Pipeline run
801b594a-6d58-485e-b531-6c4524cf7a55
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
ETL / ELT Developer
domain · Data Engineering & Analytics CASE DOMAINslug: etl-elt-developer · id: 50 · source: db
Domain=Data Engineering & Analytics; The JD is centered on building and maintaining ETL solutions, specifically Informatica and Datastage workflows/mappings, which best matches an ETL / ELT Developer.
Matched skills
Matched dimensions
Matched KRAs
Resolution:
in_db
— role exists in library; skill↔dim and role↔dim links saved when applicable.
Job description
Note:- Minimum 5 years of experience required About Company: Truelancer Enterprise gig work platform where we take up on-the-ground or digital recurring work from enterprises and get it done through a million gig workers across 200+ cities in India. Our objective is to simplify the execution of different complex processes involving managing teams of multiple people at different hierarchical levels and enable millions of Gig workers to perform their task efficiently. Skills:- • ETL Job Description • No less than 4 years IT experience in Design ETL solutions and data conversion strategies and solutions for multiple disparate systems • Create an end-to-end solution for ETL transformational jobs that involve writing workflows and mappings, and creating database Packages, procedures and functions • Develop, maintain, and enhance Informatica Mappings, Work-flows, and processes • Support proper functioning and performance tuning. • Translate business requirements into functional/technical specifications • Designing and coding Datastage packages • Prepare root cause analysis documents, conduct code review, and test and tune the developed jobs • Provide inputs to testing strategy, configuration, deployment, hardware/software requirement • Identify avenues to improve project delivery parameters (eg productivity, efficiency, process, security etc) by leveraging tools, automation • Understand various technical tools used in the project (third party homegrown) to improve efficiency , productivity. Location- Remote
Skills from this JD
Each row merges API 1 extraction, API 2 library match / v3 orchestration (dimensions + locked dims), and API 3 persistence tags.
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
Aliases — catalog
- Informatica (CANONICAL) primary
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Platform
- Sub-category
- Data Integration Platform
- Vendor
- Informatica
- License
- proprietary
- Year introduced
- 1993
- Confidence
- 0.90
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Informatica appears frequently in enterprise data-integration and ETL job postings, especially alongside cloud migration and MDM roles; it remains a common hiring keyword rather than a sunset technology.
Skill profile (library / DB)
- Skill nature
- PLATFORM
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 9
- Sub-category id
- 114
- 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 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
- ETL Tools
- Skill nature
- TOOL
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Aliases — catalog
- SQL (CANONICAL) primary
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Language
- Sub-category
- Query Language
- Vendor
- ANSI
- License
- unknown
- Year introduced
- 1974
- Confidence
- 0.99
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: SQL appears in a large share of data, backend, and analytics job descriptions and remains the default query language for PostgreSQL, MySQL, and cloud warehouses like Snowflake/BigQuery.
Skill profile (library / DB)
- Skill nature
- LANGUAGE
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 6
- Sub-category id
- 97
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
Pega Programming Languages & DSLs Catalog dimension db id 267
Library dimension (catalog)
Roles linked in library: Pega Developer
-
Programming Languages & 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 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
- Databases
- Sub-category
- general
- Skill nature
- CONCEPT
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Version strategy
- UNVERSIONED
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Databases
- Sub-category
- general
- Skill nature
- CONCEPT
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Version strategy
- UNVERSIONED
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Automation 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
- CONCEPT
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Aliases — catalog
- Code Review (CANONICAL)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- SoftSkill
- Sub-category
- Code Review
- Confidence
- 0.96
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Code review is a standard hiring-pipeline requirement in engineering JDs and is built into major platforms like GitHub/GitLab pull-request workflows, indicating broad adoption.
Skill profile (library / DB)
- Skill nature
- PRACTICE
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 58
- Sub-category id
- 364
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
React Frontend Development Catalog dimension db id 96
Library dimension (catalog)
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
React Frontend Development
d_init_01
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Testing Tools
- Sub-category
- general
- Skill nature
- PRACTICE
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Aliases — catalog
- query tuning (CANONICAL) primary
- Query Tuning (CANONICAL)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Concept
- Sub-category
- Query Optimization
- Confidence
- 0.82
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Common in DB/analytics job descriptions and vendor docs; PostgreSQL, MySQL, SQL Server, and Oracle all expose EXPLAIN/ANALYZE and tuning guides, showing broad hiring-pipeline demand.
Skill profile (library / DB)
- Skill nature
- CONCEPT
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 2
- Sub-category id
- 3067
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
Relational Database Design Catalog dimension db id 4
Library dimension (catalog)
Roles linked in library: .NET Backend Developer, Backend Developer, Kotlin Backend Developer, Node.js Backend Developer, Python Backend Developer, Ruby Backend Developer, Scala Backend Developer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Relational Database Design
relational-database-design
|
— | — |
Skipped — no persistable v3 meta for new skill
skill_not_in_db_v3_proposed
|
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Automation 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 |
|---|---|---|---|---|---|---|
| Informatica | in_db |
ETL and ELT Tooling
etl-and-elt-tooling
|
✓ | — | 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 skipped (dimension not under chosen role) | |
| Code Review | in_db |
React Frontend Development
d_init_01
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Performance Tuning | new |
Relational Database Design
relational-database-design
|
— | — | Skipped — no persistable v3 meta for new skill | skill_not_in_db_v3_proposed |
Library artifacts (this run)
| Kind | Detail | DB id |
|---|---|---|
| canonical_skill_proposed | ETL | type=Data Engineering Tools subtype=general nature=PRACTICE lifespan=MULTI_YEAR | |
| canonical_skill_proposed | DataStage | type=Data Engineering Tools subtype=ETL Tools nature=TOOL lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Database Packages | type=Databases subtype=general nature=TOOL lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Stored Procedures | type=Databases subtype=general nature=CONCEPT lifespan=EVERGREEN | |
| canonical_skill_proposed | Functions | type=Databases subtype=general nature=CONCEPT lifespan=EVERGREEN | |
| canonical_skill_proposed | Workflows | type=Automation Tools subtype=general nature=PRACTICE lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Mappings | type=Data Engineering Tools subtype=general nature=CONCEPT lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Testing | type=Testing Tools subtype=general nature=PRACTICE lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Automation | type=Automation Tools subtype=general nature=PRACTICE lifespan=MULTI_YEAR | |
| dimension_skill_link_proposed | Performance Tuning ↔ Relational Database Design |
nano JD Parser — gpt-4.1-nano click to toggle
Show raw JSON
{
"JD_type": "fail",
"archetype_override_applied": true,
"archetype_override_matched_skills": [
"Informatica",
"Code Review",
"Location",
"Task",
"workers",
"root cause analysis"
],
"role_archetype": "Engineering"
}
API 1 — extract-from-jd click to toggle
{
"final_skills": [
{
"is_primary": true,
"skill_name": "ETL"
},
{
"is_primary": true,
"skill_name": "Informatica"
},
{
"is_primary": true,
"skill_name": "DataStage"
},
{
"is_primary": true,
"skill_name": "SQL"
},
{
"is_primary": true,
"skill_name": "Database Packages"
},
{
"is_primary": true,
"skill_name": "Stored Procedures"
},
{
"is_primary": true,
"skill_name": "Functions"
},
{
"is_primary": true,
"skill_name": "Workflows"
},
{
"is_primary": true,
"skill_name": "Mappings"
},
{
"is_primary": false,
"skill_name": "Code Review"
},
{
"is_primary": false,
"skill_name": "Testing"
},
{
"is_primary": false,
"skill_name": "Performance Tuning"
},
{
"is_primary": false,
"skill_name": "Automation"
}
],
"jd_role": null,
"nano_parsed": {
"JD_type": "fail",
"archetype_override_applied": true,
"archetype_override_matched_skills": [
"Informatica",
"Code Review",
"Location",
"Task",
"workers",
"root cause analysis"
],
"role_archetype": "Engineering"
},
"rejected": false,
"rejection_reason": null,
"run_id": "801b594a-6d58-485e-b531-6c4524cf7a55",
"stage3_signals": {
"alias_found": false,
"alias_match_roles": [],
"kra_match_roles": [
{
"display_name": "Flutter Developer",
"kra_matches": [
{
"kra_text": "translate product and design requirements",
"sentence": "Translate business requirements into functional/technical specifications",
"similarity": 0.7176
},
{
"kra_text": "optimize responsiveness and performance",
"sentence": "Support proper functioning and performance tuning.",
"similarity": 0.5427
},
{
"kra_text": "collaborate with design, product, and backend teams",
"sentence": "Prepare root cause analysis documents, conduct code review, and test and tune the developed jobs",
"similarity": 0.4216
}
],
"matched_count": null,
"matched_skills": null,
"role_id": 74,
"score": 0.5606,
"slug": "flutter-developer",
"total_count": null
},
{
"display_name": "Scala Backend Developer",
"kra_matches": [
{
"kra_text": "performance and reliability tuning",
"sentence": "Support proper functioning and performance tuning.",
"similarity": 0.6528
},
{
"kra_text": "performance and reliability tuning",
"sentence": "Prepare root cause analysis documents, conduct code review, and test and tune the developed jobs",
"similarity": 0.4925
},
{
"kra_text": "backend workflow orchestration",
"sentence": "Our objective is to simplify the execution of different complex processes involving managing teams of multiple people at different hierarchical levels and enable millions of Gig workers to perform their task efficiently.",
"similarity": 0.4704
}
],
"matched_count": null,
"matched_skills": null,
"role_id": 87,
"score": 0.5386,
"slug": "scala-backend-developer",
"total_count": null
},
{
"display_name": "Go Backend Developer",
"kra_matches": [
{
"kra_text": "performance tuning and resource efficiency",
"sentence": "Support proper functioning and performance tuning.",
"similarity": 0.5925
},
{
"kra_text": "code review and testing support",
"sentence": "Prepare root cause analysis documents, conduct code review, and test and tune the developed jobs",
"similarity": 0.5718
},
{
"kra_text": "performance tuning and resource efficiency",
"sentence": "Understand various technical tools used in the project (third party homegrown) to improve efficiency , productivity.",
"similarity": 0.4428
}
],
"matched_count": null,
"matched_skills": null,
"role_id": 81,
"score": 0.5357,
"slug": "go-backend-developer",
"total_count": null
},
{
"display_name": "Kotlin Backend Developer",
"kra_matches": [
{
"kra_text": "performance and reliability tuning",
"sentence": "Support proper functioning and performance tuning.",
"similarity": 0.6528
},
{
"kra_text": "performance and reliability tuning",
"sentence": "Prepare root cause analysis documents, conduct code review, and test and tune the developed jobs",
"similarity": 0.4925
},
{
"kra_text": "Backend business logic implementation",
"sentence": "Translate business requirements into functional/technical specifications",
"similarity": 0.439
}
],
"matched_count": null,
"matched_skills": null,
"role_id": 84,
"score": 0.5281,
"slug": "kotlin-server-backend-developer",
"total_count": null
},
{
"display_name": "Java Backend Developer",
"kra_matches": [
{
"kra_text": "backend performance tuning",
"sentence": "Support proper functioning and performance tuning.",
"similarity": 0.6262
},
{
"kra_text": "code refactoring and defect fixes",
"sentence": "Prepare root cause analysis documents, conduct code review, and test and tune the developed jobs",
"similarity": 0.5203
},
{
"kra_text": "asynchronous job processing",
"sentence": "Our objective is to simplify the execution of different complex processes involving managing teams of multiple people at different hierarchical levels and enable millions of Gig workers to perform their task efficiently.",
"similarity": 0.4233
}
],
"matched_count": null,
"matched_skills": null,
"role_id": 79,
"score": 0.5233,
"slug": "java-backend-developer",
"total_count": null
}
],
"skill_match_roles": [
{
"display_name": "Data Engineer",
"kra_matches": null,
"matched_count": 2,
"matched_skills": [
"Informatica",
"SQL"
],
"role_id": 2,
"score": 0.2222,
"slug": "data-engineer",
"total_count": 9
},
{
"display_name": "Pega Developer",
"kra_matches": null,
"matched_count": 1,
"matched_skills": [
"SQL"
],
"role_id": 24,
"score": 0.1111,
"slug": "pega-developer",
"total_count": 9
},
{
"display_name": "Engineering Manager",
"kra_matches": null,
"matched_count": 1,
"matched_skills": [
"SQL"
],
"role_id": 121,
"score": 0.1111,
"slug": "engineering-manager",
"total_count": 9
}
]
},
"stage4_decision": {
"alias_collision_detected": false,
"case": "DOMAIN",
"chosen_role": {
"display_name": "ETL / ELT Developer",
"kra_matches": null,
"matched_count": null,
"matched_skills": null,
"role_id": 50,
"score": 0.98,
"slug": "etl-elt-developer",
"total_count": null
},
"confidence": 0.98,
"is_new_role": false,
"llm2_fired": false,
"llm2_reasoning": null,
"matched_dimensions": [
"ETL Solution Design",
"Data Conversion Strategy",
"Workflow and Mapping Development",
"Performance Tuning",
"Technical Requirements Translation",
"Testing and Deployment Support",
"Process and Automation Improvement"
],
"matched_kras": [
"Design ETL solutions and data conversion strategies",
"Create an end-to-end solution for ETL transformational jobs",
"Develop, maintain, and enhance Informatica Mappings, Work-flows, and processes",
"Support proper functioning and performance tuning",
"Translate business requirements into functional/technical specifications",
"Designing and coding Datastage packages",
"Prepare root cause analysis documents",
"Conduct code review, and test and tune the developed jobs",
"Provide inputs to testing strategy, configuration, deployment"
],
"matched_skills": [
"ETL",
"Informatica",
"Datastage",
"workflows",
"mappings",
"database Packages",
"procedures",
"functions",
"performance tuning",
"root cause analysis",
"code review",
"testing"
],
"new_role_display_name": null,
"new_role_slug": null,
"queued": false,
"reasoning": "Domain=Data Engineering \u0026 Analytics; The JD is centered on building and maintaining ETL solutions, specifically Informatica and Datastage workflows/mappings, which best matches an ETL / ELT Developer.",
"sub_role": null
},
"stage5_updates": {
"centroid_n_after": 9,
"centroid_updated": true,
"collision_log_id": null,
"new_kra_attached": null,
"new_skills_attached": [
{
"is_primary": true,
"queue_id": 13837,
"role_display_name": "ETL / ELT Developer",
"role_slug": "etl-elt-developer",
"skill_name": "ETL",
"status": "pending"
},
{
"is_primary": true,
"queue_id": 13838,
"role_display_name": "ETL / ELT Developer",
"role_slug": "etl-elt-developer",
"skill_name": "DataStage",
"status": "pending"
},
{
"is_primary": true,
"queue_id": 13839,
"role_display_name": "ETL / ELT Developer",
"role_slug": "etl-elt-developer",
"skill_name": "Database Packages",
"status": "pending"
},
{
"is_primary": true,
"queue_id": 13840,
"role_display_name": "ETL / ELT Developer",
"role_slug": "etl-elt-developer",
"skill_name": "Stored Procedures",
"status": "pending"
},
{
"is_primary": true,
"queue_id": 13841,
"role_display_name": "ETL / ELT Developer",
"role_slug": "etl-elt-developer",
"skill_name": "Functions",
"status": "pending"
},
{
"is_primary": true,
"queue_id": 13842,
"role_display_name": "ETL / ELT Developer",
"role_slug": "etl-elt-developer",
"skill_name": "Workflows",
"status": "pending"
},
{
"is_primary": true,
"queue_id": 13843,
"role_display_name": "ETL / ELT Developer",
"role_slug": "etl-elt-developer",
"skill_name": "Mappings",
"status": "pending"
},
{
"is_primary": false,
"queue_id": 13844,
"role_display_name": "ETL / ELT Developer",
"role_slug": "etl-elt-developer",
"skill_name": "Testing",
"status": "pending"
},
{
"is_primary": false,
"queue_id": 13845,
"role_display_name": "ETL / ELT Developer",
"role_slug": "etl-elt-developer",
"skill_name": "Performance Tuning",
"status": "pending"
},
{
"is_primary": false,
"queue_id": 13846,
"role_display_name": "ETL / ELT Developer",
"role_slug": "etl-elt-developer",
"skill_name": "Automation",
"status": "pending"
}
],
"queue_entry_id": null,
"v3_pipeline_triggered": false,
"v3_role_slug": null,
"v3_run_id": null
}
}
API 2 — extract-details
{
"alias_matches": [
{
"alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
"alias_persisted": false,
"existing_alias_id": 311,
"existing_alias_text": "Informatica",
"input_term": "Informatica",
"matched_canonical": {
"category_id": 9,
"display_name": "Informatica",
"id": 117,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "PLATFORM",
"slug": "informatica",
"sub_category_id": 114,
"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": 864,
"existing_alias_text": "Code Review",
"input_term": "Code Review",
"matched_canonical": {
"category_id": 58,
"display_name": "Code Review",
"id": 516,
"is_also_category": false,
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"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [],
"input_skill": "Automation",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": {
"derived": {
"category": "Automation 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": "automation",
"split_log": [],
"typed": null,
"warnings": []
},
"source_tag": "llm",
"was_in_llm_skills": true
}
],
"unmatched_skills": [
"ETL",
"DataStage",
"Database Packages",
"Stored Procedures",
"Functions",
"Workflows",
"Mappings",
"Testing",
"Automation"
]
}
API 3 — final-role-output
{
"chosen_role": {
"display_name": "ETL / ELT Developer",
"id": 50,
"rationale": "Domain=Data Engineering \u0026 Analytics; The JD is centered on building and maintaining ETL solutions, specifically Informatica and Datastage workflows/mappings, which best matches an ETL / ELT Developer.",
"role_archetype": "Data",
"slug": "etl-elt-developer",
"source": "db"
},
"chosen_role_resolution": "in_db",
"final_input_skills": [
{
"skill": "ETL",
"tag": "new"
},
{
"skill": "Informatica",
"tag": "in_db"
},
{
"skill": "DataStage",
"tag": "new"
},
{
"skill": "SQL",
"tag": "in_db"
},
{
"skill": "Database Packages",
"tag": "new"
},
{
"skill": "Stored Procedures",
"tag": "new"
},
{
"skill": "Functions",
"tag": "new"
},
{
"skill": "Workflows",
"tag": "new"
},
{
"skill": "Mappings",
"tag": "new"
},
{
"skill": "Code Review",
"tag": "in_db"
},
{
"skill": "Testing",
"tag": "new"
},
{
"skill": "Performance Tuning",
"tag": "in_db"
},
{
"skill": "Automation",
"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": 50,
"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": "Informatica",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "Data Engineer",
"id": 2,
"rationale": null,
"role_archetype": null,
"slug": "data-engineer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 117,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 50,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Pega Programming Languages \u0026 DSLs",
"id": 267,
"rationale": "Programming languages and domain-specific languages used in Pega development.",
"slug": "pega-programming-languages-dsls",
"source": "db"
},
"dimension_id": 267,
"input_skill": "SQL",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "Pega Developer",
"id": 24,
"rationale": null,
"role_archetype": null,
"slug": "pega-developer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 101,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 50,
"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": "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": "Engineering Manager",
"id": 121,
"rationale": null,
"role_archetype": null,
"slug": "engineering-manager",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 101,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 50,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Programming Languages for Data Work",
"id": 21,
"rationale": "Languages used to implement data pipelines, transformations, and operational glue. This is the primary coding surface for building ingestion, enrichment, and automation logic in data engineering.",
"slug": "programming-languages-for-data-work",
"source": "db"
},
"dimension_id": 21,
"input_skill": "SQL",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "Data Engineer",
"id": 2,
"rationale": null,
"role_archetype": null,
"slug": "data-engineer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 101,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 50,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "React Frontend Development",
"id": 96,
"rationale": "Building interactive web user interfaces with React.js, including component composition, state management, hooks, and rendering patterns. React.js belongs here because it is a core library for client-side UI development in modern web applications.",
"slug": "d_init_01",
"source": "db"
},
"dimension_id": 96,
"input_skill": "Code Review",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [],
"skill_dimension_saved": true,
"skill_id": 516,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 50,
"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"
},
"dimension_id": 4,
"input_skill": "Performance Tuning",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "Skipped \u2014 no persistable v3 meta for new skill",
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": ".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"
}
],
"skill_dimension_saved": false,
"skill_id": null,
"skill_tag": "new",
"skipped_reason": "skill_not_in_db_v3_proposed"
}
],
"new_skills_created": 0,
"role_dimension_saved": 0,
"skill_dimension_saved": 0,
"skipped": 1
},
"planner_output": null,
"run_id": "801b594a-6d58-485e-b531-6c4524cf7a55"
}
LLM Calls
Every model call made for this run, in pipeline order. Click a card to see the model's response.