Pipeline run
939a51b7-48cd-45d5-a944-5263b1a54bd7
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 0.71 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
Senior Associate
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. "
Job Description & Summary: A career within….
Responsibilities:
- Design and build data pipelines & Data lakes to automate ingestion of structured and unstructured data that provide fast, optimized, and robust end-to-end solutions - Knowledge about the concepts of data lake and data warehouse - Experience working with AWS big data technologies - Improve the data quality and reliability of data pipelines through monitoring, validation and failure detection. - Deploy and configure components to production environments - Technology: Redshift, S3, AWS Glue, Lambda, SQL, PySpark, SQL
Mandatory skill sets:
AWS Data Engineer
Preferred skill sets:
AWS Data Engineer
Years of experience required:
3-8
Education qualification:
B. Tech/MBA/MCA / M. Tech
Education (if blank, degree and/or field of study not specified)
Degrees/Field of Study required: Bachelor of Engineering, Master of Business Administration
Degrees/Field of Study preferred:
Certifications (if blank, certifications not specified)
Required Skills
AWS Devops
Optional Skills
Accepting Feedback, Accepting Feedback, Active Listening, Agile Scalability, Amazon Web Services (AWS), Analytical Thinking, Apache Airflow, Apache Hadoop, Azure Data Factory, 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, Data Pipeline {+ 27 more}
Desired Languages (If blank, desired languages not specified)
Travel Requirements
Not Specified
Available for Work Visa Sponsorship?
No
Government Clearance Required?
No
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
- 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
- Amazon Redshift (CANONICAL) primary
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Service
- Sub-category
- Data Warehouse Service
- Vendor
- Amazon Web Services
- License
- proprietary
- Year introduced
- 2012
- Confidence
- 0.97
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Commonly listed in data/analytics job descriptions and widely used as AWS’s managed warehouse; strong vendor adoption and steady JD volume signal broad market demand.
Skill profile (library / DB)
- Skill nature
- CLOUD_SERVICE
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 11
- Sub-category id
- 118
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
Cloud Data Warehouses Catalog dimension db id 22
Library dimension (catalog)
Roles linked in library: Data Engineer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Cloud Data Warehouses
cloud-data-warehouses
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved |
Aliases — catalog
- Amazon S3 (CANONICAL) primary
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Service
- Sub-category
- Object Storage Service
- Vendor
- Amazon Web Services
- License
- proprietary
- Year introduced
- 2006
- Confidence
- 0.98
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Amazon S3 is a standard cloud storage service widely listed in job descriptions and core AWS certifications; it remains a default object-storage choice rather than a niche or sunset product.
Skill profile (library / DB)
- Skill nature
- CLOUD_SERVICE
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 11
- Sub-category id
- 120
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
Cloud Storage and Data Services Catalog dimension db id 144
Library dimension (catalog)
Roles linked in library: Cloud Architect
-
Cloud Storage and File Formats Catalog dimension db id 35
Library dimension (catalog)
Roles linked in library: Data Engineer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Cloud Storage and Data Services
cloud-storage-and-data-services
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Cloud Storage and File Formats
cloud-storage-and-file-formats
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved |
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Cloud Platforms
- Sub-category
- Data Engineering Tools
- Skill nature
- PLATFORM
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Aliases — catalog
- AWS Lambda (CANONICAL) primary
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Service
- Sub-category
- Serverless Function Service
- Vendor
- Amazon Web Services
- License
- other_open
- Year introduced
- 2014
- Confidence
- 0.98
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Broadly listed in cloud/backend JDs and AWS’s serverless docs/marketplace; it’s a standard 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
- 1704
- 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 & 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
-
Cloud Platforms & Services Catalog dimension db id 296
Library dimension (catalog)
Roles linked in library: Python Backend Developer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Cloud Platforms
cloud-platforms
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved |
|
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) |
|
Cloud Platforms & Services
cloud-platforms-services
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
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 |
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
- Apache Airflow (CANONICAL) primary
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Tool
- Sub-category
- Workflow Orchestration Tool
- Vendor
- Apache Software Foundation
- License
- apache_2
- Year introduced
- 2015
- Confidence
- 0.98
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Frequently listed in data engineering JDs and widely adopted for workflow orchestration; strong GitHub activity and managed offerings from AWS/GCP/Azure signal broad market demand.
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)
-
Data Pipeline Orchestration Catalog dimension db id 23
Library dimension (catalog)
Roles linked in library: Data Engineer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Data Pipeline Orchestration
data-pipeline-orchestration
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved |
Aliases — catalog
- Hadoop (CANONICAL)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Framework
- Sub-category
- Data Processing Framework
- Vendor
- Apache Software Foundation
- License
- apache_2
- Year introduced
- 2006
- Confidence
- 0.90
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Job postings still mention Hadoop for legacy big-data stacks, but JD volume has fallen as Spark and cloud warehouses replaced MapReduce-era clusters.
Skill profile (library / DB)
- Skill nature
- FRAMEWORK
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 5
- Sub-category id
- 91
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
ETL and ELT Tooling Catalog dimension db id 24
Library dimension (catalog)
Roles linked in library: Data Engineer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
ETL and ELT Tooling
etl-and-elt-tooling
|
— | — |
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
- Cloud Platforms
- Sub-category
- Data Engineering Tools
- Skill nature
- PLATFORM
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
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
- Data Lakes (CANONICAL)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Architecture
- Sub-category
- Data Lake Architecture
- Confidence
- 0.90
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Data lakes are widely listed in cloud/data platform job descriptions and are a standard architecture in AWS, Azure, and GCP ecosystems; they’re a common hiring-pipeline staple rather than a niche pattern.
Skill profile (library / DB)
- Skill nature
- PATTERN
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 1
- Sub-category id
- 1025
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
Cloud Storage and Data Services Catalog dimension db id 144
Library dimension (catalog)
Roles linked in library: Cloud Architect
-
React Frontend Development Catalog dimension db id 96
Library dimension (catalog)
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Cloud Storage and Data Services
cloud-storage-and-data-services
|
— | — |
Skipped — no persistable v3 meta for new skill
skill_not_in_db_v3_proposed
|
|
React Frontend Development
d_init_01
|
— | — |
Skipped — no persistable v3 meta for new skill
skill_not_in_db_v3_proposed
|
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Databases
- Sub-category
- general
- Skill nature
- CONCEPT
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Version strategy
- UNVERSIONED
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Data Engineering Tools
- 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
- Agile (CANONICAL) primary
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Methodology
- Sub-category
- Agile
- Confidence
- 0.99
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Agile appears in a large share of software job descriptions and is a standard hiring-pipeline requirement; Scrum/Kanban are commonly listed alongside it, showing broad market adoption.
Skill profile (library / DB)
- Skill nature
- METHODOLOGY
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 8
- Sub-category id
- 367
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
React Frontend Development Catalog dimension db id 96
Library dimension (catalog)
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
React Frontend Development
d_init_01
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
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 |
|---|---|---|---|---|---|---|
| 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) | |
| Amazon Redshift | in_db |
Cloud Data Warehouses
cloud-data-warehouses
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved | |
| Amazon S3 | in_db |
Cloud Storage and Data Services
cloud-storage-and-data-services
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Amazon S3 | in_db |
Cloud Storage and File Formats
cloud-storage-and-file-formats
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved | |
| AWS Lambda | in_db |
Cloud Platforms
cloud-platforms
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved | |
| AWS Lambda | in_db |
Cloud Platforms & Hosting Providers
cloud-platforms-hosting-providers
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| AWS Lambda | in_db |
Cloud Platforms & Managed Services
cloud-platforms-managed-services
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| AWS Lambda | in_db |
Cloud Platforms & Services
cloud-platforms-services
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| SQL | in_db |
Pega Programming Languages & DSLs
pega-programming-languages-dsls
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| SQL | in_db |
Programming Languages for Data Work
programming-languages-for-data-work
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved | |
| PySpark | new |
ETL and ELT Tooling
etl-and-elt-tooling
|
— | — | Skipped — no persistable v3 meta for new skill | skill_not_in_db_v3_proposed |
| Apache Airflow | in_db |
Data Pipeline Orchestration
data-pipeline-orchestration
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved | |
| Apache Hadoop | new |
ETL and ELT Tooling
etl-and-elt-tooling
|
— | — | Skipped — no persistable v3 meta for new skill | skill_not_in_db_v3_proposed |
| Databricks | in_db |
React Frontend Development
d_init_01
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Data Lake | new |
Cloud Storage and Data Services
cloud-storage-and-data-services
|
— | — | Skipped — no persistable v3 meta for new skill | skill_not_in_db_v3_proposed |
| Data Lake | new |
React Frontend Development
d_init_01
|
— | — | Skipped — no persistable v3 meta for new skill | skill_not_in_db_v3_proposed |
| 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) | |
| Agile | 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 | AWS Glue | type=Cloud Platforms subtype=Data Engineering Tools nature=PLATFORM lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Azure Data Factory | type=Cloud Platforms subtype=Data Engineering Tools nature=PLATFORM lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Data Warehouse | type=Databases subtype=general nature=CONCEPT lifespan=EVERGREEN | |
| canonical_skill_proposed | Data Pipeline | type=Data Engineering Tools subtype=general nature=CONCEPT lifespan=MULTI_YEAR | |
| dimension_skill_link_proposed | PySpark ↔ ETL and ELT Tooling | |
| role_dimension_link_proposed | Data Engineer ↔ ETL and ELT Tooling | |
| dimension_skill_link_proposed | Apache Hadoop ↔ ETL and ELT Tooling | |
| role_dimension_link_proposed | Data Engineer ↔ ETL and ELT Tooling | |
| dimension_skill_link_proposed | Data Lake ↔ Cloud Storage and Data Services | |
| dimension_skill_link_proposed | Data Lake ↔ React Frontend Development |
nano JD Parser — gpt-4.1-nano click to toggle
Show raw JSON
{
"JD_type": "pass",
"about_company": {
"source_marker": {
"first_5_words": "At PwC, we believe in",
"last_5_words": "based on the above considerations."
},
"text": "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\u2019s growth. To enable this, we have zero tolerance for any discrimination and harassment based on the above considerations.",
"word_count": 84
},
"certifications": [],
"company_name": "PwC",
"ctc": null,
"domain": {
"primary": {
"aliases": [],
"domain": "IT Services \u0026 Consulting"
},
"secondary": null
},
"education": [
{
"level": "Bachelor\u0027s",
"qualification": "BTECH/BE - Engineering",
"raw": "Bachelor of Engineering",
"requirement": "required"
},
{
"level": "Master\u0027s",
"qualification": "MBA - Business Administration",
"raw": "Master of Business Administration",
"requirement": "required"
}
],
"experience": {
"max": 8,
"min": 3,
"raw": "3-8"
},
"job_locations": [],
"role": "Senior Associate",
"role_aliases": [
"Data Engineer",
"AWS Data Engineer"
],
"role_archetype": "Data",
"roles_and_responsibilities": [
{
"bullet_count": 6,
"heading": "Responsibilities",
"heading_was_present": true,
"source_marker": {
"first_5_words": "Design and build data pipelines",
"last_5_words": "SQL, PySpark, SQL"
},
"text": "- Design and build data pipelines \u0026 Data lakes to automate ingestion of structured and unstructured data that provide fast, optimized, and robust end-to-end solutions\n- Knowledge about the concepts of data lake and data warehouse\n- Experience working with AWS big data technologies\n- Improve the data quality and reliability of data pipelines through monitoring, validation and failure detection.\n- Deploy and configure components to production environments\n- Technology: Redshift, S3, AWS Glue, Lambda, SQL, PySpark, SQL",
"word_count": 66
},
{
"bullet_count": 0,
"heading": "Mandatory skill sets",
"heading_was_present": true,
"source_marker": {
"first_5_words": "AWS Data Engineer",
"last_5_words": "AWS Data Engineer"
},
"text": "AWS Data Engineer",
"word_count": 3
},
{
"bullet_count": 0,
"heading": "Preferred skill sets",
"heading_was_present": true,
"source_marker": {
"first_5_words": "AWS Data Engineer",
"last_5_words": "AWS Data Engineer"
},
"text": "AWS Data Engineer",
"word_count": 3
},
{
"bullet_count": 0,
"heading": "Required Skills",
"heading_was_present": true,
"source_marker": {
"first_5_words": "AWS Devops",
"last_5_words": "AWS Devops"
},
"text": "AWS Devops",
"word_count": 2
},
{
"bullet_count": 0,
"heading": "Optional Skills",
"heading_was_present": true,
"source_marker": {
"first_5_words": "Accepting Feedback, Accepting Feedback,",
"last_5_words": "Data Pipeline {+ 27 more}"
},
"text": "Accepting Feedback, Accepting Feedback, Active Listening, Agile Scalability, Amazon Web Services (AWS), Analytical Thinking, Apache Airflow, Apache Hadoop, Azure Data Factory, 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, Data Pipeline {+ 27 more}",
"word_count": 56
}
],
"urls": []
}
API 1 — extract-from-jd click to toggle
{
"final_skills": [
{
"is_primary": true,
"skill_name": "AWS"
},
{
"is_primary": true,
"skill_name": "Amazon Redshift"
},
{
"is_primary": true,
"skill_name": "Amazon S3"
},
{
"is_primary": true,
"skill_name": "AWS Glue"
},
{
"is_primary": true,
"skill_name": "AWS Lambda"
},
{
"is_primary": true,
"skill_name": "SQL"
},
{
"is_primary": true,
"skill_name": "PySpark"
},
{
"is_primary": false,
"skill_name": "Apache Airflow"
},
{
"is_primary": false,
"skill_name": "Apache Hadoop"
},
{
"is_primary": false,
"skill_name": "Azure Data Factory"
},
{
"is_primary": false,
"skill_name": "Databricks"
},
{
"is_primary": false,
"skill_name": "Data Lake"
},
{
"is_primary": false,
"skill_name": "Data Warehouse"
},
{
"is_primary": false,
"skill_name": "Data Pipeline"
},
{
"is_primary": false,
"skill_name": "DevOps"
},
{
"is_primary": false,
"skill_name": "Agile"
}
],
"jd_role": {
"display_name": "Senior Associate",
"rationale": null,
"role_aliases": [
"Data Engineer",
"AWS Data Engineer"
],
"role_archetype": "Data",
"slug": ""
},
"nano_parsed": {
"JD_type": "pass",
"about_company": {
"source_marker": {
"first_5_words": "At PwC, we believe in",
"last_5_words": "based on the above considerations."
},
"text": "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\u2019s growth. To enable this, we have zero tolerance for any discrimination and harassment based on the above considerations.",
"word_count": 84
},
"certifications": [],
"company_name": "PwC",
"ctc": null,
"domain": {
"primary": {
"aliases": [],
"domain": "IT Services \u0026 Consulting"
},
"secondary": null
},
"education": [
{
"level": "Bachelor\u0027s",
"qualification": "BTECH/BE - Engineering",
"raw": "Bachelor of Engineering",
"requirement": "required"
},
{
"level": "Master\u0027s",
"qualification": "MBA - Business Administration",
"raw": "Master of Business Administration",
"requirement": "required"
}
],
"experience": {
"max": 8,
"min": 3,
"raw": "3-8"
},
"job_locations": [],
"role": "Senior Associate",
"role_aliases": [
"Data Engineer",
"AWS Data Engineer"
],
"role_archetype": "Data",
"roles_and_responsibilities": [
{
"bullet_count": 6,
"heading": "Responsibilities",
"heading_was_present": true,
"source_marker": {
"first_5_words": "Design and build data pipelines",
"last_5_words": "SQL, PySpark, SQL"
},
"text": "- Design and build data pipelines \u0026 Data lakes to automate ingestion of structured and unstructured data that provide fast, optimized, and robust end-to-end solutions\n- Knowledge about the concepts of data lake and data warehouse\n- Experience working with AWS big data technologies\n- Improve the data quality and reliability of data pipelines through monitoring, validation and failure detection.\n- Deploy and configure components to production environments\n- Technology: Redshift, S3, AWS Glue, Lambda, SQL, PySpark, SQL",
"word_count": 66
},
{
"bullet_count": 0,
"heading": "Mandatory skill sets",
"heading_was_present": true,
"source_marker": {
"first_5_words": "AWS Data Engineer",
"last_5_words": "AWS Data Engineer"
},
"text": "AWS Data Engineer",
"word_count": 3
},
{
"bullet_count": 0,
"heading": "Preferred skill sets",
"heading_was_present": true,
"source_marker": {
"first_5_words": "AWS Data Engineer",
"last_5_words": "AWS Data Engineer"
},
"text": "AWS Data Engineer",
"word_count": 3
},
{
"bullet_count": 0,
"heading": "Required Skills",
"heading_was_present": true,
"source_marker": {
"first_5_words": "AWS Devops",
"last_5_words": "AWS Devops"
},
"text": "AWS Devops",
"word_count": 2
},
{
"bullet_count": 0,
"heading": "Optional Skills",
"heading_was_present": true,
"source_marker": {
"first_5_words": "Accepting Feedback, Accepting Feedback,",
"last_5_words": "Data Pipeline {+ 27 more}"
},
"text": "Accepting Feedback, Accepting Feedback, Active Listening, Agile Scalability, Amazon Web Services (AWS), Analytical Thinking, Apache Airflow, Apache Hadoop, Azure Data Factory, 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, Data Pipeline {+ 27 more}",
"word_count": 56
}
],
"urls": []
},
"rejected": false,
"rejection_reason": null,
"run_id": "939a51b7-48cd-45d5-a944-5263b1a54bd7",
"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": "Builds data ingestion pipelines to collect data from transactional databases, third-party APIs, event streams, and file sources into centralized data platforms.",
"sentence": "Design and build data pipelines \u0026 Data lakes to automate ingestion of structured and unstructured data that provide fast, optimized, and robust end-to-end solutions",
"similarity": 0.6674
},
{
"kra_text": "Monitors pipeline health, SLA breach alerts, and job failure notifications, and performs root cause analysis for data pipeline incidents.",
"sentence": "Improve the data quality and reliability of data pipelines through monitoring, validation and failure detection.",
"similarity": 0.6488
},
{
"kra_text": "Optimizes pipeline throughput, partitioning strategies, and query performance across cloud data warehouses like Snowflake, BigQuery, or Redshift.",
"sentence": "Technology: Redshift, S3, AWS Glue, Lambda, SQL, PySpark, SQL",
"similarity": 0.5796
}
],
"matched_count": null,
"matched_skills": null,
"role_id": 2,
"score": 0.6319,
"slug": "data-engineer",
"total_count": null
},
{
"display_name": "DevOps Engineer",
"kra_matches": [
{
"kra_text": "Monitors CI/CD pipeline reliability, identifies bottlenecks in delivery workflows, and improves deployment frequency, lead time, and failure recovery rate.",
"sentence": "Improve the data quality and reliability of data pipelines through monitoring, validation and failure detection.",
"similarity": 0.6486
},
{
"kra_text": "Sets up and maintains development, staging, and production environments with consistent configuration management using Ansible, Puppet, or Chef.",
"sentence": "Deploy and configure components to production environments",
"similarity": 0.5736
},
{
"kra_text": "Provisions and manages cloud infrastructure on AWS, Azure, or GCP using Terraform or CloudFormation to enforce infrastructure-as-code standards.",
"sentence": "Experience working with AWS big data technologies",
"similarity": 0.4203
}
],
"matched_count": null,
"matched_skills": null,
"role_id": 10,
"score": 0.5475,
"slug": "devops-engineer",
"total_count": null
},
{
"display_name": "MLOps Engineer",
"kra_matches": [
{
"kra_text": "Coordinates model promotion workflows across development, staging, and production environments including integration testing and data contract validation.",
"sentence": "Deploy and configure components to production environments",
"similarity": 0.4957
},
{
"kra_text": "Validates model performance benchmarks, data schema contracts, and system integration health before signing off on production release readiness.",
"sentence": "Improve the data quality and reliability of data pipelines through monitoring, validation and failure detection.",
"similarity": 0.4881
},
{
"kra_text": "Automates ML platform operations including scheduled retraining triggers, pipeline orchestration, evaluation workflows, and alerting configuration.",
"sentence": "Design and build data pipelines \u0026 Data lakes to automate ingestion of structured and unstructured data that provide fast, optimized, and robust end-to-end solutions",
"similarity": 0.4424
}
],
"matched_count": null,
"matched_skills": null,
"role_id": 16,
"score": 0.4754,
"slug": "ml-ops-engineer",
"total_count": null
},
{
"display_name": "ML Engineer",
"kra_matches": [
{
"kra_text": "Designs end-to-end ML training pipelines and model inference workflows using TensorFlow, PyTorch, or scikit-learn on cloud ML platforms.",
"sentence": "Design and build data pipelines \u0026 Data lakes to automate ingestion of structured and unstructured data that provide fast, optimized, and robust end-to-end solutions",
"similarity": 0.4967
},
{
"kra_text": "Monitors production model behavior for data drift, concept drift, and prediction performance degradation using monitoring dashboards and alerting.",
"sentence": "Improve the data quality and reliability of data pipelines through monitoring, validation and failure detection.",
"similarity": 0.4908
},
{
"kra_text": "Prepares, cleans, and transforms training datasets, manages feature stores, and builds feature engineering pipelines for model training.",
"sentence": "Experience working with AWS big data technologies",
"similarity": 0.412
}
],
"matched_count": null,
"matched_skills": null,
"role_id": 3,
"score": 0.4665,
"slug": "ml-engineer",
"total_count": null
},
{
"display_name": "Backend Developer",
"kra_matches": [
{
"kra_text": "Configures Docker containers, deployment descriptors, environment variables, and CI/CD pipeline stages for backend service releases.",
"sentence": "Deploy and configure components to production environments",
"similarity": 0.5355
},
{
"kra_text": "Identifies and resolves backend performance bottlenecks through query optimization, indexing strategies, connection pooling, and distributed caching with Redis.",
"sentence": "Technology: Redshift, S3, AWS Glue, Lambda, SQL, PySpark, SQL",
"similarity": 0.4345
},
{
"kra_text": "Adds structured logging, metrics, distributed tracing, and alerting to improve system observability and support production debugging.",
"sentence": "Accepting Feedback, Accepting Feedback, Active Listening, Agile Scalability, Amazon Web Services (AWS), Analytical Thinking, Apache Airflow, Apache Hadoop, Azure Data Factory, 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, Data Pipeline {+ 27 more}",
"similarity": 0.4152
}
],
"matched_count": null,
"matched_skills": null,
"role_id": 1,
"score": 0.4617,
"slug": "backend-engineer",
"total_count": null
}
],
"skill_match_roles": [
{
"display_name": "Data Engineer",
"kra_matches": null,
"matched_count": 5,
"matched_skills": [
"AWS",
"AWS Lambda",
"Amazon Redshift",
"Amazon S3",
"SQL"
],
"role_id": 2,
"score": 0.7143,
"slug": "data-engineer",
"total_count": 7
},
{
"display_name": "ML Engineer",
"kra_matches": null,
"matched_count": 2,
"matched_skills": [
"AWS",
"AWS Lambda"
],
"role_id": 3,
"score": 0.2857,
"slug": "ml-engineer",
"total_count": 7
},
{
"display_name": "Cyber Security Engineer",
"kra_matches": null,
"matched_count": 2,
"matched_skills": [
"AWS",
"AWS Lambda"
],
"role_id": 5,
"score": 0.2857,
"slug": "cybersecurity-engineer",
"total_count": 7
},
{
"display_name": "Cloud Architect",
"kra_matches": null,
"matched_count": 2,
"matched_skills": [
"AWS",
"Amazon S3"
],
"role_id": 9,
"score": 0.2857,
"slug": "cloud-architect",
"total_count": 7
},
{
"display_name": "Backend Developer",
"kra_matches": null,
"matched_count": 2,
"matched_skills": [
"AWS",
"AWS Lambda"
],
"role_id": 1,
"score": 0.2857,
"slug": "backend-engineer",
"total_count": 7
}
]
},
"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 0.71 does not contradict",
"sub_role": null
},
"stage5_updates": {
"centroid_n_after": 62,
"centroid_updated": true,
"collision_log_id": null,
"new_kra_attached": null,
"new_skills_attached": [
{
"is_primary": true,
"queue_id": 3949,
"role_display_name": "Data Engineer",
"role_slug": "data-engineer",
"skill_name": "AWS Glue",
"status": "pending"
},
{
"is_primary": true,
"queue_id": 3950,
"role_display_name": "Data Engineer",
"role_slug": "data-engineer",
"skill_name": "PySpark",
"status": "pending"
},
{
"is_primary": false,
"queue_id": 3951,
"role_display_name": "Data Engineer",
"role_slug": "data-engineer",
"skill_name": "Apache Hadoop",
"status": "pending"
},
{
"is_primary": false,
"queue_id": 3952,
"role_display_name": "Data Engineer",
"role_slug": "data-engineer",
"skill_name": "Azure Data Factory",
"status": "pending"
},
{
"is_primary": false,
"queue_id": 3953,
"role_display_name": "Data Engineer",
"role_slug": "data-engineer",
"skill_name": "Data Lake",
"status": "pending"
},
{
"is_primary": false,
"queue_id": 3954,
"role_display_name": "Data Engineer",
"role_slug": "data-engineer",
"skill_name": "Data Warehouse",
"status": "pending"
},
{
"is_primary": false,
"queue_id": 3955,
"role_display_name": "Data Engineer",
"role_slug": "data-engineer",
"skill_name": "Data Pipeline",
"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": 406,
"existing_alias_text": "AWS",
"input_term": "AWS",
"matched_canonical": {
"category_id": 9,
"display_name": "AWS",
"id": 187,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "PLATFORM",
"slug": "aws",
"sub_category_id": 46,
"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"matched_via": "alias"
},
{
"alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
"alias_persisted": false,
"existing_alias_id": 301,
"existing_alias_text": "Amazon Redshift",
"input_term": "Amazon Redshift",
"matched_canonical": {
"category_id": 11,
"display_name": "Amazon Redshift",
"id": 107,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "CLOUD_SERVICE",
"slug": "amazon-redshift",
"sub_category_id": 118,
"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": 379,
"existing_alias_text": "Amazon S3",
"input_term": "Amazon S3",
"matched_canonical": {
"category_id": 11,
"display_name": "Amazon S3",
"id": 170,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "CLOUD_SERVICE",
"slug": "amazon-s3",
"sub_category_id": 120,
"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"matched_via": "alias"
},
{
"alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
"alias_persisted": false,
"existing_alias_id": 3563,
"existing_alias_text": "AWS Lambda",
"input_term": "AWS Lambda",
"matched_canonical": {
"category_id": 11,
"display_name": "AWS Lambda",
"id": 2295,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "CLOUD_SERVICE",
"slug": "aws-lambda",
"sub_category_id": 1704,
"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"matched_via": "alias"
},
{
"alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
"alias_persisted": false,
"existing_alias_id": 271,
"existing_alias_text": "SQL",
"input_term": "SQL",
"matched_canonical": {
"category_id": 6,
"display_name": "SQL",
"id": 101,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "LANGUAGE",
"slug": "sql",
"sub_category_id": 97,
"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"matched_via": "alias"
},
{
"alias_persist_skipped_reason": "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": {
"category_id": 5,
"display_name": "Apache Spark",
"id": 1350,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "FRAMEWORK",
"slug": "apache-spark",
"sub_category_id": 1021,
"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"matched_via": "embedding_alias"
},
{
"alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
"alias_persisted": false,
"existing_alias_id": 304,
"existing_alias_text": "Apache Airflow",
"input_term": "Apache Airflow",
"matched_canonical": {
"category_id": 13,
"display_name": "Apache Airflow",
"id": 110,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "TOOL",
"slug": "apache-airflow",
"sub_category_id": 130,
"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": 2010,
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},
"matched_via": "alias"
},
{
"alias_persist_skipped_reason": "TODO: REMOVE AFTER TESTING \u2014 alias DB write disabled",
"alias_persisted": false,
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"input_term": "Data Lake",
"matched_canonical": {
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"matched_via": "embedding_alias"
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"alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
"alias_persisted": false,
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],
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{
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{
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]
},
{
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{
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},
{
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"slug": "cloud-security-posture-tools",
"source": "db"
},
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{
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{
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]
},
{
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]
},
{
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},
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{
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},
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"rationale": "Familiarity with vendor-specific hosting and backend services for deploying and scaling web applications.",
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{
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]
},
{
"dimension": {
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"rationale": "Operates and integrates vendor-specific cloud compute, storage, and hosting services.",
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{
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{
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{
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]
},
{
"dimension": {
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"rationale": "Deploying, configuring, and managing Python backends on major cloud and PaaS offerings.",
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},
"input_skill": "AWS Lambda",
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{
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]
},
{
"dimension": {
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"display_name": "Pega Programming Languages \u0026 DSLs",
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"source": "db"
},
"input_skill": "SQL",
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"roles_from_db": [
{
"display_name": "Pega Developer",
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"role_archetype": null,
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]
},
{
"dimension": {
"difficulty_hint": "well_known",
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"rationale": "Languages used to implement data pipelines, transformations, and operational glue. This is the primary coding surface for building ingestion, enrichment, and automation logic in data engineering.",
"slug": "programming-languages-for-data-work",
"source": "db"
},
"input_skill": "SQL",
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"roles_from_db": [
{
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},
{
"dimension": {
"difficulty_hint": "well_known",
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"rationale": "Packaged tools for extracting, loading, and transforming data across systems. This dimension covers connector-based ingestion, transformation frameworks, and managed integration products.",
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"source": "db"
},
"input_skill": "PySpark",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Data Engineer",
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"role_archetype": null,
"slug": "data-engineer",
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}
]
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Data Pipeline Orchestration",
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"rationale": "Workflow engines that schedule, coordinate, and recover batch data jobs. This cluster covers dependency management, retries, backfills, sensors, and operational control of pipeline DAGs.",
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"source": "db"
},
"input_skill": "Apache Airflow",
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"roles_from_db": [
{
"display_name": "Data Engineer",
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"rationale": null,
"role_archetype": null,
"slug": "data-engineer",
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}
]
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "ETL and ELT Tooling",
"id": 24,
"rationale": "Packaged tools for extracting, loading, and transforming data across systems. This dimension covers connector-based ingestion, transformation frameworks, and managed integration products.",
"slug": "etl-and-elt-tooling",
"source": "db"
},
"input_skill": "Apache Hadoop",
"llm_role": null,
"roles_from_db": [
{
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"input_skill": "SQL",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Pega Developer",
"id": 24,
"rationale": null,
"role_archetype": null,
"slug": "pega-developer",
"source": "db"
}
]
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Programming Languages for Data Work",
"id": 21,
"rationale": "Languages used to implement data pipelines, transformations, and operational glue. This is the primary coding surface for building ingestion, enrichment, and automation logic in data engineering.",
"slug": "programming-languages-for-data-work",
"source": "db"
},
"input_skill": "SQL",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Data Engineer",
"id": 2,
"rationale": null,
"role_archetype": null,
"slug": "data-engineer",
"source": "db"
}
]
}
],
"input_skill": "SQL",
"matched_via": "alias",
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": null,
"source_tag": "db",
"was_in_llm_skills": true
},
{
"aliases_in_db": [
{
"alias_text": "Apache Spark",
"alias_type": "CANONICAL",
"id": 2004,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "apache spark 3",
"alias_type": "VERSION",
"id": 2006,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "spark",
"alias_type": "VERSION",
"id": 2510,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "spark 3",
"alias_type": "VERSION",
"id": 2007,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "spark 3.x",
"alias_type": "VERSION",
"id": 2009,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "spark3",
"alias_type": "VERSION",
"id": 2008,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
}
],
"canonical": {
"category_id": 5,
"display_name": "Apache Spark",
"id": 1350,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "FRAMEWORK",
"slug": "apache-spark",
"sub_category_id": 1021,
"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"dimensions": [
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "ETL and ELT Tooling",
"id": 24,
"rationale": "Packaged tools for extracting, loading, and transforming data across systems. This dimension covers connector-based ingestion, transformation frameworks, and managed integration products.",
"slug": "etl-and-elt-tooling",
"source": "db"
},
"input_skill": "PySpark",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Data Engineer",
"id": 2,
"rationale": null,
"role_archetype": null,
"slug": "data-engineer",
"source": "db"
}
]
}
],
"input_skill": "PySpark",
"matched_via": "embedding_alias",
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": null,
"source_tag": "db",
"was_in_llm_skills": true
},
{
"aliases_in_db": [
{
"alias_text": "Apache Airflow",
"alias_type": "CANONICAL",
"id": 304,
"is_primary": true,
"match_strategy": "CASE_INSENSITIVE"
}
],
"canonical": {
"category_id": 13,
"display_name": "Apache Airflow",
"id": 110,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "TOOL",
"slug": "apache-airflow",
"sub_category_id": 130,
"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"dimensions": [
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Data Pipeline Orchestration",
"id": 23,
"rationale": "Workflow engines that schedule, coordinate, and recover batch data jobs. This cluster covers dependency management, retries, backfills, sensors, and operational control of pipeline DAGs.",
"slug": "data-pipeline-orchestration",
"source": "db"
},
"input_skill": "Apache Airflow",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Data Engineer",
"id": 2,
"rationale": null,
"role_archetype": null,
"slug": "data-engineer",
"source": "db"
}
]
}
],
"input_skill": "Apache Airflow",
"matched_via": "alias",
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": null,
"source_tag": "db",
"was_in_llm_skills": true
},
{
"aliases_in_db": [
{
"alias_text": "Hadoop",
"alias_type": "CANONICAL",
"id": 2010,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
}
],
"canonical": {
"category_id": 5,
"display_name": "Hadoop",
"id": 1351,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "FRAMEWORK",
"slug": "hadoop",
"sub_category_id": 91,
"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"dimensions": [
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "ETL and ELT Tooling",
"id": 24,
"rationale": "Packaged tools for extracting, loading, and transforming data across systems. This dimension covers connector-based ingestion, transformation frameworks, and managed integration products.",
"slug": "etl-and-elt-tooling",
"source": "db"
},
"input_skill": "Apache Hadoop",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Data Engineer",
"id": 2,
"rationale": null,
"role_archetype": null,
"slug": "data-engineer",
"source": "db"
}
]
}
],
"input_skill": "Apache Hadoop",
"matched_via": "embedding_alias",
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": null,
"source_tag": "db",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [],
"input_skill": "Azure Data Factory",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": {
"derived": {
"category": "Cloud Platforms",
"skill_nature": "PLATFORM",
"sub_category": "Data Engineering Tools",
"typical_lifespan": "MULTI_YEAR",
"version_strategy": "UNVERSIONED",
"volatility": "MEDIUM"
},
"enrichment": null,
"keep_log": [],
"locked_dimensions": [],
"merge_log": [],
"placed": null,
"relationships": null,
"skill_id": "azure-data-factory",
"split_log": [],
"typed": null,
"warnings": []
},
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [
{
"alias_text": "Databricks",
"alias_type": "CANONICAL",
"id": 1838,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
}
],
"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"
},
"dimensions": [
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "React Frontend Development",
"id": 96,
"rationale": "Building interactive web user interfaces with React.js, including component composition, state management, hooks, and rendering patterns. React.js belongs here because it is a core library for client-side UI development in modern web applications.",
"slug": "d_init_01",
"source": "db"
},
"input_skill": "Databricks",
"llm_role": null,
"roles_from_db": []
}
],
"input_skill": "Databricks",
"matched_via": "alias",
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": null,
"source_tag": "db",
"was_in_llm_skills": true
},
{
"aliases_in_db": [
{
"alias_text": "Data Lakes",
"alias_type": "CANONICAL",
"id": 2017,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
}
],
"canonical": {
"category_id": 1,
"display_name": "Data Lakes",
"id": 1358,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "PATTERN",
"slug": "data-lakes",
"sub_category_id": 1025,
"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"dimensions": [
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Cloud Storage and Data Services",
"id": 144,
"rationale": "Cloud-native storage and managed data services used to place workloads, choose durability tiers, and define platform boundaries. This is a coherent cluster because architects evaluate storage fit, access patterns, and managed service tradeoffs.",
"slug": "cloud-storage-and-data-services",
"source": "db"
},
"input_skill": "Data Lake",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Cloud Architect",
"id": 9,
"rationale": null,
"role_archetype": null,
"slug": "cloud-architect",
"source": "db"
}
]
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "React Frontend Development",
"id": 96,
"rationale": "Building interactive web user interfaces with React.js, including component composition, state management, hooks, and rendering patterns. React.js belongs here because it is a core library for client-side UI development in modern web applications.",
"slug": "d_init_01",
"source": "db"
},
"input_skill": "Data Lake",
"llm_role": null,
"roles_from_db": []
}
],
"input_skill": "Data Lake",
"matched_via": "embedding_alias",
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": null,
"source_tag": "db",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [],
"input_skill": "Data Warehouse",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": {
"derived": {
"category": "Databases",
"skill_nature": "CONCEPT",
"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": "data-warehouse",
"split_log": [],
"typed": null,
"warnings": []
},
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [],
"input_skill": "Data Pipeline",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": {
"derived": {
"category": "Data Engineering Tools",
"skill_nature": "CONCEPT",
"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": "data-pipeline",
"split_log": [],
"typed": null,
"warnings": []
},
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [
{
"alias_text": "DevOps",
"alias_type": "CANONICAL",
"id": 1852,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
}
],
"canonical": {
"category_id": 8,
"display_name": "DevOps",
"id": 1216,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "METHODOLOGY",
"slug": "devops",
"sub_category_id": 922,
"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"dimensions": [
{
"dimension": {
"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": "DevOps",
"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": "Deployment and Release Patterns",
"id": 140,
"rationale": "Patterns for promoting changes safely across environments, including rollout, rollback, and release gating strategies. Cloud Architects define these patterns so teams can deploy consistently across the platform.",
"slug": "deployment-and-release-patterns",
"source": "db"
},
"input_skill": "DevOps",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Cloud Architect",
"id": 9,
"rationale": null,
"role_archetype": null,
"slug": "cloud-architect",
"source": "db"
}
]
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Infrastructure as Code",
"id": 132,
"rationale": "Declarative provisioning and environment definition tools used to codify cloud infrastructure, repeatable environments, and platform standards. Cloud Architects use these to express reference architectures and guardrails.",
"slug": "infrastructure-as-code",
"source": "db"
},
"input_skill": "DevOps",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Cloud Architect",
"id": 9,
"rationale": null,
"role_archetype": null,
"slug": "cloud-architect",
"source": "db"
},
{
"display_name": "DevOps Engineer",
"id": 10,
"rationale": null,
"role_archetype": null,
"slug": "devops-engineer",
"source": "db"
}
]
}
],
"input_skill": "DevOps",
"matched_via": "alias",
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": null,
"source_tag": "db",
"was_in_llm_skills": true
},
{
"aliases_in_db": [
{
"alias_text": "Agile",
"alias_type": "CANONICAL",
"id": 868,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
}
],
"canonical": {
"category_id": 8,
"display_name": "Agile",
"id": 520,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "METHODOLOGY",
"slug": "agile",
"sub_category_id": 367,
"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"dimensions": [
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "React Frontend Development",
"id": 96,
"rationale": "Building interactive web user interfaces with React.js, including component composition, state management, hooks, and rendering patterns. React.js belongs here because it is a core library for client-side UI development in modern web applications.",
"slug": "d_init_01",
"source": "db"
},
"input_skill": "Agile",
"llm_role": null,
"roles_from_db": []
}
],
"input_skill": "Agile",
"matched_via": "alias",
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": null,
"source_tag": "db",
"was_in_llm_skills": true
}
],
"unmatched_skills": [
"AWS Glue",
"Azure Data Factory",
"Data Warehouse",
"Data Pipeline"
]
}
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 0.71 does not contradict",
"role_archetype": null,
"slug": "data-engineer",
"source": "db"
},
"chosen_role_resolution": "in_db",
"final_input_skills": [
{
"skill": "AWS",
"tag": "in_db"
},
{
"skill": "Amazon Redshift",
"tag": "in_db"
},
{
"skill": "Amazon S3",
"tag": "in_db"
},
{
"skill": "AWS Glue",
"tag": "new"
},
{
"skill": "AWS Lambda",
"tag": "in_db"
},
{
"skill": "SQL",
"tag": "in_db"
},
{
"skill": "PySpark",
"tag": "in_db"
},
{
"skill": "Apache Airflow",
"tag": "in_db"
},
{
"skill": "Apache Hadoop",
"tag": "in_db"
},
{
"skill": "Azure Data Factory",
"tag": "new"
},
{
"skill": "Databricks",
"tag": "in_db"
},
{
"skill": "Data Lake",
"tag": "in_db"
},
{
"skill": "Data Warehouse",
"tag": "new"
},
{
"skill": "Data Pipeline",
"tag": "new"
},
{
"skill": "DevOps",
"tag": "in_db"
},
{
"skill": "Agile",
"tag": "in_db"
}
],
"llm_cost_api1_usd": null,
"llm_cost_api2_usd": null,
"llm_cost_api3_usd": null,
"llm_cost_total_usd": null,
"persistence": {
"items": [
{
"chosen_role_id": 2,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Cloud Platforms",
"id": 20,
"rationale": "Underlying cloud providers that host the managed services or infrastructure used by the role, such as AWS, Azure, and GCP.",
"slug": "cloud-platforms",
"source": "db"
},
"dimension_id": 20,
"input_skill": "AWS",
"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": 187,
"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": "AWS",
"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": 187,
"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": "AWS",
"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"
},
{
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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": [
{
"display_name": "Cloud Security Engineer",
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"rationale": null,
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"slug": "cloud-security-engineer",
"source": "db"
},
{
"display_name": "Cyber Security Engineer",
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"role_archetype": null,
"slug": "cybersecurity-engineer",
"source": "db"
}
],
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},
{
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"display_name": "Cloud Data Warehouses",
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"source": "db"
},
"dimension_id": 22,
"input_skill": "Amazon Redshift",
"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": 107,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 2,
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"display_name": "Cloud Storage and Data Services",
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"rationale": "Cloud-native storage and managed data services used to place workloads, choose durability tiers, and define platform boundaries. This is a coherent cluster because architects evaluate storage fit, access patterns, and managed service tradeoffs.",
"slug": "cloud-storage-and-data-services",
"source": "db"
},
"dimension_id": 144,
"input_skill": "Amazon S3",
"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",
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}
],
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"skill_id": 170,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 2,
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"difficulty_hint": "well_known",
"display_name": "Cloud Storage and File Formats",
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"rationale": "Object storage and data file formats used as the physical substrate for data movement and lake-style analytics. Data engineers need these to manage landing zones, partitioned datasets, and efficient interchange.",
"slug": "cloud-storage-and-file-formats",
"source": "db"
},
"dimension_id": 35,
"input_skill": "Amazon S3",
"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": 170,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 2,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Cloud Platforms",
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"slug": "cloud-platforms",
"source": "db"
},
"dimension_id": 20,
"input_skill": "AWS Lambda",
"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",
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"slug": "backend-engineer",
"source": "db"
},
{
"display_name": "Cyber Security Engineer",
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"rationale": null,
"role_archetype": null,
"slug": "cybersecurity-engineer",
"source": "db"
},
{
"display_name": "Data Engineer",
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"rationale": null,
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"source": "db"
},
{
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"rationale": null,
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},
{
"display_name": "Fullstack Developer",
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"rationale": null,
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"source": "db"
},
{
"display_name": "Go Backend Developer",
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"source": "db"
},
{
"display_name": "Java Backend Developer",
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"slug": "java-backend-developer",
"source": "db"
},
{
"display_name": "Kotlin Backend Developer",
"id": 84,
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"slug": "kotlin-server-backend-developer",
"source": "db"
},
{
"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,
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"source": "db"
},
{
"display_name": "Python Backend Developer",
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"slug": "python-backend-developer",
"source": "db"
},
{
"display_name": "Scala Backend Developer",
"id": 87,
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}
],
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},
{
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"display_name": "Cloud Platforms \u0026 Hosting Providers",
"id": 278,
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"source": "db"
},
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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": ".NET Backend Developer",
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},
{
"display_name": "Kotlin Backend Developer",
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},
{
"display_name": "Scala Backend Developer",
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"source": "db"
},
{
"display_name": "Web Developer",
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"role_archetype": null,
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}
],
"skill_dimension_saved": true,
"skill_id": 2295,
"skill_tag": "in_db",
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},
{
"chosen_role_id": 2,
"dimension": {
"difficulty_hint": "well_known",
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"source": "db"
},
"dimension_id": 221,
"input_skill": "AWS Lambda",
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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": "Fullstack Developer",
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"slug": "full-stack-engineer",
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},
{
"display_name": "Go Backend Developer",
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"slug": "go-backend-developer",
"source": "db"
},
{
"display_name": "Node.js Backend Developer",
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"rationale": null,
"role_archetype": "Engineering",
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"source": "db"
}
],
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},
{
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"display_name": "Cloud Platforms \u0026 Services",
"id": 296,
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"source": "db"
},
"dimension_id": 296,
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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": "Python Backend Developer",
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"role_archetype": "Engineering",
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}
],
"skill_dimension_saved": true,
"skill_id": 2295,
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},
{
"chosen_role_id": 2,
"dimension": {
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"display_name": "Pega Programming Languages \u0026 DSLs",
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"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",
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}
],
"skill_dimension_saved": true,
"skill_id": 101,
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},
{
"chosen_role_id": 2,
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"difficulty_hint": "well_known",
"display_name": "Programming Languages for Data Work",
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"slug": "programming-languages-for-data-work",
"source": "db"
},
"dimension_id": 21,
"input_skill": "SQL",
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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": "Data Engineer",
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"role_archetype": null,
"slug": "data-engineer",
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}
],
"skill_dimension_saved": true,
"skill_id": 101,
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},
{
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"difficulty_hint": "well_known",
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"source": "db"
},
"dimension_id": 24,
"input_skill": "PySpark",
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"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",
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}
],
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},
{
"chosen_role_id": 2,
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"difficulty_hint": "well_known",
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"source": "db"
},
"dimension_id": 23,
"input_skill": "Apache Airflow",
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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": "Data Engineer",
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"slug": "data-engineer",
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}
],
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"skill_tag": "in_db",
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},
{
"chosen_role_id": 2,
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"difficulty_hint": "well_known",
"display_name": "ETL and ELT Tooling",
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"slug": "etl-and-elt-tooling",
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},
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"input_skill": "Apache Hadoop",
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"matched_chosen_role": true,
"outcome_line": "Skipped \u2014 no persistable v3 meta for new skill",
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"roles_from_db": [
{
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"slug": "data-engineer",
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}
],
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},
{
"chosen_role_id": 2,
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"display_name": "React Frontend Development",
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"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.",
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},
"dimension_id": 96,
"input_skill": "Databricks",
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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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"skill_id": 1202,
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},
{
"chosen_role_id": 2,
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},
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"matched_chosen_role": false,
"outcome_line": "Skipped \u2014 no persistable v3 meta for new skill",
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"roles_from_db": [
{
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}
],
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},
{
"chosen_role_id": 2,
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"difficulty_hint": "well_known",
"display_name": "React Frontend Development",
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"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": "Data Lake",
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"matched_chosen_role": false,
"outcome_line": "Skipped \u2014 no persistable v3 meta for new skill",
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"roles_from_db": [],
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},
{
"chosen_role_id": 2,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "CI/CD Pipeline Platforms",
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"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.",
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},
"dimension_id": 150,
"input_skill": "DevOps",
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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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}
],
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},
{
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},
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"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
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{
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],
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},
{
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"source": "db"
},
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"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",
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},
{
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}
],
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},
{
"chosen_role_id": 2,
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"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.",
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},
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"input_skill": "Agile",
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"matched_chosen_role": false,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [],
"skill_dimension_saved": true,
"skill_id": 520,
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}
],
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"planner_output": null,
"run_id": "939a51b7-48cd-45d5-a944-5263b1a54bd7"
}
LLM Calls
Every model call made for this run, in pipeline order. Click a card to see the model's response.