Pipeline run
0776745a-5109-40f0-89f5-3ce44415983e
Client output enrichment
v2 Skill cluster · Nature of work · AI index · Tech stack maturity · Evidence · KRA descriptionvocab breakdown (legacy)
Signals
1 POST /skills/extract-from-jd
2 POST /skills/extract-details
3 POST /skills/final-role-output
Data Engineer
CASE Aslug: data-engineer · id: 2 · source: db
Multi-alias tie (3 roles at 1.0) resolved by TIER_B_TITLE: Data Engineer
Resolution:
in_db
— role exists in library; skill↔dim and role↔dim links saved when applicable.
Job description
Hello Everyone We are hiring Python Developer TE-8+ Years Years in Corporate IT Industry Location- Remote NP-15 Days/ Immediate Joiner Kindly find the JD attached below. Must Have : Good With SQL(Advance SQL + Able to write SQL Quires + Python + Libraries like NumPy and panda + Excel) Minimum skill: Advanced to expert level SQL Skills, including CTEs (Common Table Expressions), advanced SQL string and regex functions, and heavy use of correlated subqueries. Ability to deconstruct SQL code to derive source tables/views and query dependencies without benefit of data dictionary is critical. Strong R and Python programming skills, using R-studio and Jupyter Notebook. Heavy emphasis on programming structures (if/then, for/while loops, file-system and import/export operations, string manipulation, debugging, regex, and error-handling). An “error-handling and precedence order” expert would be particularly useful. Heavy expertise in programmatic calls to vendor APIs (using R and Python), including JSON to RDBMS (preferably Netezza) table conversions, regex manipulation, and date/time functions. Extensive knowledge of ODBC connectivity in MS-Excel. Expert-level use of excel formulas for data-aggregation, data-connection objects, and pivot tables. Some excel macro/VBA scripting desirable. Visual Basic/Powershell experience for programmatic Excel file manipulations. Technical writing/process documentation experience. Nice to have: Google Big Query expertise (querying GA data stored in BigQuery). Knowledge of MS-SQL Server table manipulation, SSIS ETL jobs, ODBC/Linked-server connectivity, T-SQL programming. Experience with creating data visualizations with MS-Excel. Basic Data Science understanding. Ability to perform EDA (Exploratory Data Analysis) on complex data sets a huge plus. Microstrategy for visualizations. #interestedcandidates can share their cv and Reference atkirti.dhawan@codersbrain.com
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
- 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) |
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 & DSLs Catalog dimension db id 475
Library dimension (catalog)
Roles linked in library: Engineering Manager
-
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 & DSLs
programming-languages-dsls
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Programming Languages for Data Work
programming-languages-for-data-work
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved |
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Data Engineering Tools
- Sub-category
- general
- Skill nature
- TOOL
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
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
- Data Engineering Tools
- Sub-category
- general
- Skill nature
- TOOL
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Aliases — catalog
- R (VERSION)
- R 3 (VERSION)
- R 3.x (VERSION)
- R 4 (VERSION)
- R 4.0 (VERSION)
- R 4.1 (VERSION)
- R 4.2 (VERSION)
- R 4.3 (VERSION)
- R 4.4 (VERSION)
- R 4.x (VERSION)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Language
- Sub-category
- Programming Language
- Vendor
- R Core Team
- License
- gpl_v2
- Year introduced
- 1993
- Confidence
- 0.99
- Version strategy
- SEPARATE_ENTITY
- Version tag
- R 4.x
Maturity reasoning: R appears in many data science, statistics, and analytics job postings, and CRAN remains active with broad package usage across academia and industry.
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)
-
Programming Languages for ML Systems Catalog dimension db id 39
Library dimension (catalog)
Roles linked in library: ML Engineer, MLOps Engineer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Programming Languages for ML Systems
programming-languages-for-ml-systems
|
✓ | — | 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
- 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
- Concepts
- Sub-category
- general
- Skill nature
- CONCEPT
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Aliases — catalog
- JSON (CANONICAL) primary
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Format
- Sub-category
- Data Interchange Format
- Confidence
- 0.99
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: JSON is a default data interchange format in APIs and web stacks; it appears in a very high volume of job descriptions and is supported by every major language/runtime.
Skill profile (library / DB)
- Skill nature
- STANDARD
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 4
- Sub-category id
- 1457
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
API Integration and Data Fetching Catalog dimension db id 127
Library dimension (catalog)
Roles linked in library: Angular Frontend Developer, Frontend Developer, Fullstack Developer, React Frontend Developer, Svelte Frontend Developer, Vue Frontend Developer, Web Developer
-
API Interface and Contract Design Catalog dimension db id 289
Library dimension (catalog)
Roles linked in library: .NET Backend Developer, Go Backend Developer, Kotlin Backend Developer, Node.js Backend Developer, PHP Backend Developer, Python Backend Developer, Ruby Backend Developer, Scala Backend Developer
-
Integration Protocols & Standards Catalog dimension db id 271
Library dimension (catalog)
Roles linked in library: Pega Developer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
API Integration and Data Fetching
api-integration-and-data-fetching
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
API Interface and Contract Design
api-interface-and-contract-design
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Integration Protocols & Standards
integration-protocols-standards
|
✓ | — | 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
- Databases
- 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
- 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
- Data Engineering Tools
- Sub-category
- general
- Skill nature
- PRACTICE
- 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
- 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
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Aliases — catalog
- PowerShell (CANONICAL) primary
- PowerShell 5 (VERSION)
- PowerShell 5.1 (VERSION)
- PowerShell 6 (VERSION)
- PowerShell 7 (VERSION)
- PowerShell 7.x (VERSION)
- PowerShell Core (VERSION)
- Windows PowerShell (VERSION)
- powershell 7 (VERSION)
- powershell 7.x (VERSION)
- powershell core (VERSION)
- ps 7 (VERSION)
- pwsh (VERSION)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Language
- Sub-category
- Scripting Language
- Vendor
- Microsoft
- License
- mit
- Year introduced
- 2006
- Confidence
- 0.98
- Version strategy
- SEPARATE_ENTITY
- Version tag
- 7
Maturity reasoning: Common in Windows/admin and DevOps job descriptions; Microsoft continues active development and it remains a standard automation language alongside Bash in enterprise tooling.
Skill profile (library / DB)
- Skill nature
- LANGUAGE
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 6
- Sub-category id
- 38
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
Programming Languages and Scripting Catalog dimension db id 59
Library dimension (catalog)
Roles linked in library: Cyber Security Engineer
-
Programming Languages for ML Systems Catalog dimension db id 39
Library dimension (catalog)
Roles linked in library: ML Engineer, MLOps Engineer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Programming Languages and Scripting
programming-languages-and-scripting
|
✓ | — | 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) |
Aliases — catalog
- BigQuery (CANONICAL) primary
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Service
- Sub-category
- Data Warehouse Service
- Vendor
- License
- proprietary
- Year introduced
- 2011
- Confidence
- 0.98
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: BigQuery appears frequently in data/analytics job descriptions and is a core Google Cloud warehouse offering, with broad enterprise adoption and strong ecosystem support.
Skill profile (library / DB)
- Skill nature
- CLOUD_SERVICE
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 11
- Sub-category id
- 118
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
Cloud Data Warehouses Catalog dimension db id 22
Library dimension (catalog)
Roles linked in library: Data Engineer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Cloud Data Warehouses
cloud-data-warehouses
|
— | — |
Skipped — no persistable v3 meta for new skill
skill_not_in_db_v3_proposed
|
Aliases — catalog
- SQL Server (CANONICAL) primary
- SQL Server 2000 (VERSION)
- SQL Server 2005 (VERSION)
- SQL Server 2008 (VERSION)
- SQL Server 2012 (VERSION)
- SQL Server 2014 (VERSION)
- SQL Server 2016 (VERSION)
- SQL Server 2017 (VERSION)
- SQL Server 2019 (VERSION)
- SQL Server 2022 (VERSION)
- SQL Server 6.5 (VERSION)
- SQL Server 7.0 (VERSION)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Datastore
- Sub-category
- Relational Database
- Vendor
- Microsoft
- License
- proprietary
- Year introduced
- 1989
- Confidence
- 0.99
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: SQL Server appears in many enterprise job descriptions and remains a major Microsoft-supported RDBMS with active Azure SQL/SQL Server demand; it is a common hiring-pipeline staple, not a sunset technology.
Skill profile (library / DB)
- Skill nature
- TOOL
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 3
- Sub-category id
- 29
- 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
- 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
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Databases
- 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
- 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
- Data Engineering Tools
- Sub-category
- general
- Skill nature
- PRACTICE
- 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
- PRACTICE
- 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
- PRACTICE
- 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 |
|---|---|---|---|---|---|---|
| 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) | |
| 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 & DSLs
programming-languages-dsls
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| SQL | in_db |
Programming Languages for Data Work
programming-languages-for-data-work
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved | |
| R | in_db |
Programming Languages for ML Systems
programming-languages-for-ml-systems
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| JSON | in_db |
API Integration and Data Fetching
api-integration-and-data-fetching
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| JSON | in_db |
API Interface and Contract Design
api-interface-and-contract-design
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| JSON | in_db |
Integration Protocols & Standards
integration-protocols-standards
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| PowerShell | in_db |
Programming Languages and Scripting
programming-languages-and-scripting
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| PowerShell | in_db |
Programming Languages for ML Systems
programming-languages-for-ml-systems
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Google BigQuery | new |
Cloud Data Warehouses
cloud-data-warehouses
|
— | — | Skipped — no persistable v3 meta for new skill | skill_not_in_db_v3_proposed |
| MS SQL Server | 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 | NumPy | type=Data Engineering Tools subtype=general nature=TOOL lifespan=MULTI_YEAR | |
| canonical_skill_proposed | pandas | type=Data Engineering Tools subtype=general nature=TOOL lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Excel | type=Data Engineering Tools subtype=general nature=TOOL lifespan=MULTI_YEAR | |
| canonical_skill_proposed | RStudio | type=Data Engineering Tools subtype=general nature=TOOL lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Jupyter Notebook | type=Data Engineering Tools subtype=general nature=TOOL lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Regex | type=Concepts subtype=general nature=CONCEPT lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Netezza | type=Databases subtype=general nature=TOOL lifespan=MULTI_YEAR | |
| canonical_skill_proposed | ODBC | type=Data Engineering Tools subtype=general nature=TOOL lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Pivot Tables | type=Data Engineering Tools subtype=general nature=PRACTICE lifespan=MULTI_YEAR | |
| canonical_skill_proposed | VBA | type=Programming Languages subtype=general nature=LANGUAGE lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Visual Basic | type=Programming Languages subtype=general nature=LANGUAGE lifespan=MULTI_YEAR | |
| canonical_skill_proposed | SSIS | type=Data Engineering Tools subtype=general nature=TOOL lifespan=MULTI_YEAR | |
| canonical_skill_proposed | T-SQL | type=Programming Languages subtype=general nature=LANGUAGE lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Linked Server | type=Databases subtype=general nature=CONCEPT lifespan=MULTI_YEAR | |
| canonical_skill_proposed | MicroStrategy | type=Data Engineering Tools subtype=general nature=TOOL lifespan=MULTI_YEAR | |
| canonical_skill_proposed | ETL | type=Data Engineering Tools subtype=general nature=PRACTICE lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Data Visualization | type=Data Engineering Tools subtype=general nature=PRACTICE lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Exploratory Data Analysis | type=Data Engineering Tools subtype=general nature=PRACTICE lifespan=MULTI_YEAR | |
| dimension_skill_link_proposed | Google BigQuery ↔ Cloud Data Warehouses | |
| role_dimension_link_proposed | Data Engineer ↔ Cloud Data Warehouses | |
| dimension_skill_link_proposed | MS SQL Server ↔ 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": 8,
"raw": "8+ Years Years in Corporate IT Industry"
},
"job_locations": [
{
"aliases": [],
"city": null,
"country": null,
"state": null,
"work_mode": "remote"
}
],
"role": "Python Developer",
"role_aliases": [
"Python Engineer",
"Data Engineer",
"SQL Developer"
],
"role_archetype": "Data",
"roles_and_responsibilities": [
{
"bullet_count": 0,
"heading": "Must Have",
"heading_was_present": true,
"source_marker": {
"first_5_words": "Good With SQL(Advance SQL +",
"last_5_words": "panda + Excel"
},
"text": "Good With SQL(Advance SQL + Able to write SQL Quires + Python + Libraries like NumPy and panda + Excel",
"word_count": 19
},
{
"bullet_count": 0,
"heading": "Minimum skill",
"heading_was_present": true,
"source_marker": {
"first_5_words": "Advanced to expert level SQL",
"last_5_words": "writing/process documentation experience."
},
"text": "Advanced to expert level SQL Skills, including CTEs (Common Table Expressions), advanced SQL string and regex functions, and heavy use of correlated subqueries.\nAbility to deconstruct SQL code to derive source tables/views and query dependencies without benefit of data dictionary is critical.\nStrong R and Python programming skills, using R-studio and Jupyter Notebook. Heavy emphasis on programming structures (if/then, for/while loops, file-system and import/export operations, string manipulation, debugging, regex, and error-handling). An \u201cerror-handling and precedence order\u201d expert would be particularly useful.\nHeavy expertise in programmatic calls to vendor APIs (using R and Python), including JSON to RDBMS (preferably Netezza) table conversions, regex manipulation, and date/time functions.\nExtensive knowledge of ODBC connectivity in MS-Excel. Expert-level use of excel formulas for data-aggregation, data-connection objects, and pivot tables. Some excel macro/VBA scripting desirable.\nVisual Basic/Powershell experience for programmatic Excel file manipulations.\nTechnical writing/process documentation experience.",
"word_count": 164
},
{
"bullet_count": 5,
"heading": "Nice to have",
"heading_was_present": true,
"source_marker": {
"first_5_words": "Google Big Query expertise (querying",
"last_5_words": "for visualizations."
},
"text": "Google Big Query expertise (querying GA data stored in BigQuery).\nKnowledge of MS-SQL Server table manipulation, SSIS ETL jobs, ODBC/Linked-server connectivity, T-SQL programming.\nExperience with creating data visualizations with MS-Excel.\nBasic Data Science understanding. Ability to perform EDA (Exploratory Data Analysis) on complex data sets a huge plus.\nMicrostrategy for visualizations.",
"word_count": 66
}
],
"urls": []
}
API 1 — extract-from-jd click to toggle
{
"final_skills": [
{
"is_primary": true,
"skill_name": "Python"
},
{
"is_primary": true,
"skill_name": "SQL"
},
{
"is_primary": false,
"skill_name": "NumPy"
},
{
"is_primary": false,
"skill_name": "pandas"
},
{
"is_primary": true,
"skill_name": "Excel"
},
{
"is_primary": true,
"skill_name": "R"
},
{
"is_primary": false,
"skill_name": "RStudio"
},
{
"is_primary": false,
"skill_name": "Jupyter Notebook"
},
{
"is_primary": true,
"skill_name": "Regex"
},
{
"is_primary": false,
"skill_name": "JSON"
},
{
"is_primary": false,
"skill_name": "Netezza"
},
{
"is_primary": true,
"skill_name": "ODBC"
},
{
"is_primary": false,
"skill_name": "Pivot Tables"
},
{
"is_primary": false,
"skill_name": "VBA"
},
{
"is_primary": false,
"skill_name": "Visual Basic"
},
{
"is_primary": false,
"skill_name": "PowerShell"
},
{
"is_primary": false,
"skill_name": "Google BigQuery"
},
{
"is_primary": false,
"skill_name": "MS SQL Server"
},
{
"is_primary": false,
"skill_name": "SSIS"
},
{
"is_primary": false,
"skill_name": "T-SQL"
},
{
"is_primary": false,
"skill_name": "Linked Server"
},
{
"is_primary": false,
"skill_name": "MicroStrategy"
},
{
"is_primary": false,
"skill_name": "ETL"
},
{
"is_primary": false,
"skill_name": "Data Visualization"
},
{
"is_primary": false,
"skill_name": "Exploratory Data Analysis"
}
],
"jd_role": {
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"rationale": null,
"role_aliases": [
"Python Engineer",
"Data Engineer",
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],
"role_archetype": "Data",
"slug": ""
},
"nano_parsed": {
"JD_type": "pass",
"about_company": null,
"certifications": [],
"company_name": null,
"ctc": null,
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},
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{
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"city": null,
"country": null,
"state": null,
"work_mode": "remote"
}
],
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"Python Engineer",
"Data Engineer",
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],
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{
"bullet_count": 0,
"heading": "Must Have",
"heading_was_present": true,
"source_marker": {
"first_5_words": "Good With SQL(Advance SQL +",
"last_5_words": "panda + Excel"
},
"text": "Good With SQL(Advance SQL + Able to write SQL Quires + Python + Libraries like NumPy and panda + Excel",
"word_count": 19
},
{
"bullet_count": 0,
"heading": "Minimum skill",
"heading_was_present": true,
"source_marker": {
"first_5_words": "Advanced to expert level SQL",
"last_5_words": "writing/process documentation experience."
},
"text": "Advanced to expert level SQL Skills, including CTEs (Common Table Expressions), advanced SQL string and regex functions, and heavy use of correlated subqueries.\nAbility to deconstruct SQL code to derive source tables/views and query dependencies without benefit of data dictionary is critical.\nStrong R and Python programming skills, using R-studio and Jupyter Notebook. Heavy emphasis on programming structures (if/then, for/while loops, file-system and import/export operations, string manipulation, debugging, regex, and error-handling). An \u201cerror-handling and precedence order\u201d expert would be particularly useful.\nHeavy expertise in programmatic calls to vendor APIs (using R and Python), including JSON to RDBMS (preferably Netezza) table conversions, regex manipulation, and date/time functions.\nExtensive knowledge of ODBC connectivity in MS-Excel. Expert-level use of excel formulas for data-aggregation, data-connection objects, and pivot tables. Some excel macro/VBA scripting desirable.\nVisual Basic/Powershell experience for programmatic Excel file manipulations.\nTechnical writing/process documentation experience.",
"word_count": 164
},
{
"bullet_count": 5,
"heading": "Nice to have",
"heading_was_present": true,
"source_marker": {
"first_5_words": "Google Big Query expertise (querying",
"last_5_words": "for visualizations."
},
"text": "Google Big Query expertise (querying GA data stored in BigQuery).\nKnowledge of MS-SQL Server table manipulation, SSIS ETL jobs, ODBC/Linked-server connectivity, T-SQL programming.\nExperience with creating data visualizations with MS-Excel.\nBasic Data Science understanding. Ability to perform EDA (Exploratory Data Analysis) on complex data sets a huge plus.\nMicrostrategy for visualizations.",
"word_count": 66
}
],
"urls": []
},
"rejected": false,
"rejection_reason": null,
"run_id": "0776745a-5109-40f0-89f5-3ce44415983e",
"stage3_signals": {
"alias_found": true,
"alias_match_roles": [
{
"display_name": "Data Engineer",
"kra_matches": null,
"matched_count": null,
"matched_skills": null,
"role_id": 2,
"score": 1.0,
"slug": "data-engineer",
"total_count": null
},
{
"display_name": "Backend Developer",
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"matched_count": null,
"matched_skills": null,
"role_id": 1,
"score": 1.0,
"slug": "backend-engineer",
"total_count": null
},
{
"display_name": "Python Backend Developer",
"kra_matches": null,
"matched_count": null,
"matched_skills": null,
"role_id": 80,
"score": 1.0,
"slug": "python-backend-developer",
"total_count": null
}
],
"kra_match_roles": [
{
"display_name": "Data Engineer",
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{
"kra_text": "Designs dimensional models, star schemas, data vault structures, and curated data mart tables to support BI tools and self-service analytics consumption.",
"sentence": "Ability to deconstruct SQL code to derive source tables/views and query dependencies without benefit of data dictionary is critical.",
"similarity": 0.4772
},
{
"kra_text": "Optimizes pipeline throughput, partitioning strategies, and query performance across cloud data warehouses like Snowflake, BigQuery, or Redshift.",
"sentence": "Google Big Query expertise (querying GA data stored in BigQuery).",
"similarity": 0.4741
},
{
"kra_text": "Implements data transformation, cleansing, deduplication, and enrichment logic to convert raw source data into analytics-ready curated datasets.",
"sentence": "Ability to perform EDA (Exploratory Data Analysis) on complex data sets a huge plus.",
"similarity": 0.4521
}
],
"matched_count": null,
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"role_id": 2,
"score": 0.4678,
"slug": "data-engineer",
"total_count": null
},
{
"display_name": "Fullstack Developer",
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{
"kra_text": "Designs and queries relational databases like PostgreSQL and document stores like MongoDB, writing migrations, indexes, and optimized queries.",
"sentence": "Advanced to expert level SQL Skills, including CTEs (Common Table Expressions), advanced SQL string and regex functions, and heavy use of correlated subqueries.",
"similarity": 0.4744
},
{
"kra_text": "Designs and queries relational databases like PostgreSQL and document stores like MongoDB, writing migrations, indexes, and optimized queries.",
"sentence": "Google Big Query expertise (querying GA data stored in BigQuery).",
"similarity": 0.4485
},
{
"kra_text": "Designs and queries relational databases like PostgreSQL and document stores like MongoDB, writing migrations, indexes, and optimized queries.",
"sentence": "Ability to deconstruct SQL code to derive source tables/views and query dependencies without benefit of data dictionary is critical.",
"similarity": 0.4192
}
],
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"role_id": 15,
"score": 0.4474,
"slug": "full-stack-engineer",
"total_count": null
},
{
"display_name": "Backend Developer",
"kra_matches": [
{
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"sentence": "Ability to deconstruct SQL code to derive source tables/views and query dependencies without benefit of data dictionary is critical.",
"similarity": 0.4557
},
{
"kra_text": "Identifies and resolves backend performance bottlenecks through query optimization, indexing strategies, connection pooling, and distributed caching with Redis.",
"sentence": "Advanced to expert level SQL Skills, including CTEs (Common Table Expressions), advanced SQL string and regex functions, and heavy use of correlated subqueries.",
"similarity": 0.4239
},
{
"kra_text": "Writes database access logic including SQL queries, ORM mappings, stored procedures, and migration scripts for relational databases like PostgreSQL and MySQL.",
"sentence": "Heavy expertise in programmatic calls to vendor APIs (using R and Python), including JSON to RDBMS (preferably Netezza) table conversions, regex manipulation, and date/time functions.",
"similarity": 0.4124
}
],
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"matched_skills": null,
"role_id": 1,
"score": 0.4306,
"slug": "backend-engineer",
"total_count": null
},
{
"display_name": "Pega Developer",
"kra_matches": [
{
"kra_text": "Requirements analysis and process translation",
"sentence": "Technical writing/process documentation experience.",
"similarity": 0.45
},
{
"kra_text": "exception, escalation, and assignment handling",
"sentence": "An \u201cerror-handling and precedence order\u201d expert would be particularly useful.",
"similarity": 0.4428
},
{
"kra_text": "Requirements analysis and process translation",
"sentence": "Ability to deconstruct SQL code to derive source tables/views and query dependencies without benefit of data dictionary is critical.",
"similarity": 0.3921
}
],
"matched_count": null,
"matched_skills": null,
"role_id": 24,
"score": 0.4283,
"slug": "pega-developer",
"total_count": null
},
{
"display_name": "Node.js Backend Developer",
"kra_matches": [
{
"kra_text": "code review and refactoring",
"sentence": "Heavy emphasis on programming structures (if/then, for/while loops, file-system and import/export operations, string manipulation, debugging, regex, and error-handling).",
"similarity": 0.4709
},
{
"kra_text": "defect diagnosis and performance tuning",
"sentence": "Ability to deconstruct SQL code to derive source tables/views and query dependencies without benefit of data dictionary is critical.",
"similarity": 0.4169
},
{
"kra_text": "defect diagnosis and performance tuning",
"sentence": "Advanced to expert level SQL Skills, including CTEs (Common Table Expressions), advanced SQL string and regex functions, and heavy use of correlated subqueries.",
"similarity": 0.3834
}
],
"matched_count": null,
"matched_skills": null,
"role_id": 82,
"score": 0.4237,
"slug": "node-backend-developer",
"total_count": null
}
],
"skill_match_roles": [
{
"display_name": "Engineering Manager",
"kra_matches": null,
"matched_count": 2,
"matched_skills": [
"Python",
"SQL"
],
"role_id": 121,
"score": 0.3333,
"slug": "engineering-manager",
"total_count": 6
},
{
"display_name": "ML Engineer",
"kra_matches": null,
"matched_count": 2,
"matched_skills": [
"Python",
"R"
],
"role_id": 3,
"score": 0.3333,
"slug": "ml-engineer",
"total_count": 6
},
{
"display_name": "Data Engineer",
"kra_matches": null,
"matched_count": 2,
"matched_skills": [
"Python",
"SQL"
],
"role_id": 2,
"score": 0.3333,
"slug": "data-engineer",
"total_count": 6
},
{
"display_name": "MLOps Engineer",
"kra_matches": null,
"matched_count": 2,
"matched_skills": [
"Python",
"R"
],
"role_id": 16,
"score": 0.3333,
"slug": "ml-ops-engineer",
"total_count": 6
},
{
"display_name": "AR/VR Engineer",
"kra_matches": null,
"matched_count": 1,
"matched_skills": [
"Python"
],
"role_id": 8,
"score": 0.1667,
"slug": "ar-vr-engineer",
"total_count": 6
}
]
},
"stage4_decision": {
"alias_collision_detected": true,
"case": "A",
"chosen_role": {
"display_name": "Data Engineer",
"kra_matches": null,
"matched_count": null,
"matched_skills": null,
"role_id": 2,
"score": 1.0,
"slug": "data-engineer",
"total_count": null
},
"confidence": 0.95,
"is_new_role": false,
"llm2_fired": false,
"llm2_reasoning": null,
"matched_dimensions": [],
"matched_kras": [],
"matched_skills": [],
"new_role_display_name": null,
"new_role_slug": null,
"queued": false,
"reasoning": "Multi-alias tie (3 roles at 1.0) resolved by TIER_B_TITLE: Data Engineer",
"sub_role": null
},
"stage5_updates": null
}
API 2 — extract-details
{
"alias_matches": [
{
"alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
"alias_persisted": false,
"existing_alias_id": 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": "alias_text already exists for this canonical skill",
"alias_persisted": false,
"existing_alias_id": 271,
"existing_alias_text": "SQL",
"input_term": "SQL",
"matched_canonical": {
"category_id": 6,
"display_name": "SQL",
"id": 101,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "LANGUAGE",
"slug": "sql",
"sub_category_id": 97,
"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"matched_via": "alias"
},
{
"alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
"alias_persisted": false,
"existing_alias_id": 430,
"existing_alias_text": "R",
"input_term": "R",
"matched_canonical": {
"category_id": 6,
"display_name": "R",
"id": 194,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "LANGUAGE",
"slug": "r",
"sub_category_id": 96,
"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"matched_via": "alias"
},
{
"alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
"alias_persisted": false,
"existing_alias_id": 3018,
"existing_alias_text": "JSON",
"input_term": "JSON",
"matched_canonical": {
"category_id": 4,
"display_name": "JSON",
"id": 1984,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "STANDARD",
"slug": "json",
"sub_category_id": 1457,
"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": 583,
"existing_alias_text": "PowerShell",
"input_term": "PowerShell",
"matched_canonical": {
"category_id": 6,
"display_name": "PowerShell",
"id": 297,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "LANGUAGE",
"slug": "powershell",
"sub_category_id": 38,
"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": 300,
"existing_alias_text": "BigQuery",
"input_term": "Google BigQuery",
"matched_canonical": {
"category_id": 11,
"display_name": "BigQuery",
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"is_also_category": false,
"is_extractable": true,
"skill_nature": "CLOUD_SERVICE",
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"typical_lifespan": "EVERGREEN",
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},
"matched_via": "embedding_alias"
},
{
"alias_persist_skipped_reason": "TODO: REMOVE AFTER TESTING \u2014 alias DB write disabled",
"alias_persisted": false,
"existing_alias_id": 135,
"existing_alias_text": "SQL Server",
"input_term": "MS SQL Server",
"matched_canonical": {
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"display_name": "SQL Server",
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"skill_nature": "TOOL",
"slug": "sql-server",
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"typical_lifespan": "EVERGREEN",
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},
"matched_via": "embedding_alias"
}
],
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{
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"source": "db"
},
{
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{
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},
{
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},
{
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},
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},
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},
{
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"new_alias_text": null,
"new_skill_meta": {
"derived": {
"category": "Data Engineering Tools",
"skill_nature": "PRACTICE",
"sub_category": "general",
"typical_lifespan": "MULTI_YEAR",
"version_strategy": "UNVERSIONED",
"volatility": "MEDIUM"
},
"enrichment": null,
"keep_log": [],
"locked_dimensions": [],
"merge_log": [],
"placed": null,
"relationships": null,
"skill_id": "pivot-tables",
"split_log": [],
"typed": null,
"warnings": []
},
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [],
"input_skill": "VBA",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": {
"derived": {
"category": "Programming Languages",
"skill_nature": "LANGUAGE",
"sub_category": "general",
"typical_lifespan": "MULTI_YEAR",
"version_strategy": "UNVERSIONED",
"volatility": "MEDIUM"
},
"enrichment": null,
"keep_log": [],
"locked_dimensions": [],
"merge_log": [],
"placed": null,
"relationships": null,
"skill_id": "vba",
"split_log": [],
"typed": null,
"warnings": []
},
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [],
"input_skill": "Visual Basic",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": {
"derived": {
"category": "Programming Languages",
"skill_nature": "LANGUAGE",
"sub_category": "general",
"typical_lifespan": "MULTI_YEAR",
"version_strategy": "UNVERSIONED",
"volatility": "MEDIUM"
},
"enrichment": null,
"keep_log": [],
"locked_dimensions": [],
"merge_log": [],
"placed": null,
"relationships": null,
"skill_id": "visual-basic",
"split_log": [],
"typed": null,
"warnings": []
},
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [
{
"alias_text": "PowerShell",
"alias_type": "CANONICAL",
"id": 583,
"is_primary": true,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "PowerShell 5",
"alias_type": "VERSION",
"id": 585,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "PowerShell 5.1",
"alias_type": "VERSION",
"id": 588,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "PowerShell 6",
"alias_type": "VERSION",
"id": 586,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "PowerShell 7",
"alias_type": "VERSION",
"id": 587,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "PowerShell 7.x",
"alias_type": "VERSION",
"id": 589,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "PowerShell Core",
"alias_type": "VERSION",
"id": 590,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Windows PowerShell",
"alias_type": "VERSION",
"id": 591,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "powershell 7",
"alias_type": "VERSION",
"id": 2400,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "powershell 7.x",
"alias_type": "VERSION",
"id": 2401,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "powershell core",
"alias_type": "VERSION",
"id": 2402,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "ps 7",
"alias_type": "VERSION",
"id": 2398,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "pwsh",
"alias_type": "VERSION",
"id": 584,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
}
],
"canonical": {
"category_id": 6,
"display_name": "PowerShell",
"id": 297,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "LANGUAGE",
"slug": "powershell",
"sub_category_id": 38,
"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"dimensions": [
{
"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"
},
"input_skill": "PowerShell",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Cyber Security Engineer",
"id": 5,
"rationale": null,
"role_archetype": null,
"slug": "cybersecurity-engineer",
"source": "db"
}
]
},
{
"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"
},
"input_skill": "PowerShell",
"llm_role": null,
"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"
}
]
}
],
"input_skill": "PowerShell",
"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": "BigQuery",
"alias_type": "CANONICAL",
"id": 300,
"is_primary": true,
"match_strategy": "CASE_INSENSITIVE"
}
],
"canonical": {
"category_id": 11,
"display_name": "BigQuery",
"id": 106,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "CLOUD_SERVICE",
"slug": "bigquery",
"sub_category_id": 118,
"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"dimensions": [
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Cloud Data Warehouses",
"id": 22,
"rationale": "Managed analytical storage and compute platforms used for curated datasets, reporting, and downstream analytics. These systems are central to data modeling, performance tuning, and cost-aware query design.",
"slug": "cloud-data-warehouses",
"source": "db"
},
"input_skill": "Google BigQuery",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Data Engineer",
"id": 2,
"rationale": null,
"role_archetype": null,
"slug": "data-engineer",
"source": "db"
}
]
}
],
"input_skill": "Google BigQuery",
"matched_via": "embedding_alias",
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": null,
"source_tag": "db",
"was_in_llm_skills": true
},
{
"aliases_in_db": [
{
"alias_text": "SQL Server",
"alias_type": "CANONICAL",
"id": 135,
"is_primary": true,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "SQL Server 2000",
"alias_type": "VERSION",
"id": 138,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "SQL Server 2005",
"alias_type": "VERSION",
"id": 139,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "SQL Server 2008",
"alias_type": "VERSION",
"id": 140,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "SQL Server 2012",
"alias_type": "VERSION",
"id": 141,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "SQL Server 2014",
"alias_type": "VERSION",
"id": 142,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "SQL Server 2016",
"alias_type": "VERSION",
"id": 143,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "SQL Server 2017",
"alias_type": "VERSION",
"id": 144,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "SQL Server 2019",
"alias_type": "VERSION",
"id": 145,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "SQL Server 2022",
"alias_type": "VERSION",
"id": 146,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "SQL Server 6.5",
"alias_type": "VERSION",
"id": 136,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "SQL Server 7.0",
"alias_type": "VERSION",
"id": 137,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
}
],
"canonical": {
"category_id": 3,
"display_name": "SQL Server",
"id": 18,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "TOOL",
"slug": "sql-server",
"sub_category_id": 29,
"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"dimensions": [
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Relational Database Design",
"id": 4,
"rationale": "Modeling and operating relational persistence for backend services. Includes schema design, normalization, indexing, transactions, and query tuning for operational data stores.",
"slug": "relational-database-design",
"source": "db"
},
"input_skill": "MS SQL Server",
"llm_role": null,
"roles_from_db": [
{
"display_name": ".NET Backend Developer",
"id": 83,
"rationale": null,
"role_archetype": "Engineering",
"slug": "dotnet-backend-developer",
"source": "db"
},
{
"display_name": "Backend Developer",
"id": 1,
"rationale": null,
"role_archetype": "A Backend Engineer designs, builds, and maintains the server-side logic and data handling that power applications and services. They focus on implementing reliable business functionality, integrating with other systems, and ensuring the backend is scalable, maintainable, and observable.",
"slug": "backend-engineer",
"source": "db"
},
{
"display_name": "Kotlin Backend Developer",
"id": 84,
"rationale": null,
"role_archetype": "Engineering",
"slug": "kotlin-server-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": "Python Backend Developer",
"id": 80,
"rationale": null,
"role_archetype": "Engineering",
"slug": "python-backend-developer",
"source": "db"
},
{
"display_name": "Ruby Backend Developer",
"id": 85,
"rationale": null,
"role_archetype": "Engineering",
"slug": "ruby-backend-developer",
"source": "db"
},
{
"display_name": "Scala Backend Developer",
"id": 87,
"rationale": null,
"role_archetype": "Engineering",
"slug": "scala-backend-developer",
"source": "db"
}
]
}
],
"input_skill": "MS SQL Server",
"matched_via": "embedding_alias",
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": null,
"source_tag": "db",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [],
"input_skill": "SSIS",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": {
"derived": {
"category": "Data Engineering Tools",
"skill_nature": "TOOL",
"sub_category": "general",
"typical_lifespan": "MULTI_YEAR",
"version_strategy": "UNVERSIONED",
"volatility": "MEDIUM"
},
"enrichment": null,
"keep_log": [],
"locked_dimensions": [],
"merge_log": [],
"placed": null,
"relationships": null,
"skill_id": "ssis",
"split_log": [],
"typed": null,
"warnings": []
},
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [],
"input_skill": "T-SQL",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": {
"derived": {
"category": "Programming Languages",
"skill_nature": "LANGUAGE",
"sub_category": "general",
"typical_lifespan": "MULTI_YEAR",
"version_strategy": "UNVERSIONED",
"volatility": "MEDIUM"
},
"enrichment": null,
"keep_log": [],
"locked_dimensions": [],
"merge_log": [],
"placed": null,
"relationships": null,
"skill_id": "t-sql",
"split_log": [],
"typed": null,
"warnings": []
},
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [],
"input_skill": "Linked Server",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": {
"derived": {
"category": "Databases",
"skill_nature": "CONCEPT",
"sub_category": "general",
"typical_lifespan": "MULTI_YEAR",
"version_strategy": "UNVERSIONED",
"volatility": "MEDIUM"
},
"enrichment": null,
"keep_log": [],
"locked_dimensions": [],
"merge_log": [],
"placed": null,
"relationships": null,
"skill_id": "linked-server",
"split_log": [],
"typed": null,
"warnings": []
},
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [],
"input_skill": "MicroStrategy",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": {
"derived": {
"category": "Data Engineering Tools",
"skill_nature": "TOOL",
"sub_category": "general",
"typical_lifespan": "MULTI_YEAR",
"version_strategy": "UNVERSIONED",
"volatility": "MEDIUM"
},
"enrichment": null,
"keep_log": [],
"locked_dimensions": [],
"merge_log": [],
"placed": null,
"relationships": null,
"skill_id": "microstrategy",
"split_log": [],
"typed": null,
"warnings": []
},
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [],
"input_skill": "ETL",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": {
"derived": {
"category": "Data Engineering Tools",
"skill_nature": "PRACTICE",
"sub_category": "general",
"typical_lifespan": "MULTI_YEAR",
"version_strategy": "UNVERSIONED",
"volatility": "MEDIUM"
},
"enrichment": null,
"keep_log": [],
"locked_dimensions": [],
"merge_log": [],
"placed": null,
"relationships": null,
"skill_id": "etl",
"split_log": [],
"typed": null,
"warnings": []
},
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [],
"input_skill": "Data Visualization",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": {
"derived": {
"category": "Data Engineering Tools",
"skill_nature": "PRACTICE",
"sub_category": "general",
"typical_lifespan": "MULTI_YEAR",
"version_strategy": "UNVERSIONED",
"volatility": "MEDIUM"
},
"enrichment": null,
"keep_log": [],
"locked_dimensions": [],
"merge_log": [],
"placed": null,
"relationships": null,
"skill_id": "data-visualization",
"split_log": [],
"typed": null,
"warnings": []
},
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [],
"input_skill": "Exploratory Data Analysis",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": {
"derived": {
"category": "Data Engineering Tools",
"skill_nature": "PRACTICE",
"sub_category": "general",
"typical_lifespan": "MULTI_YEAR",
"version_strategy": "UNVERSIONED",
"volatility": "MEDIUM"
},
"enrichment": null,
"keep_log": [],
"locked_dimensions": [],
"merge_log": [],
"placed": null,
"relationships": null,
"skill_id": "exploratory-data-analysis",
"split_log": [],
"typed": null,
"warnings": []
},
"source_tag": "llm",
"was_in_llm_skills": true
}
],
"unmatched_skills": [
"NumPy",
"pandas",
"Excel",
"RStudio",
"Jupyter Notebook",
"Regex",
"Netezza",
"ODBC",
"Pivot Tables",
"VBA",
"Visual Basic",
"SSIS",
"T-SQL",
"Linked Server",
"MicroStrategy",
"ETL",
"Data Visualization",
"Exploratory Data Analysis"
]
}
API 3 — final-role-output
{
"chosen_role": {
"display_name": "Data Engineer",
"id": 2,
"rationale": "Multi-alias tie (3 roles at 1.0) resolved by TIER_B_TITLE: Data Engineer",
"role_archetype": null,
"slug": "data-engineer",
"source": "db"
},
"chosen_role_resolution": "in_db",
"final_input_skills": [
{
"skill": "Python",
"tag": "in_db"
},
{
"skill": "SQL",
"tag": "in_db"
},
{
"skill": "NumPy",
"tag": "new"
},
{
"skill": "pandas",
"tag": "new"
},
{
"skill": "Excel",
"tag": "new"
},
{
"skill": "R",
"tag": "in_db"
},
{
"skill": "RStudio",
"tag": "new"
},
{
"skill": "Jupyter Notebook",
"tag": "new"
},
{
"skill": "Regex",
"tag": "new"
},
{
"skill": "JSON",
"tag": "in_db"
},
{
"skill": "Netezza",
"tag": "new"
},
{
"skill": "ODBC",
"tag": "new"
},
{
"skill": "Pivot Tables",
"tag": "new"
},
{
"skill": "VBA",
"tag": "new"
},
{
"skill": "Visual Basic",
"tag": "new"
},
{
"skill": "PowerShell",
"tag": "in_db"
},
{
"skill": "Google BigQuery",
"tag": "in_db"
},
{
"skill": "MS SQL Server",
"tag": "in_db"
},
{
"skill": "SSIS",
"tag": "new"
},
{
"skill": "T-SQL",
"tag": "new"
},
{
"skill": "Linked Server",
"tag": "new"
},
{
"skill": "MicroStrategy",
"tag": "new"
},
{
"skill": "ETL",
"tag": "new"
},
{
"skill": "Data Visualization",
"tag": "new"
},
{
"skill": "Exploratory Data Analysis",
"tag": "new"
}
],
"llm_cost_api1_usd": null,
"llm_cost_api2_usd": null,
"llm_cost_api3_usd": null,
"llm_cost_total_usd": null,
"persistence": {
"items": [
{
"chosen_role_id": 2,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Cloud 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": 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": 2,
"dimension": {
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"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,
"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
},
{
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"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": true,
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"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": 5,
"skill_tag": "in_db",
"skipped_reason": null
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{
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"dimension": {
"difficulty_hint": "well_known",
"display_name": "Programming Languages for ML Systems",
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"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",
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"role_archetype": null,
"slug": "ml-ops-engineer",
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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",
"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",
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"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": 2,
"dimension": {
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"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"
}
],
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"skill_tag": "in_db",
"skipped_reason": null
},
{
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"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,
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"skill_tag": "in_db",
"skipped_reason": null
},
{
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"display_name": "Programming Languages \u0026 DSLs",
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"slug": "programming-languages-dsls",
"source": "db"
},
"dimension_id": 475,
"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": [
{
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"rationale": null,
"role_archetype": null,
"slug": "engineering-manager",
"source": "db"
}
],
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"skill_tag": "in_db",
"skipped_reason": null
},
{
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"display_name": "Programming Languages for Data Work",
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"source": "db"
},
"dimension_id": 21,
"input_skill": "SQL",
"llm_role": null,
"matched_chosen_role": true,
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"role_dimension_saved": true,
"roles_from_db": [
{
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"role_archetype": null,
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}
],
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},
{
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"display_name": "Programming Languages for ML Systems",
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"source": "db"
},
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"input_skill": "R",
"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": 194,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 2,
"dimension": {
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"display_name": "API Integration and Data Fetching",
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"rationale": "Client-side integration with backend endpoints and third-party services, including request shaping, response handling, and synchronization with UI state. This is central to frontend work because most screens depend on remote data.",
"slug": "api-integration-and-data-fetching",
"source": "db"
},
"dimension_id": 127,
"input_skill": "JSON",
"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": "Angular Frontend Developer",
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"source": "db"
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{
"display_name": "Frontend Developer",
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"rationale": null,
"role_archetype": null,
"slug": "frontend-engineer",
"source": "db"
},
{
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"role_archetype": null,
"slug": "full-stack-engineer",
"source": "db"
},
{
"display_name": "React Frontend Developer",
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"rationale": null,
"role_archetype": "Engineering",
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"source": "db"
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{
"display_name": "Svelte Frontend Developer",
"id": 92,
"rationale": null,
"role_archetype": "Engineering",
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"source": "db"
},
{
"display_name": "Vue Frontend Developer",
"id": 91,
"rationale": null,
"role_archetype": "Engineering",
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"source": "db"
},
{
"display_name": "Web Developer",
"id": 25,
"rationale": null,
"role_archetype": null,
"slug": "web-developer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 1984,
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},
{
"chosen_role_id": 2,
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"slug": "api-interface-and-contract-design",
"source": "db"
},
"dimension_id": 289,
"input_skill": "JSON",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": ".NET Backend Developer",
"id": 83,
"rationale": null,
"role_archetype": "Engineering",
"slug": "dotnet-backend-developer",
"source": "db"
},
{
"display_name": "Go Backend Developer",
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"rationale": null,
"role_archetype": "Engineering",
"slug": "go-backend-developer",
"source": "db"
},
{
"display_name": "Kotlin Backend Developer",
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"rationale": null,
"role_archetype": "Engineering",
"slug": "kotlin-server-backend-developer",
"source": "db"
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{
"display_name": "Node.js Backend Developer",
"id": 82,
"rationale": null,
"role_archetype": "Engineering",
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"source": "db"
},
{
"display_name": "PHP Backend Developer",
"id": 86,
"rationale": null,
"role_archetype": "Engineering",
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"source": "db"
},
{
"display_name": "Python Backend Developer",
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"source": "db"
},
{
"display_name": "Ruby Backend Developer",
"id": 85,
"rationale": null,
"role_archetype": "Engineering",
"slug": "ruby-backend-developer",
"source": "db"
},
{
"display_name": "Scala Backend Developer",
"id": 87,
"rationale": null,
"role_archetype": "Engineering",
"slug": "scala-backend-developer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 1984,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 2,
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"display_name": "Integration Protocols \u0026 Standards",
"id": 271,
"rationale": "Standards and protocols for integrating Pega applications.",
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"source": "db"
},
"dimension_id": 271,
"input_skill": "JSON",
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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": "Pega Developer",
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"role_archetype": null,
"slug": "pega-developer",
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}
],
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},
{
"chosen_role_id": 2,
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"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.",
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"source": "db"
},
"dimension_id": 59,
"input_skill": "PowerShell",
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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": "Cyber Security Engineer",
"id": 5,
"rationale": null,
"role_archetype": null,
"slug": "cybersecurity-engineer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 297,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 2,
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"display_name": "Programming Languages for ML Systems",
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},
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"roles_from_db": [
{
"display_name": "ML Engineer",
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"role_archetype": null,
"slug": "ml-engineer",
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},
{
"display_name": "MLOps Engineer",
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"role_archetype": null,
"slug": "ml-ops-engineer",
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}
],
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},
{
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"source": "db"
},
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{
"display_name": "Data Engineer",
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}
],
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{
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},
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"input_skill": "MS SQL Server",
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"roles_from_db": [
{
"display_name": ".NET Backend Developer",
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},
{
"display_name": "Backend Developer",
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"rationale": null,
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"slug": "backend-engineer",
"source": "db"
},
{
"display_name": "Kotlin Backend Developer",
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"role_archetype": "Engineering",
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},
{
"display_name": "Node.js Backend Developer",
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"rationale": null,
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},
{
"display_name": "Python Backend Developer",
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"rationale": null,
"role_archetype": "Engineering",
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"source": "db"
},
{
"display_name": "Ruby Backend Developer",
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"rationale": null,
"role_archetype": "Engineering",
"slug": "ruby-backend-developer",
"source": "db"
},
{
"display_name": "Scala Backend Developer",
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"role_archetype": "Engineering",
"slug": "scala-backend-developer",
"source": "db"
}
],
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"skill_tag": "new",
"skipped_reason": "skill_not_in_db_v3_proposed"
}
],
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"skill_dimension_saved": 0,
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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.