backend Developer",
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},
{
"display_name": "Python Backend Developer",
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}
],
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"skill_id": 29,
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},
{
"chosen_role_id": 2,
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"rationale": "Identity and access control mechanisms used by backend services to authenticate users or clients and enforce permissions on protected resources. This includes token and session handling, OAuth 2.0 / OpenID Connect / SAML flows, JWT and access tokens, API keys, MFA, role- or claim-based authorization, RBAC/ABAC, scopes, policies, Spring Security, and authorization filters.",
"slug": "backend-authentication-and-authorization",
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},
"dimension_id": 386,
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"matched_chosen_role": false,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
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"roles_from_db": [
{
"display_name": "Kotlin Backend Developer",
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},
{
"display_name": "Ruby Backend Developer",
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{
"display_name": "Scala Backend Developer",
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"role_archetype": "Engineering",
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}
],
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}
],
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},
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}
LLM Calls
Every model call made for this run, in pipeline order. Click a card to see the model's response.