Pipeline run
d8ac84be-457c-4db6-877f-e9e89edde05c
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
Working in a permanent feature team, you’ll be developing knowledge of aspects of the associated platform across the disciplines of business, applications, data and infrastructure. You’ll also be liai…
1 POST /skills/extract-from-jd
2 POST /skills/extract-details
3 POST /skills/final-role-output
Backend Developer
→Python Backend Developer
sub-role · 0.98 domain · Software Engineering CASE DOMAINslug: backend-engineer · id: 1 · source: db · sub-role slug: python-backend-developer
Domain=Software Engineering → sub-role python-backend-developer; The JD centers on Python/PySpark, Spark SQL, AWS, Airflow, CI/DevOps, and software design/implementation for a platform backend rather than frontend, mobile, or infrastructure-only work.
Matched skills
Matched dimensions
Matched KRAs
Resolution:
in_db
— role exists in library; skill↔dim and role↔dim links saved when applicable.
Job description
Join us as a Software Engineer, PySpark • This is an opportunity for a technically minded individual to join us as a Software Engineer • You’ll be designing, producing, testing and implementing working software, working across the lifecycle of the system • Hone your existing software engineering skills and advance your career in this critical role • We're offering this role at vice president level What you'll do Working in a permanent feature team, you’ll be developing knowledge of aspects of the associated platform across the disciplines of business, applications, data and infrastructure. You’ll also be liaising with principal engineers, architects in the domain and other key stakeholders to understand how the platform works and how it supports business objectives. You’ll Also Be • Applying Agile methods to the development of software on the backlog • Producing resilient and long-lived software and acting flexibly to cope with future needs • Delivering intentional architecture and formulating emergent design through innovative ideas, experimentation and prototyping • Designing and developing software with a focus on the automation of build, test and deployment activities, using executable patterns The skills you'll need We’re looking for someone with strong full stack experience in software design and implementation, including being able to exploit programming languages to solve complex problems. You’ll also need at least twelve years of experience with Python or PySpark with AWS and Airflow. You'll need experience in writing advance Spark SQL or ANSI SQL queries with experience Spark SQL query optimistaion and performance tuning. You'll also need experience with continuous integration, DevOps, Gitlab and Artifactory. Additionally, You’ll Demonstrate • Experience of leading the implementation of programming best practice, especially around scalability, automation, virtualisation, optimisation, availability and performance • Sound collaboration skills with the ability to work with business teams to produce pragmatic solutions that work for the business • Experience of information security policies and practices within the financial sector • Strong stakeholder management skills and communication skills with the ability to communicate complex technical concepts in a simple way
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
- 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) |
Aliases — catalog
- Python (CANONICAL) primary
- Python 2 (VERSION)
- Python 2.x (VERSION)
- Python 3 (VERSION)
- Python 3.10 (VERSION)
- Python 3.11 (VERSION)
- Python 3.12 (VERSION)
- Python 3.x (VERSION)
- py (VERSION)
- py2 (VERSION)
- py3 (VERSION)
- python 3 (VERSION)
- python 3.x (VERSION)
- python2 (VERSION)
- python3 (VERSION)
- python3.x (VERSION)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Language
- Sub-category
- Programming Language
- Vendor
- PSF
- License
- mit
- Year introduced
- 1991
- Confidence
- 0.99
- Version strategy
- SEPARATE_ENTITY
- Version tag
- 3
Maturity reasoning: Python appears in a very high volume of job descriptions across data, backend, automation, and ML roles, and remains a default hiring-pipeline language on major job boards and tech stacks.
Skill profile (library / DB)
- Skill nature
- LANGUAGE
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 6
- Sub-category id
- 96
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
Cloud Security Scripting & DSL Languages Catalog dimension db id 248
Library dimension (catalog)
Roles linked in library: Cloud Security Engineer
-
Programming Languages Catalog dimension db id 1
Library dimension (catalog)
Roles linked in library: Backend Developer, Fullstack Developer, Fullstack Developer
-
Programming Languages and Scripting Catalog dimension db id 59
Library dimension (catalog)
Roles linked in library: Cyber Security Engineer
-
Programming Languages for Data Work Catalog dimension db id 21
Library dimension (catalog)
Roles linked in library: Data Engineer
-
Programming Languages for ML Systems Catalog dimension db id 39
Library dimension (catalog)
Roles linked in library: ML Engineer, MLOps Engineer
-
Programming Languages for XR Catalog dimension db id 97
Library dimension (catalog)
Roles linked in library: AR/VR Engineer
-
Python Programming Catalog dimension db id 290
Library dimension (catalog)
Roles linked in library: Python Backend Developer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Cloud Security Scripting & DSL Languages
cloud-security-scripting-dsl-languages
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Programming Languages
programming-languages
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved |
|
Programming Languages and Scripting
programming-languages-and-scripting
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Programming Languages for Data Work
programming-languages-for-data-work
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Programming Languages for ML Systems
programming-languages-for-ml-systems
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Programming Languages for XR
programming-languages-for-xr
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Python Programming
python-programming
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
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
- 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) |
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
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Programming Languages
- Sub-category
- general
- Skill nature
- LANGUAGE
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Version strategy
- UNVERSIONED
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 skipped (dimension not under chosen role) |
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- DevOps Tools
- Sub-category
- general
- Skill nature
- PRACTICE
- 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
- GitLab (CANONICAL) primary
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Platform
- Sub-category
- Devops Platform
- Vendor
- GitLab Inc.
- License
- mit
- Year introduced
- 2011
- Confidence
- 0.96
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: GitLab appears in many DevOps/CI-CD job descriptions and is widely used as an integrated source control and pipeline platform; its GitLab CI/CD and self-managed/SaaS offerings are common hiring signals.
Skill profile (library / DB)
- Skill nature
- PLATFORM
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 9
- Sub-category id
- 170
- 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
- DevOps Tools
- Sub-category
- general
- Skill nature
- TOOL
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Aliases — catalog
- virtualization (CANONICAL) primary
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Concept
- Sub-category
- Ui Rendering Concept
- Confidence
- 0.72
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Virtualization is a common requirement in frontend JDs for large lists/tables, and major libraries like React Window and AG Grid document it as a standard performance pattern.
Skill profile (library / DB)
- Skill nature
- CONCEPT
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 2
- Sub-category id
- 1597
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
Performance and Rendering Optimization Catalog dimension db id 124
Library dimension (catalog)
Roles linked in library: Angular Frontend Developer, Flutter Developer, Frontend Developer, Ionic Developer, React Frontend Developer, React Native Developer, Web Developer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Performance and Rendering Optimization
performance-and-rendering-optimization
|
✓ | — | 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
- DevOps Tools
- Sub-category
- general
- Skill nature
- PRACTICE
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Aliases — catalog
- query tuning (CANONICAL) primary
- Query Tuning (CANONICAL)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Concept
- Sub-category
- Query Optimization
- Confidence
- 0.82
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Common in DB/analytics job descriptions and vendor docs; PostgreSQL, MySQL, SQL Server, and Oracle all expose EXPLAIN/ANALYZE and tuning guides, showing broad hiring-pipeline demand.
Skill profile (library / DB)
- Skill nature
- CONCEPT
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 2
- Sub-category id
- 3067
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
Relational Database Design Catalog dimension db id 4
Library dimension (catalog)
Roles linked in library: .NET Backend Developer, Backend Developer, Kotlin Backend Developer, Node.js Backend Developer, Python Backend Developer, Ruby Backend Developer, Scala Backend Developer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Relational Database Design
relational-database-design
|
— | — |
Skipped — no persistable v3 meta for new skill
skill_not_in_db_v3_proposed
|
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
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
- Security Tools
- Sub-category
- general
- Skill nature
- CONCEPT
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
All API 3 persistence rows
Same grid as the skill-extractor “Persistence items” table: one row per (skill × dimension) work item.
| Skill | Tag | Dimension | Skill↔dim | Role↔dim | Outcome | Notes |
|---|---|---|---|---|---|---|
| Agile | in_db |
React Frontend Development
d_init_01
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Python | in_db |
Cloud Security Scripting & DSL Languages
cloud-security-scripting-dsl-languages
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Python | in_db |
Programming Languages
programming-languages
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved | |
| Python | in_db |
Programming Languages and Scripting
programming-languages-and-scripting
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Python | in_db |
Programming Languages for Data Work
programming-languages-for-data-work
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Python | in_db |
Programming Languages for ML Systems
programming-languages-for-ml-systems
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Python | in_db |
Programming Languages for XR
programming-languages-for-xr
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Python | in_db |
Python Programming
python-programming
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| PySpark | new |
ETL and ELT Tooling
etl-and-elt-tooling
|
— | — | Skipped — no persistable v3 meta for new skill | skill_not_in_db_v3_proposed |
| 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) | |
| 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 skipped (dimension not under chosen role) | |
| 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) | |
| GitLab | in_db |
CI/CD Pipeline Platforms
ci-cd-pipeline-platforms
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| GitLab | in_db |
CI/CD for Machine Learning
ci-cd-for-machine-learning
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Virtualization | in_db |
Performance and Rendering Optimization
performance-and-rendering-optimization
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Performance Tuning | new |
Relational Database Design
relational-database-design
|
— | — | Skipped — no persistable v3 meta for new skill | skill_not_in_db_v3_proposed |
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 | ANSI SQL | type=Programming Languages subtype=general nature=LANGUAGE lifespan=EVERGREEN | |
| canonical_skill_proposed | Continuous Integration | type=DevOps Tools subtype=general nature=PRACTICE lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Artifactory | type=DevOps Tools subtype=general nature=TOOL lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Automation | type=DevOps Tools subtype=general nature=PRACTICE lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Optimization | type=Concepts subtype=general nature=CONCEPT lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Scalability | type=Concepts subtype=general nature=CONCEPT lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Availability | type=Concepts subtype=general nature=CONCEPT lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Information Security | type=Security Tools subtype=general nature=CONCEPT lifespan=MULTI_YEAR | |
| dimension_skill_link_proposed | PySpark ↔ ETL and ELT Tooling | |
| dimension_skill_link_proposed | Performance Tuning ↔ Relational Database Design | |
| role_dimension_link_proposed | Backend Developer ↔ Relational Database Design |
nano JD Parser — gpt-4.1-nano click to toggle
Show raw JSON
{
"JD_type": "pass",
"about_company": null,
"certifications": [],
"company_name": null,
"ctc": null,
"domain": {
"primary": {
"aliases": [],
"domain": "Other"
},
"secondary": null
},
"education": [],
"experience": {
"max": null,
"min": 12,
"raw": "at least twelve years of experience"
},
"job_locations": [],
"role": "Software Engineer, PySpark",
"role_aliases": [
"Software Engineer",
"Full Stack Engineer",
"PySpark Developer"
],
"role_archetype": "Engineering",
"roles_and_responsibilities": [
{
"bullet_count": 0,
"heading": "What you\u0027ll do",
"heading_was_present": true,
"source_marker": {
"first_5_words": "Working in a permanent feature",
"last_5_words": "supports business objectives."
},
"text": "Working in a permanent feature team, you\u2019ll be developing knowledge of aspects of the associated platform across the disciplines of business, applications, data and infrastructure. You\u2019ll also be liaising with principal engineers, architects in the domain and other key stakeholders to understand how the platform works and how it supports business objectives.",
"word_count": 47
},
{
"bullet_count": 4,
"heading": "You\u2019ll Also Be",
"heading_was_present": true,
"source_marker": {
"first_5_words": "\u2022 Applying Agile methods to",
"last_5_words": "using executable patterns"
},
"text": "\u2022 Applying Agile methods to the development of software on the backlog\n\u2022 Producing resilient and long-lived software and acting flexibly to cope with future needs\n\u2022 Delivering intentional architecture and formulating emergent design through innovative ideas, experimentation and prototyping\n\u2022 Designing and developing software with a focus on the automation of build, test and deployment activities, using executable patterns",
"word_count": 56
},
{
"bullet_count": 7,
"heading": "The skills you\u0027ll need",
"heading_was_present": true,
"source_marker": {
"first_5_words": "We\u2019re looking for someone with",
"last_5_words": "in a simple way"
},
"text": "We\u2019re looking for someone with strong full stack experience in software design and implementation, including being able to exploit programming languages to solve complex problems. You\u2019ll also need at least twelve years of experience with Python or PySpark with AWS and Airflow.\n\nYou\u0027ll need experience in writing advance Spark SQL or ANSI SQL queries with experience Spark SQL query optimistaion and performance tuning. You\u0027ll also need experience with continuous integration, DevOps, Gitlab and Artifactory.\n\nAdditionally, You\u2019ll Demonstrate\n\n\u2022 Experience of leading the implementation of programming best practice, especially around scalability, automation, virtualisation, optimisation, availability and performance\n\u2022 Sound collaboration skills with the ability to work with business teams to produce pragmatic solutions that work for the business\n\u2022 Experience of information security policies and practices within the financial sector\n\u2022 Strong stakeholder management skills and communication skills with the ability to communicate complex technical concepts in a simple way",
"word_count": 218
}
],
"urls": []
}
API 1 — extract-from-jd click to toggle
{
"final_skills": [
{
"is_primary": true,
"skill_name": "Agile"
},
{
"is_primary": true,
"skill_name": "Python"
},
{
"is_primary": true,
"skill_name": "PySpark"
},
{
"is_primary": true,
"skill_name": "AWS"
},
{
"is_primary": true,
"skill_name": "Airflow"
},
{
"is_primary": true,
"skill_name": "Spark SQL"
},
{
"is_primary": true,
"skill_name": "ANSI SQL"
},
{
"is_primary": true,
"skill_name": "SQL"
},
{
"is_primary": true,
"skill_name": "Continuous Integration"
},
{
"is_primary": true,
"skill_name": "DevOps"
},
{
"is_primary": true,
"skill_name": "GitLab"
},
{
"is_primary": true,
"skill_name": "Artifactory"
},
{
"is_primary": false,
"skill_name": "Virtualization"
},
{
"is_primary": false,
"skill_name": "Automation"
},
{
"is_primary": false,
"skill_name": "Performance Tuning"
},
{
"is_primary": false,
"skill_name": "Optimization"
},
{
"is_primary": false,
"skill_name": "Scalability"
},
{
"is_primary": false,
"skill_name": "Availability"
},
{
"is_primary": false,
"skill_name": "Information Security"
}
],
"jd_role": {
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"role_aliases": [
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],
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},
"nano_parsed": {
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},
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],
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{
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},
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},
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},
{
"bullet_count": 7,
"heading": "The skills you\u0027ll need",
"heading_was_present": true,
"source_marker": {
"first_5_words": "We\u2019re looking for someone with",
"last_5_words": "in a simple way"
},
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}
],
"urls": []
},
"rejected": false,
"rejection_reason": null,
"run_id": "d8ac84be-457c-4db6-877f-e9e89edde05c",
"stage3_signals": {
"alias_found": true,
"alias_match_roles": [
{
"display_name": "Fullstack Developer",
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"matched_skills": null,
"role_id": 15,
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}
],
"kra_match_roles": [
{
"display_name": "DevOps Engineer",
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{
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"similarity": 0.5657
},
{
"kra_text": "Collaborates with development teams to improve build processes, reduce deployment friction, containerize applications, and adopt DevOps best practices.",
"sentence": "Experience of leading the implementation of programming best practice, especially around scalability, automation, virtualisation, optimisation, availability and performance",
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},
{
"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": "You\u0027ll also need experience with continuous integration, DevOps, Gitlab and Artifactory.",
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}
],
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"role_id": 10,
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},
{
"display_name": "Fullstack Developer",
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{
"kra_text": "Delivers features through CI/CD pipelines using automated tests, staged rollouts, feature flags, and incremental deployments.",
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"similarity": 0.5434
},
{
"kra_text": "Works closely with product managers and UX designers to translate requirements and wireframes into working software features through iterative development.",
"sentence": "You\u2019ll also be liaising with principal engineers, architects in the domain and other key stakeholders to understand how the platform works and how it supports business objectives.",
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{
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}
],
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},
{
"display_name": "Cloud Architect",
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{
"kra_text": "Conducts architecture reviews, approves technical design documents, and guides engineering teams through cloud migration and modernization projects.",
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},
{
"kra_text": "Conducts architecture reviews, approves technical design documents, and guides engineering teams through cloud migration and modernization projects.",
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"similarity": 0.4759
},
{
"kra_text": "Architects blue-green, canary, and immutable infrastructure deployment patterns for zero-downtime releases and fast rollback capabilities.",
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}
],
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},
{
"display_name": "Data Engineer",
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{
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},
{
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"similarity": 0.4763
},
{
"kra_text": "Optimizes pipeline throughput, partitioning strategies, and query performance across cloud data warehouses like Snowflake, BigQuery, or Redshift.",
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}
],
"matched_count": null,
"matched_skills": null,
"role_id": 2,
"score": 0.48,
"slug": "data-engineer",
"total_count": null
},
{
"display_name": "Flutter Developer",
"kra_matches": [
{
"kra_text": "collaborate with design, product, and backend teams",
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},
{
"kra_text": "collaborate with design, product, and backend teams",
"sentence": "You\u2019ll also be liaising with principal engineers, architects in the domain and other key stakeholders to understand how the platform works and how it supports business objectives.",
"similarity": 0.4722
},
{
"kra_text": "optimize responsiveness and performance",
"sentence": "Producing resilient and long-lived software and acting flexibly to cope with future needs",
"similarity": 0.4611
}
],
"matched_count": null,
"matched_skills": null,
"role_id": 74,
"score": 0.4733,
"slug": "flutter-developer",
"total_count": null
}
],
"skill_match_roles": [
{
"display_name": "ML Engineer",
"kra_matches": null,
"matched_count": 4,
"matched_skills": [
"AWS",
"Airflow",
"GitLab",
"Python"
],
"role_id": 3,
"score": 0.3333,
"slug": "ml-engineer",
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},
{
"display_name": "MLOps Engineer",
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"matched_count": 3,
"matched_skills": [
"AWS",
"Airflow",
"Python"
],
"role_id": 16,
"score": 0.25,
"slug": "ml-ops-engineer",
"total_count": 12
},
{
"display_name": "Data Engineer",
"kra_matches": null,
"matched_count": 3,
"matched_skills": [
"AWS",
"Python",
"SQL"
],
"role_id": 2,
"score": 0.25,
"slug": "data-engineer",
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},
{
"display_name": "DevOps Engineer",
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"matched_skills": [
"AWS",
"DevOps",
"GitLab"
],
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"slug": "devops-engineer",
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},
{
"display_name": "Cyber Security Engineer",
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"matched_count": 2,
"matched_skills": [
"AWS",
"Python"
],
"role_id": 5,
"score": 0.1667,
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}
]
},
"stage4_decision": {
"alias_collision_detected": false,
"case": "DOMAIN",
"chosen_role": {
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"kra_matches": null,
"matched_count": null,
"matched_skills": null,
"role_id": 1,
"score": 0.91,
"slug": "backend-engineer",
"total_count": null
},
"confidence": 0.91,
"is_new_role": false,
"llm2_fired": false,
"llm2_reasoning": null,
"matched_dimensions": [
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"Data Engineering and Spark Development",
"CI/CD and DevOps Automation",
"Performance Tuning and Query Optimization",
"Architecture and Design",
"Stakeholder Collaboration",
"Security Awareness in Financial Services"
],
"matched_kras": [
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"Producing resilient and long-lived software",
"Delivering intentional architecture and emergent design",
"Designing and developing software with automation of build, test and deployment",
"Leverage Python or PySpark with AWS and Airflow",
"Write and optimize Spark SQL or ANSI SQL queries",
"Implement continuous integration, DevOps, Gitlab and Artifactory",
"Lead implementation of programming best practice",
"Work with business teams to produce pragmatic solutions",
"Communicate complex technical concepts in a simple way"
],
"matched_skills": [
"Python",
"PySpark",
"AWS",
"Airflow",
"Spark SQL",
"ANSI SQL",
"continuous integration",
"DevOps",
"Gitlab",
"Artifactory",
"Agile",
"automation",
"build, test and deployment activities",
"information security policies"
],
"new_role_display_name": null,
"new_role_slug": null,
"queued": false,
"reasoning": "Domain=Software Engineering \u2192 sub-role python-backend-developer; The JD centers on Python/PySpark, Spark SQL, AWS, Airflow, CI/DevOps, and software design/implementation for a platform backend rather than frontend, mobile, or infrastructure-only work.",
"sub_role": {
"confidence": 0.98,
"display_name": "Python Backend Developer",
"reasoning": "The JD explicitly centers on Python/PySpark, Spark SQL, and AWS/Airflow, which strongly matches a Python backend role.",
"role_id": 80,
"slug": "python-backend-developer"
}
},
"stage5_updates": {
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"centroid_updated": true,
"collision_log_id": null,
"new_kra_attached": {
"best_kra_similarity": 0.0,
"queue_id": 430,
"r_and_r_preview": "Working in a permanent feature team, you\u2019ll be developing knowledge of aspects of the associated platform across the disciplines of business, applications, data and infrastructure. You\u2019ll also be liai",
"role_display_name": "Backend Developer",
"role_slug": "backend-engineer",
"status": "pending"
},
"new_skills_attached": [
{
"is_primary": true,
"queue_id": 7772,
"role_display_name": "Backend Developer",
"role_slug": "backend-engineer",
"skill_name": "PySpark",
"status": "pending"
},
{
"is_primary": true,
"queue_id": 7773,
"role_display_name": "Backend Developer",
"role_slug": "backend-engineer",
"skill_name": "Spark SQL",
"status": "pending"
},
{
"is_primary": true,
"queue_id": 7774,
"role_display_name": "Backend Developer",
"role_slug": "backend-engineer",
"skill_name": "ANSI SQL",
"status": "pending"
},
{
"is_primary": true,
"queue_id": 7775,
"role_display_name": "Backend Developer",
"role_slug": "backend-engineer",
"skill_name": "Continuous Integration",
"status": "pending"
},
{
"is_primary": true,
"queue_id": 7776,
"role_display_name": "Backend Developer",
"role_slug": "backend-engineer",
"skill_name": "Artifactory",
"status": "pending"
},
{
"is_primary": false,
"queue_id": 7777,
"role_display_name": "Backend Developer",
"role_slug": "backend-engineer",
"skill_name": "Automation",
"status": "pending"
},
{
"is_primary": false,
"queue_id": 7778,
"role_display_name": "Backend Developer",
"role_slug": "backend-engineer",
"skill_name": "Performance Tuning",
"status": "pending"
},
{
"is_primary": false,
"queue_id": 7779,
"role_display_name": "Backend Developer",
"role_slug": "backend-engineer",
"skill_name": "Optimization",
"status": "pending"
},
{
"is_primary": false,
"queue_id": 7780,
"role_display_name": "Backend Developer",
"role_slug": "backend-engineer",
"skill_name": "Scalability",
"status": "pending"
},
{
"is_primary": false,
"queue_id": 7781,
"role_display_name": "Backend Developer",
"role_slug": "backend-engineer",
"skill_name": "Availability",
"status": "pending"
},
{
"is_primary": false,
"queue_id": 7782,
"role_display_name": "Backend Developer",
"role_slug": "backend-engineer",
"skill_name": "Information Security",
"status": "pending"
}
],
"queue_entry_id": null,
"v3_pipeline_triggered": false,
"v3_role_slug": null,
"v3_run_id": null
}
}
API 2 — extract-details
{
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{
"alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
"alias_persisted": false,
"existing_alias_id": 868,
"existing_alias_text": "Agile",
"input_term": "Agile",
"matched_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"
},
"matched_via": "alias"
},
{
"alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
"alias_persisted": false,
"existing_alias_id": 67,
"existing_alias_text": "Python",
"input_term": "Python",
"matched_canonical": {
"category_id": 6,
"display_name": "Python",
"id": 5,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "LANGUAGE",
"slug": "python",
"sub_category_id": 96,
"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"matched_via": "alias"
},
{
"alias_persist_skipped_reason": "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": 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": 526,
"existing_alias_text": "Airflow",
"input_term": "Airflow",
"matched_canonical": {
"category_id": 13,
"display_name": "Airflow",
"id": 265,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "TOOL",
"slug": "airflow",
"sub_category_id": 130,
"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": {
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"is_extractable": true,
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"matched_via": "alias"
},
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"alias_persisted": false,
"existing_alias_id": 1852,
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"matched_via": "alias"
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{
"alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
"alias_persisted": false,
"existing_alias_id": 540,
"existing_alias_text": "GitLab",
"input_term": "GitLab",
"matched_canonical": {
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"is_also_category": false,
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"matched_via": "alias"
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{
"alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
"alias_persisted": false,
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"matched_canonical": {
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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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},
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},
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},
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},
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]
},
{
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]
},
{
"dimension": {
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{
"display_name": "Cyber Security Engineer",
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]
},
{
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"rationale": "Workflow engines used to coordinate training, evaluation, deployment, and retraining jobs. This cluster covers dependencies, retries, scheduling, and pipeline composition for ML lifecycle automation.",
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},
"input_skill": "Airflow",
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{
"display_name": "ML Engineer",
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},
{
"display_name": "MLOps Engineer",
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}
]
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Pega Programming Languages \u0026 DSLs",
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"rationale": "Programming languages and domain-specific languages used in Pega development.",
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},
"input_skill": "SQL",
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"roles_from_db": [
{
"display_name": "Pega Developer",
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}
]
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Programming Languages for Data Work",
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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",
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},
"input_skill": "SQL",
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"roles_from_db": [
{
"display_name": "Data Engineer",
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]
},
{
"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.",
"slug": "ci-cd-pipeline-platforms",
"source": "db"
},
"input_skill": "DevOps",
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"roles_from_db": [
{
"display_name": "DevOps Engineer",
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}
]
},
{
"dimension": {
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"display_name": "Deployment and Release Patterns",
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"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",
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"roles_from_db": [
{
"display_name": "Cloud Architect",
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"version_strategy": "UNVERSIONED",
"volatility": "MEDIUM"
},
"enrichment": null,
"keep_log": [],
"locked_dimensions": [],
"merge_log": [],
"placed": null,
"relationships": null,
"skill_id": "information-security",
"split_log": [],
"typed": null,
"warnings": []
},
"source_tag": "llm",
"was_in_llm_skills": true
}
],
"unmatched_skills": [
"Spark SQL",
"ANSI SQL",
"Continuous Integration",
"Artifactory",
"Automation",
"Optimization",
"Scalability",
"Availability",
"Information Security"
]
}
API 3 — final-role-output
{
"chosen_role": {
"display_name": "Backend Developer",
"id": 1,
"rationale": "Domain=Software Engineering \u2192 sub-role python-backend-developer; The JD centers on Python/PySpark, Spark SQL, AWS, Airflow, CI/DevOps, and software design/implementation for a platform backend rather than frontend, mobile, or infrastructure-only work.",
"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"
},
"chosen_role_resolution": "in_db",
"final_input_skills": [
{
"skill": "Agile",
"tag": "in_db"
},
{
"skill": "Python",
"tag": "in_db"
},
{
"skill": "PySpark",
"tag": "in_db"
},
{
"skill": "AWS",
"tag": "in_db"
},
{
"skill": "Airflow",
"tag": "in_db"
},
{
"skill": "Spark SQL",
"tag": "new"
},
{
"skill": "ANSI SQL",
"tag": "new"
},
{
"skill": "SQL",
"tag": "in_db"
},
{
"skill": "Continuous Integration",
"tag": "new"
},
{
"skill": "DevOps",
"tag": "in_db"
},
{
"skill": "GitLab",
"tag": "in_db"
},
{
"skill": "Artifactory",
"tag": "new"
},
{
"skill": "Virtualization",
"tag": "in_db"
},
{
"skill": "Automation",
"tag": "new"
},
{
"skill": "Performance Tuning",
"tag": "in_db"
},
{
"skill": "Optimization",
"tag": "new"
},
{
"skill": "Scalability",
"tag": "new"
},
{
"skill": "Availability",
"tag": "new"
},
{
"skill": "Information Security",
"tag": "new"
}
],
"llm_cost_api1_usd": null,
"llm_cost_api2_usd": null,
"llm_cost_api3_usd": null,
"llm_cost_total_usd": null,
"persistence": {
"items": [
{
"chosen_role_id": 1,
"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": "Agile",
"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": 520,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 1,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Cloud Security Scripting \u0026 DSL Languages",
"id": 248,
"rationale": "Proficiency in programming and domain-specific languages used to automate and script cloud security controls.",
"slug": "cloud-security-scripting-dsl-languages",
"source": "db"
},
"dimension_id": 248,
"input_skill": "Python",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "Cloud Security Engineer",
"id": 23,
"rationale": null,
"role_archetype": null,
"slug": "cloud-security-engineer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 5,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 1,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Programming Languages",
"id": 1,
"rationale": "Primary implementation languages used to build client and server feature code. Full stack engineers need enough fluency to move across layers and implement product behavior end to end.",
"slug": "programming-languages",
"source": "db"
},
"dimension_id": 1,
"input_skill": "Python",
"llm_role": null,
"matched_chosen_role": true,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension saved",
"role_dimension_saved": true,
"roles_from_db": [
{
"display_name": "Backend Developer",
"id": 1,
"rationale": null,
"role_archetype": "A Backend Engineer designs, builds, and maintains the server-side logic and data handling that power applications and services. They focus on implementing reliable business functionality, integrating with other systems, and ensuring the backend is scalable, maintainable, and observable.",
"slug": "backend-engineer",
"source": "db"
},
{
"display_name": "Fullstack Developer",
"id": 435,
"rationale": null,
"role_archetype": "Engineering",
"slug": "fullstack-developer",
"source": "db"
},
{
"display_name": "Fullstack Developer",
"id": 15,
"rationale": null,
"role_archetype": null,
"slug": "full-stack-engineer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 5,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 1,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Programming Languages and Scripting",
"id": 59,
"rationale": "Languages used to write security automation, analysis scripts, detection logic, and remediation helpers. This is the primary implementation surface for a cybersecurity engineer across tooling and response workflows.",
"slug": "programming-languages-and-scripting",
"source": "db"
},
"dimension_id": 59,
"input_skill": "Python",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "Cyber Security Engineer",
"id": 5,
"rationale": null,
"role_archetype": null,
"slug": "cybersecurity-engineer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 5,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 1,
"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": "Python",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "Data Engineer",
"id": 2,
"rationale": null,
"role_archetype": null,
"slug": "data-engineer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 5,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 1,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Programming Languages for ML Systems",
"id": 39,
"rationale": "Languages used to build training code, inference services, evaluation jobs, and ML glue code. This is the primary implementation surface for ML engineers across experimentation and productionization.",
"slug": "programming-languages-for-ml-systems",
"source": "db"
},
"dimension_id": 39,
"input_skill": "Python",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "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": 5,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 1,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Programming Languages for XR",
"id": 97,
"rationale": "Primary implementation languages used to build immersive client features, interaction logic, and device-specific runtime behavior. This is the core coding surface for AR/VR experiences.",
"slug": "programming-languages-for-xr",
"source": "db"
},
"dimension_id": 97,
"input_skill": "Python",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "AR/VR Engineer",
"id": 8,
"rationale": null,
"role_archetype": null,
"slug": "ar-vr-engineer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 5,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 1,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Python Programming",
"id": 290,
"rationale": "Core Python language skills used to implement backend business logic, request handlers, integrations, and service internals. This is the primary coding surface for the role.",
"slug": "python-programming",
"source": "db"
},
"dimension_id": 290,
"input_skill": "Python",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "Python Backend Developer",
"id": 80,
"rationale": null,
"role_archetype": "Engineering",
"slug": "python-backend-developer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 5,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 1,
"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": false,
"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": 1,
"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": 1,
"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": 1,
"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"
},
{
"display_name": "Cloud Security Engineer",
"id": 23,
"rationale": null,
"role_archetype": null,
"slug": "cloud-security-engineer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 187,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 1,
"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": "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 Security Engineer",
"id": 23,
"rationale": null,
"role_archetype": null,
"slug": "cloud-security-engineer",
"source": "db"
},
{
"display_name": "Cyber Security Engineer",
"id": 5,
"rationale": null,
"role_archetype": null,
"slug": "cybersecurity-engineer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 187,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 1,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Workflow Orchestration for ML Pipelines",
"id": 54,
"rationale": "Workflow engines used to coordinate training, evaluation, deployment, and retraining jobs. This cluster covers dependencies, retries, scheduling, and pipeline composition for ML lifecycle automation.",
"slug": "workflow-orchestration-for-ml-pipelines",
"source": "db"
},
"dimension_id": 54,
"input_skill": "Airflow",
"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": 265,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 1,
"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": 1,
"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": false,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"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": true,
"skill_id": 101,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 1,
"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"
},
"dimension_id": 150,
"input_skill": "DevOps",
"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": "DevOps Engineer",
"id": 10,
"rationale": null,
"role_archetype": null,
"slug": "devops-engineer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 1216,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 1,
"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"
},
"dimension_id": 140,
"input_skill": "DevOps",
"llm_role": null,
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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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"matched_chosen_role": false,
"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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],
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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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{
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{
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{
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{
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{
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{
"display_name": "Python Backend Developer",
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{
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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.