Pipeline run
bca6549b-b891-45a3-ab92-60e635eb365b
Client output enrichment
v2 Skill cluster · Nature of work · AI index · Tech stack maturity · Evidence · KRA descriptionvocab breakdown (legacy)
Signals
Post-classification
Captured for admin review
1 POST /skills/extract-from-jd
2 POST /skills/extract-details
3 POST /skills/final-role-output
Data Engineer
domain · Data Engineering & Analytics CASE DOMAINslug: data-engineer · id: 2 · source: db
Domain=Data Engineering & Analytics; The JD focuses on Python/PySpark-based data extraction, transformation, and pipeline creation, which most closely matches a Data Engineer role.
Matched skills
Matched dimensions
Matched KRAs
Resolution:
in_db
— role exists in library; skill↔dim and role↔dim links saved when applicable.
Job description
About Accenture: Accenture is a global professional services company with leading capabilities in digital, cloud and security. Combining unmatched experience and specialized skills across more than 40 industries, we offer Strategy and Consulting, Interactive, Technology and Operations services-all powered by the world's largest network of Advanced Technology and Intelligent Operations centers. Our 674,000 people deliver on the promise of technology and human ingenuity every day, serving clients in more than 120 countries.We embrace the power of change to create value and shared success for our clients, people, shareholders, partners and communities. Visit us at www.accenture.com Accenture | Let there be change We embrace change to create 360-degree value www.accenture.com Project Role :Application Developer Project Role Description :Design, build and configure applications to meet business process and application requirements. Management Level :10 Work Experience :4-6 years Work location :Mumbai Must Have Skills :Python Programming Language Good To Have Skills :Oracle Procedural Language Extensions to SQL Job Requirements : Key Responsibilities : A Data extraction, transformation using spark Pyspark, data pipelines creations from source to target and respectively should have experience using python, B should have good knowledge on Map, reduce functions in python Collections, dictionaries, sets, exceptional handling, should have extensive hands on C exposure Should have python pandas, ability to create data frames using pandas, ability to create data frames using pandas Technical Experience : A Expert in Python, with knowledge of at least one Python web framework Django, Flask, etc Able to integrate multiple data sources and databases into one system Understanding of the threading limitations of Python, and multi-process architecture Good understanding of serverside templating languages Jinja 2, Mako, etc Able to create database schemas that represent and support business processes B 3-4 yrs exp in Python Professional Attributes : Good analytical skill and Should have good Communication skills Should be a good team player Educational Qualification : Should have Bachelors in Engineering, preferably in computer science or IT discipline 15 years of full time education
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
- 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
|
✓ | ✓ | 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
- Python (CANONICAL) primary
- Python 2 (VERSION)
- Python 2.x (VERSION)
- Python 3 (VERSION)
- Python 3.10 (VERSION)
- Python 3.11 (VERSION)
- Python 3.12 (VERSION)
- Python 3.x (VERSION)
- py (VERSION)
- py2 (VERSION)
- py3 (VERSION)
- python 3 (VERSION)
- python 3.x (VERSION)
- python2 (VERSION)
- python3 (VERSION)
- python3.x (VERSION)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Language
- Sub-category
- Programming Language
- Vendor
- PSF
- License
- mit
- Year introduced
- 1991
- Confidence
- 0.99
- Version strategy
- SEPARATE_ENTITY
- Version tag
- 3
Maturity reasoning: Python appears in a very high volume of job descriptions across data, backend, automation, and ML roles, and remains a default hiring-pipeline language on major job boards and tech stacks.
Skill profile (library / DB)
- Skill nature
- LANGUAGE
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 6
- Sub-category id
- 96
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
Cloud Security Scripting & DSL Languages Catalog dimension db id 248
Library dimension (catalog)
Roles linked in library: Cloud Security Engineer
-
Programming Languages Catalog dimension db id 1
Library dimension (catalog)
Roles linked in library: Backend Developer, Fullstack Developer, Fullstack Developer
-
Programming Languages & DSLs Catalog dimension db id 475
Library dimension (catalog)
Roles linked in library: Engineering Manager
-
Programming Languages and Scripting Catalog dimension db id 59
Library dimension (catalog)
Roles linked in library: Cyber Security Engineer
-
Programming Languages for Data Work Catalog dimension db id 21
Library dimension (catalog)
Roles linked in library: Data Engineer
-
Programming Languages for ML Systems Catalog dimension db id 39
Library dimension (catalog)
Roles linked in library: ML Engineer, MLOps Engineer
-
Programming Languages for XR Catalog dimension db id 97
Library dimension (catalog)
Roles linked in library: AR/VR Engineer
-
Python Programming Catalog dimension db id 290
Library dimension (catalog)
Roles linked in library: Python Backend Developer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Cloud Security Scripting & DSL Languages
cloud-security-scripting-dsl-languages
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Programming Languages
programming-languages
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Programming Languages & DSLs
programming-languages-dsls
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Programming Languages and Scripting
programming-languages-and-scripting
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Programming Languages for Data Work
programming-languages-for-data-work
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved |
|
Programming Languages for ML Systems
programming-languages-for-ml-systems
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Programming Languages for XR
programming-languages-for-xr
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Python Programming
python-programming
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Data Engineering Tools
- Sub-category
- general
- Skill nature
- CONCEPT
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Version strategy
- UNVERSIONED
Aliases — catalog
- C# (CANONICAL) primary
- C (CANONICAL)
- C# 1 (VERSION)
- C# 10 (VERSION)
- C# 11 (VERSION)
- C# 12 (VERSION)
- C# 13 (VERSION)
- C# 14 (VERSION)
- C# 2 (VERSION)
- C# 3 (VERSION)
- C# 4 (VERSION)
- C# 5 (VERSION)
- C# 6 (VERSION)
- C# 7 (VERSION)
- C# 8 (VERSION)
- C# 9 (VERSION)
- C# latest (VERSION)
- C#1 (VERSION)
- C#10 (VERSION)
- C#11 (VERSION)
- C#12 (VERSION)
- C#2 (VERSION)
- C#3 (VERSION)
- C#4 (VERSION)
- C#5 (VERSION)
- C#6 (VERSION)
- C#7 (VERSION)
- C#8 (VERSION)
- C#9 (VERSION)
- C++ (CANONICAL)
- C++03 (VERSION)
- C++11 (VERSION)
- C++14 (VERSION)
- C++17 (VERSION)
- C++20 (VERSION)
- C++23 (VERSION)
- C++26 (VERSION)
- C++98 (VERSION)
- c sharp (VERSION)
- c# (VERSION)
- cpp03 (VERSION)
- cpp11 (VERSION)
- cpp14 (VERSION)
- cpp17 (VERSION)
- cpp20 (VERSION)
- cpp23 (VERSION)
- cpp26 (VERSION)
- cpp98 (VERSION)
- csharp (VERSION)
- modern C++ (VERSION)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Language
- Sub-category
- Programming Language
- Vendor
- Microsoft
- License
- mit
- Year introduced
- 2000
- Confidence
- 0.99
- Version strategy
- SEPARATE_ENTITY
- Version tag
- latest
Maturity reasoning: C# is a mainstream hiring staple with high JD volume across .NET, Azure, and enterprise roles; Microsoft continues active platform investment in .NET, reinforcing broad adoption.
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)
-
C# and .NET Languages Catalog dimension db id 362
Library dimension (catalog)
Roles linked in library: .NET Backend Developer
-
Cross-Platform App Languages Catalog dimension db id 167
Library dimension (catalog)
Roles linked in library: Hybrid Mobile Developer
-
Programming Languages Catalog dimension db id 1
Library dimension (catalog)
Roles linked in library: Backend Developer, Fullstack Developer, Fullstack Developer
-
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
-
React Frontend Development Catalog dimension db id 96
Library dimension (catalog)
-
Sitecore Development Languages Catalog dimension db id 438
Library dimension (catalog)
Roles linked in library: Sitecore Dev
-
Video Codec Languages and DSLs Catalog dimension db id 225
Library dimension (catalog)
Roles linked in library: Video Codec Engineer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
C# and .NET Languages
c-and-net-languages
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Cross-Platform App Languages
cross-platform-app-languages
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Programming Languages
programming-languages
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Programming Languages 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) |
|
React Frontend Development
d_init_01
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Sitecore Development Languages
sitecore-development-languages
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Video Codec Languages and DSLs
video-codec-languages-and-dsls
|
✓ | — | 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
- Data Analysis Libraries
- 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
- Data Engineering Tools
- Sub-category
- general
- Skill nature
- CONCEPT
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Version strategy
- UNVERSIONED
Aliases — catalog
- Django (CANONICAL) primary
- Django 1 (VERSION)
- Django 1.x (VERSION)
- Django 2 (VERSION)
- Django 2.x (VERSION)
- Django 3 (VERSION)
- Django 3.x (VERSION)
- Django 4 (VERSION)
- Django 4.x (VERSION)
- Django 5 (VERSION)
- Django 5.x (VERSION)
- Django1 (VERSION)
- Django2 (VERSION)
- Django3 (VERSION)
- Django4 (VERSION)
- Django5 (VERSION)
- django 2 (VERSION)
- django 2.x (VERSION)
- django 3 (VERSION)
- django 3.x (VERSION)
- django 4 (VERSION)
- django 4.x (VERSION)
- django 5 (VERSION)
- django 5.0 (VERSION)
- django 5.x (VERSION)
- django2 (VERSION)
- django2.x (VERSION)
- django3 (VERSION)
- django3.x (VERSION)
- django4 (VERSION)
- django4.x (VERSION)
- django5 (VERSION)
- django5.0 (VERSION)
- django5.x (VERSION)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Framework
- Sub-category
- Web Framework
- Vendor
- Django Software Foundation
- License
- bsd
- Year introduced
- 2005
- Confidence
- 0.99
- Version strategy
- SEPARATE_ENTITY
- Version tag
- 5
Maturity reasoning: Django appears in many backend web job descriptions and remains a standard Python web framework; its GitHub ecosystem and long-term LTS releases show sustained market demand.
Skill profile (library / DB)
- Skill nature
- FRAMEWORK
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 5
- Sub-category id
- 35
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
Frameworks & Libraries Catalog dimension db id 360
Library dimension (catalog)
Roles linked in library: Drupal Dev, Engineering Manager
-
Web Application Frameworks Catalog dimension db id 2
Library dimension (catalog)
Roles linked in library: Backend Developer, Fullstack Developer, Fullstack Developer, Java Backend Developer, Node.js Backend Developer, PHP Backend Developer, Python Backend Developer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Frameworks & Libraries
frameworks-libraries
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Web Application Frameworks
web-application-frameworks
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Aliases — catalog
- Flask (CANONICAL) primary
- flask 2 (VERSION)
- flask 2.x (VERSION)
- flask 3 (VERSION)
- flask 3.x (VERSION)
- flask2 (VERSION)
- flask3 (VERSION)
- flask>=3 (VERSION)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Framework
- Sub-category
- Web Framework
- Vendor
- Pallets Projects
- License
- bsd
- Year introduced
- 2010
- Confidence
- 0.99
- Version strategy
- SEPARATE_ENTITY
- Version tag
- 3.x
Maturity reasoning: Flask appears in many Python web developer job postings and remains a common lightweight framework in hiring pipelines, though often alongside Django/FastAPI rather than as a niche tool.
Skill profile (library / DB)
- Skill nature
- FRAMEWORK
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 5
- Sub-category id
- 35
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
React Frontend Development Catalog dimension db id 96
Library dimension (catalog)
-
Web Application Frameworks Catalog dimension db id 2
Library dimension (catalog)
Roles linked in library: Backend Developer, Fullstack Developer, Fullstack Developer, Java Backend Developer, Node.js Backend Developer, PHP Backend Developer, Python Backend Developer
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) |
|
Web Application Frameworks
web-application-frameworks
|
✓ | — | 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
- Web Frameworks
- Sub-category
- Template Engines
- 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
- Web Frameworks
- Sub-category
- Template Engines
- Skill nature
- TOOL
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
All API 3 persistence rows
Same grid as the skill-extractor “Persistence items” table: one row per (skill × dimension) work item.
| Skill | Tag | Dimension | Skill↔dim | Role↔dim | Outcome | Notes |
|---|---|---|---|---|---|---|
| Spark | in_db |
ETL and ELT Tooling
etl-and-elt-tooling
|
✓ | ✓ | 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 |
| Python | in_db |
Cloud Security Scripting & DSL Languages
cloud-security-scripting-dsl-languages
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Python | in_db |
Programming Languages
programming-languages
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Python | in_db |
Programming Languages & DSLs
programming-languages-dsls
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Python | in_db |
Programming Languages and Scripting
programming-languages-and-scripting
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Python | in_db |
Programming Languages for Data Work
programming-languages-for-data-work
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved | |
| Python | in_db |
Programming Languages for ML Systems
programming-languages-for-ml-systems
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Python | in_db |
Programming Languages for XR
programming-languages-for-xr
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Python | in_db |
Python Programming
python-programming
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| C | in_db |
C# and .NET Languages
c-and-net-languages
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| C | in_db |
Cross-Platform App Languages
cross-platform-app-languages
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| C | in_db |
Programming Languages
programming-languages
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| C | in_db |
Programming Languages for ML Systems
programming-languages-for-ml-systems
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| C | in_db |
Programming Languages for XR
programming-languages-for-xr
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| C | in_db |
React Frontend Development
d_init_01
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| C | in_db |
Sitecore Development Languages
sitecore-development-languages
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| C | in_db |
Video Codec Languages and DSLs
video-codec-languages-and-dsls
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Django | in_db |
Frameworks & Libraries
frameworks-libraries
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Django | in_db |
Web Application Frameworks
web-application-frameworks
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Flask | in_db |
React Frontend Development
d_init_01
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Flask | in_db |
Web Application Frameworks
web-application-frameworks
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Library artifacts (this run)
| Kind | Detail | DB id |
|---|---|---|
| canonical_skill_proposed | MapReduce | type=Data Engineering Tools subtype=general nature=CONCEPT lifespan=EVERGREEN | |
| canonical_skill_proposed | Pandas | type=Data Engineering Tools subtype=Data Analysis Libraries nature=TOOL lifespan=MULTI_YEAR | |
| canonical_skill_proposed | DataFrame | type=Data Engineering Tools subtype=general nature=CONCEPT lifespan=EVERGREEN | |
| canonical_skill_proposed | Jinja2 | type=Web Frameworks subtype=Template Engines nature=TOOL lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Mako | type=Web Frameworks subtype=Template Engines nature=TOOL lifespan=MULTI_YEAR | |
| dimension_skill_link_proposed | PySpark ↔ ETL and ELT Tooling | |
| role_dimension_link_proposed | Data Engineer ↔ ETL and ELT Tooling |
nano JD Parser — gpt-4.1-nano click to toggle
Show raw JSON
{
"JD_type": "pass",
"about_company": {
"source_marker": {
"first_5_words": "About Accenture: Accenture is a",
"last_5_words": "people, shareholders, partners and communities."
},
"text": "About Accenture: Accenture is a global professional services company with leading capabilities in digital, cloud and security. Combining unmatched experience and specialized skills across more than 40 industries, we offer Strategy and Consulting, Interactive, Technology and Operations services-all powered by the world\u0027s largest network of Advanced Technology and Intelligent Operations centers. Our 674,000 people deliver on the promise of technology and human ingenuity every day, serving clients in more than 120 countries.We embrace the power of change to create value and shared success for our clients, people, shareholders, partners and communities.",
"word_count": 84
},
"certifications": [],
"company_name": "Accenture",
"ctc": null,
"domain": {
"primary": {
"aliases": [
"ITES",
"BPO",
"Tech Consulting"
],
"domain": "IT Services \u0026 Consulting"
},
"secondary": null
},
"education": [
{
"level": "Bachelor\u0027s",
"qualification": "BTECH/BE - Computer Science / IT",
"raw": "Should have Bachelors in Engineering, preferably in computer science or IT discipline",
"requirement": "required"
}
],
"experience": {
"max": 6,
"min": 4,
"raw": "4-6 years"
},
"job_locations": [
{
"aliases": [
"Bombay"
],
"city": "Mumbai",
"country": "India",
"state": null,
"work_mode": null
}
],
"role": "Application Developer",
"role_aliases": [
"App Developer",
"Software Developer",
"Application Engineer"
],
"role_archetype": "Engineering",
"roles_and_responsibilities": [
{
"bullet_count": 0,
"heading": "Key Responsibilities",
"heading_was_present": true,
"source_marker": {
"first_5_words": "A Data extraction, transformation using",
"last_5_words": "good team player"
},
"text": "A Data extraction, transformation using spark Pyspark, data pipelines creations from source to target and respectively should have experience using python, B should have good knowledge on Map, reduce functions in python Collections, dictionaries, sets, exceptional handling, should have extensive hands on C exposure Should have python pandas, ability to create data frames using pandas, ability to create data frames using pandas\n\nTechnical Experience : A Expert in Python, with knowledge of at least one Python web framework Django, Flask, etc Able to integrate multiple data sources and databases into one system Understanding of the threading limitations of Python, and multi-process architecture Good understanding of serverside templating languages Jinja 2, Mako, etc Able to create database schemas that represent and support business processes B 3-4 yrs exp in Python\n\nProfessional Attributes : Good analytical skill and Should have good Communication skills Should be a good team player",
"word_count": 236
}
],
"urls": [
{
"type": "website",
"url": "www.accenture.com"
}
]
}
API 1 — extract-from-jd click to toggle
{
"final_skills": [
{
"is_primary": true,
"skill_name": "Spark"
},
{
"is_primary": true,
"skill_name": "PySpark"
},
{
"is_primary": true,
"skill_name": "Python"
},
{
"is_primary": false,
"skill_name": "MapReduce"
},
{
"is_primary": false,
"skill_name": "C"
},
{
"is_primary": true,
"skill_name": "Pandas"
},
{
"is_primary": false,
"skill_name": "DataFrame"
},
{
"is_primary": false,
"skill_name": "Django"
},
{
"is_primary": false,
"skill_name": "Flask"
},
{
"is_primary": false,
"skill_name": "Jinja2"
},
{
"is_primary": false,
"skill_name": "Mako"
}
],
"jd_role": {
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"role_aliases": [
"App Developer",
"Software Developer",
"Application Engineer"
],
"role_archetype": "Engineering",
"slug": ""
},
"nano_parsed": {
"JD_type": "pass",
"about_company": {
"source_marker": {
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"last_5_words": "people, shareholders, partners and communities."
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},
"certifications": [],
"company_name": "Accenture",
"ctc": null,
"domain": {
"primary": {
"aliases": [
"ITES",
"BPO",
"Tech Consulting"
],
"domain": "IT Services \u0026 Consulting"
},
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},
"education": [
{
"level": "Bachelor\u0027s",
"qualification": "BTECH/BE - Computer Science / IT",
"raw": "Should have Bachelors in Engineering, preferably in computer science or IT discipline",
"requirement": "required"
}
],
"experience": {
"max": 6,
"min": 4,
"raw": "4-6 years"
},
"job_locations": [
{
"aliases": [
"Bombay"
],
"city": "Mumbai",
"country": "India",
"state": null,
"work_mode": null
}
],
"role": "Application Developer",
"role_aliases": [
"App Developer",
"Software Developer",
"Application Engineer"
],
"role_archetype": "Engineering",
"roles_and_responsibilities": [
{
"bullet_count": 0,
"heading": "Key Responsibilities",
"heading_was_present": true,
"source_marker": {
"first_5_words": "A Data extraction, transformation using",
"last_5_words": "good team player"
},
"text": "A Data extraction, transformation using spark Pyspark, data pipelines creations from source to target and respectively should have experience using python, B should have good knowledge on Map, reduce functions in python Collections, dictionaries, sets, exceptional handling, should have extensive hands on C exposure Should have python pandas, ability to create data frames using pandas, ability to create data frames using pandas\n\nTechnical Experience : A Expert in Python, with knowledge of at least one Python web framework Django, Flask, etc Able to integrate multiple data sources and databases into one system Understanding of the threading limitations of Python, and multi-process architecture Good understanding of serverside templating languages Jinja 2, Mako, etc Able to create database schemas that represent and support business processes B 3-4 yrs exp in Python\n\nProfessional Attributes : Good analytical skill and Should have good Communication skills Should be a good team player",
"word_count": 236
}
],
"urls": [
{
"type": "website",
"url": "www.accenture.com"
}
]
},
"rejected": false,
"rejection_reason": null,
"run_id": "bca6549b-b891-45a3-ab92-60e635eb365b",
"stage3_signals": {
"alias_found": true,
"alias_match_roles": [
{
"display_name": "Backend Developer",
"kra_matches": null,
"matched_count": null,
"matched_skills": null,
"role_id": 1,
"score": 1.0,
"slug": "backend-engineer",
"total_count": null
}
],
"kra_match_roles": [
{
"display_name": "Data Engineer",
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{
"kra_text": "Develops batch and real-time streaming data pipelines using Apache Spark, Apache Kafka, Apache Flink, or Airflow for data movement and processing at scale.",
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{
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{
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}
],
"matched_count": null,
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"role_id": 2,
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"slug": "data-engineer",
"total_count": null
},
{
"display_name": "Fullstack Developer",
"kra_matches": [
{
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},
{
"kra_text": "Designs and queries relational databases like PostgreSQL and document stores like MongoDB, writing migrations, indexes, and optimized queries.",
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{
"kra_text": "Works closely with product managers and UX designers to translate requirements and wireframes into working software features through iterative development.",
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}
],
"matched_count": null,
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"score": 0.3494,
"slug": "full-stack-engineer",
"total_count": null
},
{
"display_name": "ML Engineer",
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{
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"similarity": 0.4497
},
{
"kra_text": "Prepares, cleans, and transforms training datasets, manages feature stores, and builds feature engineering pipelines for model training.",
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"similarity": 0.3303
},
{
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}
],
"matched_count": null,
"matched_skills": null,
"role_id": 3,
"score": 0.34,
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"total_count": null
},
{
"display_name": "AI Engineer",
"kra_matches": [
{
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"similarity": 0.409
},
{
"kra_text": "Designs and implements prompt engineering workflows, few-shot examples, chain-of-thought patterns, and structured output parsing for AI feature pipelines.",
"sentence": "Technical Experience : A Expert in Python, with knowledge of at least one Python web framework Django, Flask, etc Able to integrate multiple data sources and databases into one system Understanding of the threading limitations of Python, and multi-process architecture Good understanding of serverside templating languages Jinja 2, Mako, etc Able to create database schemas that represent and support business processes B 3-4 yrs exp in Python",
"similarity": 0.348
},
{
"kra_text": "Documents AI feature capabilities, known limitations, failure modes, prompt versioning, and operational runbooks for engineering and product teams.",
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"similarity": 0.2619
}
],
"matched_count": null,
"matched_skills": null,
"role_id": 13,
"score": 0.3396,
"slug": "ai-engineer",
"total_count": null
},
{
"display_name": "Flutter Developer",
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{
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"sentence": "Technical Experience : A Expert in Python, with knowledge of at least one Python web framework Django, Flask, etc Able to integrate multiple data sources and databases into one system Understanding of the threading limitations of Python, and multi-process architecture Good understanding of serverside templating languages Jinja 2, Mako, etc Able to create database schemas that represent and support business processes B 3-4 yrs exp in Python",
"similarity": 0.3434
},
{
"kra_text": "integrate external APIs and data sources",
"sentence": "A Data extraction, transformation using spark Pyspark, data pipelines creations from source to target and respectively should have experience using python, B should have good knowledge on Map, reduce functions in python Collections, dictionaries, sets, exceptional handling, should have extensive hands on C exposure Should have python pandas, ability to create data frames using pandas, ability to create data frames using pandas",
"similarity": 0.318
},
{
"kra_text": "collaborate with design, product, and backend teams",
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"similarity": 0.3008
}
],
"matched_count": null,
"matched_skills": null,
"role_id": 74,
"score": 0.3207,
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}
],
"skill_match_roles": [
{
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"matched_count": 2,
"matched_skills": [
"Apache Spark",
"Python"
],
"role_id": 2,
"score": 0.5,
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},
{
"display_name": "ML Engineer",
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"matched_skills": [
"Python"
],
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},
{
"display_name": "Cyber Security Engineer",
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"matched_count": 1,
"matched_skills": [
"Python"
],
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"score": 0.25,
"slug": "cybersecurity-engineer",
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},
{
"display_name": "AR/VR Engineer",
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"matched_count": 1,
"matched_skills": [
"Python"
],
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},
{
"display_name": "Backend Developer",
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"matched_count": 1,
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"Python"
],
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}
]
},
"stage4_decision": {
"alias_collision_detected": false,
"case": "DOMAIN",
"chosen_role": {
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"matched_count": null,
"matched_skills": null,
"role_id": 2,
"score": 0.96,
"slug": "data-engineer",
"total_count": null
},
"confidence": 0.96,
"is_new_role": false,
"llm2_fired": false,
"llm2_reasoning": null,
"matched_dimensions": [
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"Data Transformation and ETL",
"Python Application Development",
"Web Framework Integration",
"Database Integration",
"Data Modeling"
],
"matched_kras": [
"Data extraction, transformation using spark Pyspark",
"data pipelines creations from source to target",
"integrate multiple data sources and databases into one system",
"create database schemas that represent and support business processes"
],
"matched_skills": [
"spark",
"PySpark",
"python",
"Map",
"reduce",
"Collections",
"dictionaries",
"sets",
"exceptional handling",
"C",
"pandas",
"data frames",
"Django",
"Flask",
"Jinja 2",
"Mako"
],
"new_role_display_name": null,
"new_role_slug": null,
"queued": false,
"reasoning": "Domain=Data Engineering \u0026 Analytics; The JD focuses on Python/PySpark-based data extraction, transformation, and pipeline creation, which most closely matches a Data Engineer role.",
"sub_role": null
},
"stage5_updates": {
"centroid_n_after": 356,
"centroid_updated": true,
"collision_log_id": null,
"new_kra_attached": null,
"new_skills_attached": [
{
"is_primary": true,
"queue_id": 16815,
"role_display_name": "Data Engineer",
"role_slug": "data-engineer",
"skill_name": "PySpark",
"status": "pending"
},
{
"is_primary": false,
"queue_id": 16816,
"role_display_name": "Data Engineer",
"role_slug": "data-engineer",
"skill_name": "MapReduce",
"status": "pending"
},
{
"is_primary": true,
"queue_id": 16817,
"role_display_name": "Data Engineer",
"role_slug": "data-engineer",
"skill_name": "Pandas",
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},
{
"is_primary": false,
"queue_id": 16818,
"role_display_name": "Data Engineer",
"role_slug": "data-engineer",
"skill_name": "DataFrame",
"status": "pending"
},
{
"is_primary": false,
"queue_id": 16819,
"role_display_name": "Data Engineer",
"role_slug": "data-engineer",
"skill_name": "Jinja2",
"status": "pending"
},
{
"is_primary": false,
"queue_id": 16820,
"role_display_name": "Data Engineer",
"role_slug": "data-engineer",
"skill_name": "Mako",
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}
],
"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": 2510,
"existing_alias_text": "spark",
"input_term": "Spark",
"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": "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,
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"id": 1350,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "FRAMEWORK",
"slug": "apache-spark",
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"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": 67,
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"matched_canonical": {
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"is_also_category": false,
"is_extractable": true,
"skill_nature": "LANGUAGE",
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"typical_lifespan": "EVERGREEN",
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},
"matched_via": "alias"
},
{
"alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
"alias_persisted": false,
"existing_alias_id": 1609,
"existing_alias_text": "C",
"input_term": "C",
"matched_canonical": {
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"display_name": "C#",
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"is_also_category": false,
"is_extractable": true,
"skill_nature": "LANGUAGE",
"slug": "c",
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"typical_lifespan": "EVERGREEN",
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},
"matched_via": "alias"
},
{
"alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
"alias_persisted": false,
"existing_alias_id": 82,
"existing_alias_text": "Django",
"input_term": "Django",
"matched_canonical": {
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"display_name": "Django",
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"is_extractable": true,
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},
"matched_via": "alias"
},
{
"alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
"alias_persisted": false,
"existing_alias_id": 1980,
"existing_alias_text": "Flask",
"input_term": "Flask",
"matched_canonical": {
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"display_name": "Flask",
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"is_also_category": false,
"is_extractable": true,
"skill_nature": "FRAMEWORK",
"slug": "flask",
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"typical_lifespan": "EVERGREEN",
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},
"matched_via": "alias"
}
],
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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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"new_alias_text": null,
"new_skill_meta": null,
"source_tag": "db",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [],
"input_skill": "Pandas",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": {
"derived": {
"category": "Data Engineering Tools",
"skill_nature": "TOOL",
"sub_category": "Data Analysis Libraries",
"typical_lifespan": "MULTI_YEAR",
"version_strategy": "UNVERSIONED",
"volatility": "MEDIUM"
},
"enrichment": null,
"keep_log": [],
"locked_dimensions": [],
"merge_log": [],
"placed": null,
"relationships": null,
"skill_id": "pandas",
"split_log": [],
"typed": null,
"warnings": []
},
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [],
"input_skill": "DataFrame",
"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": "EVERGREEN",
"version_strategy": "UNVERSIONED",
"volatility": "STABLE"
},
"enrichment": null,
"keep_log": [],
"locked_dimensions": [],
"merge_log": [],
"placed": null,
"relationships": null,
"skill_id": "dataframe",
"split_log": [],
"typed": null,
"warnings": []
},
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [
{
"alias_text": "Django",
"alias_type": "CANONICAL",
"id": 82,
"is_primary": true,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Django 1",
"alias_type": "VERSION",
"id": 83,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Django 1.x",
"alias_type": "VERSION",
"id": 88,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Django 2",
"alias_type": "VERSION",
"id": 84,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Django 2.x",
"alias_type": "VERSION",
"id": 89,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Django 3",
"alias_type": "VERSION",
"id": 85,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Django 3.x",
"alias_type": "VERSION",
"id": 90,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Django 4",
"alias_type": "VERSION",
"id": 86,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Django 4.x",
"alias_type": "VERSION",
"id": 91,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Django 5",
"alias_type": "VERSION",
"id": 87,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Django 5.x",
"alias_type": "VERSION",
"id": 92,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Django1",
"alias_type": "VERSION",
"id": 2285,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Django2",
"alias_type": "VERSION",
"id": 2286,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Django3",
"alias_type": "VERSION",
"id": 2287,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Django4",
"alias_type": "VERSION",
"id": 2288,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Django5",
"alias_type": "VERSION",
"id": 2289,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "django 2",
"alias_type": "VERSION",
"id": 6523,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "django 2.x",
"alias_type": "VERSION",
"id": 6531,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "django 3",
"alias_type": "VERSION",
"id": 6524,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "django 3.x",
"alias_type": "VERSION",
"id": 6532,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "django 4",
"alias_type": "VERSION",
"id": 6525,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "django 4.x",
"alias_type": "VERSION",
"id": 6533,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "django 5",
"alias_type": "VERSION",
"id": 3569,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "django 5.0",
"alias_type": "VERSION",
"id": 3572,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "django 5.x",
"alias_type": "VERSION",
"id": 3573,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "django2",
"alias_type": "VERSION",
"id": 6519,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "django2.x",
"alias_type": "VERSION",
"id": 6527,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "django3",
"alias_type": "VERSION",
"id": 6520,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "django3.x",
"alias_type": "VERSION",
"id": 6528,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "django4",
"alias_type": "VERSION",
"id": 6521,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "django4.x",
"alias_type": "VERSION",
"id": 6529,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "django5",
"alias_type": "VERSION",
"id": 3568,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "django5.0",
"alias_type": "VERSION",
"id": 3570,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "django5.x",
"alias_type": "VERSION",
"id": 3571,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
}
],
"canonical": {
"category_id": 5,
"display_name": "Django",
"id": 9,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "FRAMEWORK",
"slug": "django",
"sub_category_id": 35,
"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"dimensions": [
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Frameworks \u0026 Libraries",
"id": 360,
"rationale": "Manage adoption, integration, and best practices around key software frameworks and libraries.",
"slug": "frameworks-libraries",
"source": "db"
},
"input_skill": "Django",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Drupal Dev",
"id": 228,
"rationale": null,
"role_archetype": "Engineering",
"slug": "drupal-dev",
"source": "db"
},
{
"display_name": "Engineering Manager",
"id": 121,
"rationale": null,
"role_archetype": null,
"slug": "engineering-manager",
"source": "db"
}
]
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Web Application Frameworks",
"id": 2,
"rationale": "Server frameworks and runtimes used to build HTTP services, controllers, middleware, and request pipelines. These frameworks shape how backend endpoints are structured and delivered.",
"slug": "web-application-frameworks",
"source": "db"
},
"input_skill": "Django",
"llm_role": null,
"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"
},
{
"display_name": "Java Backend Developer",
"id": 79,
"rationale": null,
"role_archetype": "Engineering",
"slug": "java-backend-developer",
"source": "db"
},
{
"display_name": "Node.js Backend Developer",
"id": 82,
"rationale": null,
"role_archetype": "Engineering",
"slug": "node-backend-developer",
"source": "db"
},
{
"display_name": "PHP Backend Developer",
"id": 86,
"rationale": null,
"role_archetype": "Engineering",
"slug": "php-backend-developer",
"source": "db"
},
{
"display_name": "Python Backend Developer",
"id": 80,
"rationale": null,
"role_archetype": "Engineering",
"slug": "python-backend-developer",
"source": "db"
}
]
}
],
"input_skill": "Django",
"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": "Flask",
"alias_type": "CANONICAL",
"id": 1980,
"is_primary": true,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "flask 2",
"alias_type": "VERSION",
"id": 1985,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "flask 2.x",
"alias_type": "VERSION",
"id": 1987,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "flask 3",
"alias_type": "VERSION",
"id": 1982,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "flask 3.x",
"alias_type": "VERSION",
"id": 1983,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "flask2",
"alias_type": "VERSION",
"id": 1986,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "flask3",
"alias_type": "VERSION",
"id": 1981,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "flask\u003e=3",
"alias_type": "VERSION",
"id": 1984,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
}
],
"canonical": {
"category_id": 5,
"display_name": "Flask",
"id": 1344,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "FRAMEWORK",
"slug": "flask",
"sub_category_id": 35,
"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": "Flask",
"llm_role": null,
"roles_from_db": []
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Web Application Frameworks",
"id": 2,
"rationale": "Server frameworks and runtimes used to build HTTP services, controllers, middleware, and request pipelines. These frameworks shape how backend endpoints are structured and delivered.",
"slug": "web-application-frameworks",
"source": "db"
},
"input_skill": "Flask",
"llm_role": null,
"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"
},
{
"display_name": "Java Backend Developer",
"id": 79,
"rationale": null,
"role_archetype": "Engineering",
"slug": "java-backend-developer",
"source": "db"
},
{
"display_name": "Node.js Backend Developer",
"id": 82,
"rationale": null,
"role_archetype": "Engineering",
"slug": "node-backend-developer",
"source": "db"
},
{
"display_name": "PHP Backend Developer",
"id": 86,
"rationale": null,
"role_archetype": "Engineering",
"slug": "php-backend-developer",
"source": "db"
},
{
"display_name": "Python Backend Developer",
"id": 80,
"rationale": null,
"role_archetype": "Engineering",
"slug": "python-backend-developer",
"source": "db"
}
]
}
],
"input_skill": "Flask",
"matched_via": "alias",
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": null,
"source_tag": "db",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [],
"input_skill": "Jinja2",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": {
"derived": {
"category": "Web Frameworks",
"skill_nature": "TOOL",
"sub_category": "Template Engines",
"typical_lifespan": "MULTI_YEAR",
"version_strategy": "UNVERSIONED",
"volatility": "MEDIUM"
},
"enrichment": null,
"keep_log": [],
"locked_dimensions": [],
"merge_log": [],
"placed": null,
"relationships": null,
"skill_id": "jinja2",
"split_log": [],
"typed": null,
"warnings": []
},
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [],
"input_skill": "Mako",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": {
"derived": {
"category": "Web Frameworks",
"skill_nature": "TOOL",
"sub_category": "Template Engines",
"typical_lifespan": "MULTI_YEAR",
"version_strategy": "UNVERSIONED",
"volatility": "MEDIUM"
},
"enrichment": null,
"keep_log": [],
"locked_dimensions": [],
"merge_log": [],
"placed": null,
"relationships": null,
"skill_id": "mako",
"split_log": [],
"typed": null,
"warnings": []
},
"source_tag": "llm",
"was_in_llm_skills": true
}
],
"unmatched_skills": [
"MapReduce",
"Pandas",
"DataFrame",
"Jinja2",
"Mako"
]
}
API 3 — final-role-output
{
"chosen_role": {
"display_name": "Data Engineer",
"id": 2,
"rationale": "Domain=Data Engineering \u0026 Analytics; The JD focuses on Python/PySpark-based data extraction, transformation, and pipeline creation, which most closely matches a Data Engineer role.",
"role_archetype": null,
"slug": "data-engineer",
"source": "db"
},
"chosen_role_resolution": "in_db",
"final_input_skills": [
{
"skill": "Spark",
"tag": "in_db"
},
{
"skill": "PySpark",
"tag": "in_db"
},
{
"skill": "Python",
"tag": "in_db"
},
{
"skill": "MapReduce",
"tag": "new"
},
{
"skill": "C",
"tag": "in_db"
},
{
"skill": "Pandas",
"tag": "new"
},
{
"skill": "DataFrame",
"tag": "new"
},
{
"skill": "Django",
"tag": "in_db"
},
{
"skill": "Flask",
"tag": "in_db"
},
{
"skill": "Jinja2",
"tag": "new"
},
{
"skill": "Mako",
"tag": "new"
}
],
"llm_cost_api1_usd": null,
"llm_cost_api2_usd": null,
"llm_cost_api3_usd": null,
"llm_cost_total_usd": null,
"persistence": {
"items": [
{
"chosen_role_id": 2,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "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": "Spark",
"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": 1350,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 2,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "ETL and ELT Tooling",
"id": 24,
"rationale": "Packaged tools for extracting, loading, and transforming data across systems. This dimension covers connector-based ingestion, transformation frameworks, and managed integration products.",
"slug": "etl-and-elt-tooling",
"source": "db"
},
"dimension_id": 24,
"input_skill": "PySpark",
"llm_role": null,
"matched_chosen_role": true,
"outcome_line": "Skipped \u2014 no persistable v3 meta for new skill",
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "Data Engineer",
"id": 2,
"rationale": null,
"role_archetype": null,
"slug": "data-engineer",
"source": "db"
}
],
"skill_dimension_saved": false,
"skill_id": null,
"skill_tag": "new",
"skipped_reason": "skill_not_in_db_v3_proposed"
},
{
"chosen_role_id": 2,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Cloud Security Scripting \u0026 DSL Languages",
"id": 248,
"rationale": "Proficiency in programming and domain-specific languages used to automate and script cloud security controls.",
"slug": "cloud-security-scripting-dsl-languages",
"source": "db"
},
"dimension_id": 248,
"input_skill": "Python",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "Cloud Security Engineer",
"id": 23,
"rationale": null,
"role_archetype": null,
"slug": "cloud-security-engineer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 5,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 2,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Programming Languages",
"id": 1,
"rationale": "Primary implementation languages used to build client and server feature code. Full stack engineers need enough fluency to move across layers and implement product behavior end to end.",
"slug": "programming-languages",
"source": "db"
},
"dimension_id": 1,
"input_skill": "Python",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "Backend Developer",
"id": 1,
"rationale": null,
"role_archetype": "A Backend Engineer designs, builds, and maintains the server-side logic and data handling that power applications and services. They focus on implementing reliable business functionality, integrating with other systems, and ensuring the backend is scalable, maintainable, and observable.",
"slug": "backend-engineer",
"source": "db"
},
{
"display_name": "Fullstack Developer",
"id": 15,
"rationale": null,
"role_archetype": null,
"slug": "full-stack-engineer",
"source": "db"
},
{
"display_name": "Fullstack Developer",
"id": 435,
"rationale": null,
"role_archetype": "Engineering",
"slug": "fullstack-developer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 5,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 2,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Programming Languages \u0026 DSLs",
"id": 475,
"rationale": "Oversee and guide the selection and effective use of programming and domain\u2010specific languages in software projects.",
"slug": "programming-languages-dsls",
"source": "db"
},
"dimension_id": 475,
"input_skill": "Python",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "Engineering Manager",
"id": 121,
"rationale": null,
"role_archetype": null,
"slug": "engineering-manager",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 5,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 2,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Programming Languages and Scripting",
"id": 59,
"rationale": "Languages used to write security automation, analysis scripts, detection logic, and remediation helpers. This is the primary implementation surface for a cybersecurity engineer across tooling and response workflows.",
"slug": "programming-languages-and-scripting",
"source": "db"
},
"dimension_id": 59,
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"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,
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"skill_tag": "in_db",
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{
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"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": true,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension saved",
"role_dimension_saved": true,
"roles_from_db": [
{
"display_name": "Data Engineer",
"id": 2,
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"role_archetype": null,
"slug": "data-engineer",
"source": "db"
}
],
"skill_dimension_saved": true,
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"skipped_reason": null
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{
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"dimension": {
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"display_name": "Programming Languages for ML Systems",
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"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": [
{
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"slug": "ml-engineer",
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{
"display_name": "MLOps Engineer",
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"role_archetype": null,
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"source": "db"
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],
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{
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"dimension": {
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"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",
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"role_dimension_saved": false,
"roles_from_db": [
{
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"rationale": null,
"role_archetype": null,
"slug": "ar-vr-engineer",
"source": "db"
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],
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{
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"id": 290,
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"slug": "python-programming",
"source": "db"
},
"dimension_id": 290,
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"role_dimension_saved": false,
"roles_from_db": [
{
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"id": 80,
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"role_archetype": "Engineering",
"slug": "python-backend-developer",
"source": "db"
}
],
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"skill_tag": "in_db",
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{
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"display_name": "C# and .NET Languages",
"id": 362,
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"slug": "c-and-net-languages",
"source": "db"
},
"dimension_id": 362,
"input_skill": "C",
"llm_role": null,
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"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
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"roles_from_db": [
{
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"id": 83,
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"role_archetype": "Engineering",
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"source": "db"
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],
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"skill_id": 4,
"skill_tag": "in_db",
"skipped_reason": null
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{
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"slug": "cross-platform-app-languages",
"source": "db"
},
"dimension_id": 167,
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"roles_from_db": [
{
"display_name": "Hybrid Mobile Developer",
"id": 11,
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"slug": "hybrid-mobile-developer",
"source": "db"
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],
"skill_dimension_saved": true,
"skill_id": 4,
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{
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},
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{
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"slug": "backend-engineer",
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},
{
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{
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],
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"skill_tag": "in_db",
"skipped_reason": null
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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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"roles_from_db": [
{
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"slug": "ml-engineer",
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{
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"slug": "ml-ops-engineer",
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}
],
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{
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"source": "db"
},
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"roles_from_db": [
{
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],
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{
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"source": "db"
},
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{
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"display_name": "Sitecore Development Languages",
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"slug": "sitecore-development-languages",
"source": "db"
},
"dimension_id": 438,
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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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"id": 233,
"rationale": null,
"role_archetype": "Engineering",
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"source": "db"
}
],
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"skill_tag": "in_db",
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},
{
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"display_name": "Video Codec Languages and DSLs",
"id": 225,
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"slug": "video-codec-languages-and-dsls",
"source": "db"
},
"dimension_id": 225,
"input_skill": "C",
"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": "Video Codec Engineer",
"id": 22,
"rationale": null,
"role_archetype": null,
"slug": "video-codec-engineer",
"source": "db"
}
],
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"skill_id": 4,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 2,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Frameworks \u0026 Libraries",
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"rationale": "Manage adoption, integration, and best practices around key software frameworks and libraries.",
"slug": "frameworks-libraries",
"source": "db"
},
"dimension_id": 360,
"input_skill": "Django",
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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": "Drupal Dev",
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"rationale": null,
"role_archetype": "Engineering",
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"source": "db"
},
{
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}
],
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{
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},
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"roles_from_db": [
{
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{
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{
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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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"role_archetype": "Engineering",
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{
"display_name": "Python Backend Developer",
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"role_archetype": "Engineering",
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"source": "db"
}
],
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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.