Pipeline run
f3dba03c-7407-4d0f-9d31-56804513e8aa
Client output enrichment
v2 Skill cluster · Nature of work · AI index · Tech stack maturity · Evidence · KRA descriptionvocab breakdown (legacy)
Signals
Post-classification
Captured for admin review
1 POST /skills/extract-from-jd
2 POST /skills/extract-details
3 POST /skills/final-role-output
Data Engineer
CASE Aslug: data-engineer · id: 2 · source: db
Exact alias hit on data-engineer (1.0) — no other alias at this confidence; skill_top data-engineer 0.25 does not contradict
Resolution:
in_db
— role exists in library; skill↔dim and role↔dim links saved when applicable.
Job description
Warm Greetings from Quantzig! About Quantzig: Quantzig is a global analytics & advisory firm with offices in the US, UK, Canada, China, and India. we have assisted our clients across the globe with end-to-end advanced analytics, visual storyboarding, machine Learning and data engineering solutions implementation for prudent decision making. We are a rapidly growing organization that is built and operated by high-performance champions. If you have what it takes to be the champion with business and functional skills to take ownership of an entire project end-to-end, help build a team with great work ethic and a drive to learn, you are the one we’re looking for. The clients love us for our solutioning capability, our enthusiasm and we expect you to be a part of our growth story. Company Website: https://www.quantzig.com/ Quantzig is a Group Company of Infiniti Research Ltd https://www.infinitiresearch.com/ Job Description Designation: Senior Azure Data Engineer What do we look for? • 3-5 years of experience into Azure. • Design, develop, and maintain data pipelines and workflows to ingest, process, and deliver data efficiently and reliably. • Implement workflow automation solutions to streamline data processing, transformation, and integration tasks. • Collaborate with data stakeholders to gather and understand requirements for data workflows and automation processes. • Ensure data governance standards are met by implementing automation for data quality checks, data lineage, and metadata management. • Monitor and optimize data workflows for performance, scalability, and reliability. • Troubleshoot and resolve data pipeline issues promptly to minimize downtime and data loss. • Collaborate with data architects and data scientists to ensure data infrastructure meets analytical needs. • Develop and maintain documentation for data workflows, governance automation, and best practices. • Stay current with emerging technologies and industry trends in data engineering and governance. Additional Details • Mandatory Skill set - End to end Azure Services • Required Experience - Minimum 5 years. • Notice Period- Immediate to 30 Days • Job Location - Cessna Business Park, Kadubeesanahalli, Bangalore Note - Please DM me for more details. Mail ID - Madhuriv@infinitiresearch.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
- Azure (CANONICAL) primary
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Platform
- Sub-category
- Cloud Platform
- Vendor
- Microsoft
- License
- proprietary
- Year introduced
- 2010
- Confidence
- 0.99
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Azure is broadly adopted and frequently appears in cloud/platform job descriptions alongside AWS and GCP; Microsoft’s ongoing enterprise investment and Azure certification demand signal strong hiring-pipeline relevance.
Skill profile (library / DB)
- Skill nature
- PLATFORM
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 9
- Sub-category id
- 46
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
Cloud Platforms Catalog dimension db id 20
Library dimension (catalog)
Roles linked in library: .NET Backend Developer, Backend Developer, Cyber Security Engineer, Data Engineer, DevOps Engineer, Fullstack Developer, Go Backend Developer, Java Backend Developer, Kotlin Backend Developer, ML Engineer, MLOps Engineer, Node.js Backend Developer, Python Backend Developer, Scala Backend Developer
-
Cloud Platforms & Managed Services Catalog dimension db id 221
Library dimension (catalog)
Roles linked in library: Fullstack Developer, Go Backend Developer, Node.js Backend Developer
-
Cloud Platforms for AI Deployment Catalog dimension db id 211
Library dimension (catalog)
Roles linked in library: AI Engineer
-
Cloud Provider Platforms Catalog dimension db id 131
Library dimension (catalog)
Roles linked in library: Cloud Architect, Cloud Security Engineer
-
Cloud Security Posture Tools Catalog dimension db id 64
Library dimension (catalog)
Roles linked in library: Cloud Security Engineer, Cyber Security Engineer
-
Vendor Product Families Catalog dimension db id 477
Library dimension (catalog)
Roles linked in library: Engineering Manager
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Cloud Platforms
cloud-platforms
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved |
|
Cloud Platforms & Managed Services
cloud-platforms-managed-services
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Cloud Platforms for AI Deployment
cloud-platforms-for-ai-deployment
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Cloud Provider Platforms
cloud-provider-platforms
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Cloud Security Posture Tools
cloud-security-posture-tools
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Vendor Product Families
vendor-product-families
|
✓ | — | 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
- 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
- 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
- CONCEPT
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Aliases — catalog
- Metadata management (CANONICAL) primary
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Methodology
- Sub-category
- Metadata Management Methodology
- Confidence
- 0.92
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Common in data/platform JDs: roles for data governance, MDM, and cataloging routinely list metadata management alongside tools like Collibra/Alation and cloud data catalogs.
Skill profile (library / DB)
- Skill nature
- METHODOLOGY
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 8
- Sub-category id
- 109
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
Data Lineage and Metadata Catalog dimension db id 28
Library dimension (catalog)
Roles linked in library: Data Engineer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Data Lineage and Metadata
data-lineage-and-metadata
|
✓ | ✓ | 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
- 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
- CONCEPT
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
All API 3 persistence rows
Same grid as the skill-extractor “Persistence items” table: one row per (skill × dimension) work item.
| Skill | Tag | Dimension | Skill↔dim | Role↔dim | Outcome | Notes |
|---|---|---|---|---|---|---|
| Azure | in_db |
Cloud Platforms
cloud-platforms
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved | |
| Azure | in_db |
Cloud Platforms & Managed Services
cloud-platforms-managed-services
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Azure | in_db |
Cloud Platforms for AI Deployment
cloud-platforms-for-ai-deployment
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Azure | in_db |
Cloud Provider Platforms
cloud-provider-platforms
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Azure | in_db |
Cloud Security Posture Tools
cloud-security-posture-tools
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Azure | in_db |
Vendor Product Families
vendor-product-families
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Metadata Management | in_db |
Data Lineage and Metadata
data-lineage-and-metadata
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved |
Library artifacts (this run)
| Kind | Detail | DB id |
|---|---|---|
| canonical_skill_proposed | Data Pipelines | type=Data Engineering Tools subtype=general nature=PRACTICE lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Workflow Automation | type=Data Engineering Tools subtype=general nature=PRACTICE lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Data Quality | type=Data Engineering Tools subtype=general nature=CONCEPT lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Data Lineage | type=Data Engineering Tools subtype=general nature=CONCEPT lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Data Engineering | type=Data Engineering Tools subtype=general nature=CONCEPT lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Data Governance | type=Data Engineering Tools subtype=general nature=CONCEPT lifespan=MULTI_YEAR |
nano JD Parser — gpt-4.1-nano click to toggle
Show raw JSON
{
"JD_type": "pass",
"about_company": {
"source_marker": {
"first_5_words": "Quantzig is a global analytics",
"last_5_words": "part of our growth story."
},
"text": "Quantzig is a global analytics \u0026 advisory firm with offices in the US, UK, Canada, China, and India. we have assisted our clients across the globe with end-to-end advanced analytics, visual storyboarding, machine Learning and data engineering solutions implementation for prudent decision making. We are a rapidly growing organization that is built and operated by high-performance champions. If you have what it takes to be the champion with business and functional skills to take ownership of an entire project end-to-end, help build a team with great work ethic and a drive to learn, you are the one we\u2019re looking for. The clients love us for our solutioning capability, our enthusiasm and we expect you to be a part of our growth story.",
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"experience": {
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},
"job_locations": [
{
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],
"city": "Bangalore",
"country": "India",
"state": "Karnataka",
"work_mode": "onsite"
}
],
"role": "Senior Azure Data Engineer",
"role_aliases": [
"Azure Data Engineer",
"Data Engineer",
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],
"role_archetype": "Data",
"roles_and_responsibilities": [
{
"bullet_count": 10,
"heading": "What do we look for?",
"heading_was_present": true,
"source_marker": {
"first_5_words": "\u2022 3-5 years of experience into",
"last_5_words": "data engineering and governance."
},
"text": "\u2022 3-5 years of experience into Azure.\n\u2022 Design, develop, and maintain data pipelines and workflows to ingest, process, and deliver data efficiently and reliably.\n\u2022 Implement workflow automation solutions to streamline data processing, transformation, and integration tasks.\n\u2022 Collaborate with data stakeholders to gather and understand requirements for data workflows and automation processes.\n\u2022 Ensure data governance standards are met by implementing automation for data quality checks, data lineage, and metadata management.\n\u2022 Monitor and optimize data workflows for performance, scalability, and reliability.\n\u2022 Troubleshoot and resolve data pipeline issues promptly to minimize downtime and data loss.\n\u2022 Collaborate with data architects and data scientists to ensure data infrastructure meets analytical needs.\n\u2022 Develop and maintain documentation for data workflows, governance automation, and best practices.\n\u2022 Stay current with emerging technologies and industry trends in data engineering and governance.",
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},
{
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"heading": "Mandatory Skill set",
"heading_was_present": true,
"source_marker": {
"first_5_words": "\u2022 End to end Azure Services",
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},
"text": "\u2022 End to end Azure Services",
"word_count": 5
}
],
"urls": [
{
"type": "website",
"url": "https://www.quantzig.com/"
},
{
"type": "website",
"url": "https://www.infinitiresearch.com/"
}
]
}
API 1 — extract-from-jd click to toggle
{
"final_skills": [
{
"is_primary": true,
"skill_name": "Azure"
},
{
"is_primary": true,
"skill_name": "Data Pipelines"
},
{
"is_primary": true,
"skill_name": "Workflow Automation"
},
{
"is_primary": true,
"skill_name": "Data Quality"
},
{
"is_primary": true,
"skill_name": "Data Lineage"
},
{
"is_primary": true,
"skill_name": "Metadata Management"
},
{
"is_primary": true,
"skill_name": "Data Engineering"
},
{
"is_primary": true,
"skill_name": "Data Governance"
}
],
"jd_role": {
"display_name": "Senior Azure Data Engineer",
"rationale": null,
"role_aliases": [
"Azure Data Engineer",
"Data Engineer",
"Cloud Data Engineer"
],
"role_archetype": "Data",
"slug": ""
},
"nano_parsed": {
"JD_type": "pass",
"about_company": {
"source_marker": {
"first_5_words": "Quantzig is a global analytics",
"last_5_words": "part of our growth story."
},
"text": "Quantzig is a global analytics \u0026 advisory firm with offices in the US, UK, Canada, China, and India. we have assisted our clients across the globe with end-to-end advanced analytics, visual storyboarding, machine Learning and data engineering solutions implementation for prudent decision making. We are a rapidly growing organization that is built and operated by high-performance champions. If you have what it takes to be the champion with business and functional skills to take ownership of an entire project end-to-end, help build a team with great work ethic and a drive to learn, you are the one we\u2019re looking for. The clients love us for our solutioning capability, our enthusiasm and we expect you to be a part of our growth story.",
"word_count": 84
},
"certifications": [],
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],
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},
{
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"rejected": false,
"rejection_reason": null,
"run_id": "f3dba03c-7407-4d0f-9d31-56804513e8aa",
"stage3_signals": {
"alias_found": true,
"alias_match_roles": [
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"matched_skills": null,
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}
],
"kra_match_roles": [
{
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{
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"similarity": 0.6805
},
{
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"sentence": "Collaborate with data architects and data scientists to ensure data infrastructure meets analytical needs.",
"similarity": 0.6744
},
{
"kra_text": "Works with data analysts, data scientists, and business stakeholders to define data models, ingestion schedules, and data delivery requirements.",
"sentence": "Collaborate with data stakeholders to gather and understand requirements for data workflows and automation processes.",
"similarity": 0.6647
}
],
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"matched_skills": null,
"role_id": 2,
"score": 0.6732,
"slug": "data-engineer",
"total_count": null
},
{
"display_name": "DevOps Engineer",
"kra_matches": [
{
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"sentence": "Monitor and optimize data workflows for performance, scalability, and reliability.",
"similarity": 0.6345
},
{
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"sentence": "Troubleshoot and resolve data pipeline issues promptly to minimize downtime and data loss.",
"similarity": 0.5401
},
{
"kra_text": "Writes runbooks, deployment guides, incident playbooks, and infrastructure documentation to support operations and knowledge sharing.",
"sentence": "Develop and maintain documentation for data workflows, governance automation, and best practices.",
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}
],
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},
{
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{
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"sentence": "Monitor and optimize data workflows for performance, scalability, and reliability.",
"similarity": 0.6067
},
{
"kra_text": "Prepares, cleans, and transforms training datasets, manages feature stores, and builds feature engineering pipelines for model training.",
"sentence": "Design, develop, and maintain data pipelines and workflows to ingest, process, and deliver data efficiently and reliably.",
"similarity": 0.5319
},
{
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}
],
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},
{
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"kra_matches": [
{
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"sentence": "Monitor and optimize data workflows for performance, scalability, and reliability.",
"similarity": 0.5446
},
{
"kra_text": "Automates ML platform operations including scheduled retraining triggers, pipeline orchestration, evaluation workflows, and alerting configuration.",
"sentence": "Implement workflow automation solutions to streamline data processing, transformation, and integration tasks.",
"similarity": 0.5127
},
{
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"sentence": "Ensure data governance standards are met by implementing automation for data quality checks, data lineage, and metadata management.",
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],
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"role_id": 16,
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},
{
"display_name": "Scala Backend Developer",
"kra_matches": [
{
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"sentence": "Implement workflow automation solutions to streamline data processing, transformation, and integration tasks.",
"similarity": 0.5646
},
{
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"sentence": "Monitor and optimize data workflows for performance, scalability, and reliability.",
"similarity": 0.5416
},
{
"kra_text": "backend workflow orchestration",
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}
],
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"matched_skills": null,
"role_id": 87,
"score": 0.5086,
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}
],
"skill_match_roles": [
{
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"kra_matches": null,
"matched_count": 2,
"matched_skills": [
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],
"role_id": 2,
"score": 0.25,
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"total_count": 8
},
{
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"matched_skills": [
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},
{
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"matched_skills": [
"Azure"
],
"role_id": 5,
"score": 0.125,
"slug": "cybersecurity-engineer",
"total_count": 8
},
{
"display_name": "Cloud Architect",
"kra_matches": null,
"matched_count": 1,
"matched_skills": [
"Azure"
],
"role_id": 9,
"score": 0.125,
"slug": "cloud-architect",
"total_count": 8
},
{
"display_name": "Backend Developer",
"kra_matches": null,
"matched_count": 1,
"matched_skills": [
"Azure"
],
"role_id": 1,
"score": 0.125,
"slug": "backend-engineer",
"total_count": 8
}
]
},
"stage4_decision": {
"alias_collision_detected": false,
"case": "A",
"chosen_role": {
"display_name": "Data Engineer",
"kra_matches": null,
"matched_count": null,
"matched_skills": null,
"role_id": 2,
"score": 1.0,
"slug": "data-engineer",
"total_count": null
},
"confidence": 1.0,
"is_new_role": false,
"llm2_fired": false,
"llm2_reasoning": null,
"matched_dimensions": [],
"matched_kras": [],
"matched_skills": [],
"new_role_display_name": null,
"new_role_slug": null,
"queued": false,
"reasoning": "Exact alias hit on data-engineer (1.0) \u2014 no other alias at this confidence; skill_top data-engineer 0.25 does not contradict",
"sub_role": null
},
"stage5_updates": {
"centroid_n_after": 483,
"centroid_updated": true,
"collision_log_id": null,
"new_kra_attached": null,
"new_skills_attached": [
{
"is_primary": true,
"queue_id": 22688,
"role_display_name": "Data Engineer",
"role_slug": "data-engineer",
"skill_name": "Data Pipelines",
"status": "pending"
},
{
"is_primary": true,
"queue_id": 22689,
"role_display_name": "Data Engineer",
"role_slug": "data-engineer",
"skill_name": "Workflow Automation",
"status": "pending"
},
{
"is_primary": true,
"queue_id": 22690,
"role_display_name": "Data Engineer",
"role_slug": "data-engineer",
"skill_name": "Data Quality",
"status": "pending"
},
{
"is_primary": true,
"queue_id": 22691,
"role_display_name": "Data Engineer",
"role_slug": "data-engineer",
"skill_name": "Data Lineage",
"status": "pending"
},
{
"is_primary": true,
"queue_id": 22692,
"role_display_name": "Data Engineer",
"role_slug": "data-engineer",
"skill_name": "Data Engineering",
"status": "pending"
},
{
"is_primary": true,
"queue_id": 22693,
"role_display_name": "Data Engineer",
"role_slug": "data-engineer",
"skill_name": "Data Governance",
"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": 407,
"existing_alias_text": "Azure",
"input_term": "Azure",
"matched_canonical": {
"category_id": 9,
"display_name": "Azure",
"id": 188,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "PLATFORM",
"slug": "azure",
"sub_category_id": 46,
"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"matched_via": "alias"
},
{
"alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
"alias_persisted": false,
"existing_alias_id": 341,
"existing_alias_text": "Metadata management",
"input_term": "Metadata Management",
"matched_canonical": {
"category_id": 8,
"display_name": "Metadata management",
"id": 137,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "METHODOLOGY",
"slug": "metadata-management",
"sub_category_id": 109,
"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"matched_via": "alias"
}
],
"candidate_roles": [
{
"display_name": ".NET Backend Developer",
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"rationale": null,
"role_archetype": "Engineering",
"slug": "dotnet-backend-developer",
"source": "db"
},
{
"display_name": "Backend Developer",
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"rationale": null,
"role_archetype": "A Backend Engineer designs, builds, and maintains the server-side logic and data handling that power applications and services. They focus on implementing reliable business functionality, integrating with other systems, and ensuring the backend is scalable, maintainable, and observable.",
"slug": "backend-engineer",
"source": "db"
},
{
"display_name": "Cyber Security Engineer",
"id": 5,
"rationale": null,
"role_archetype": null,
"slug": "cybersecurity-engineer",
"source": "db"
},
{
"display_name": "Data Engineer",
"id": 2,
"rationale": null,
"role_archetype": null,
"slug": "data-engineer",
"source": "db"
},
{
"display_name": "DevOps Engineer",
"id": 10,
"rationale": null,
"role_archetype": null,
"slug": "devops-engineer",
"source": "db"
},
{
"display_name": "Fullstack Developer",
"id": 15,
"rationale": null,
"role_archetype": null,
"slug": "full-stack-engineer",
"source": "db"
},
{
"display_name": "Go Backend Developer",
"id": 81,
"rationale": null,
"role_archetype": "Engineering",
"slug": "go-backend-developer",
"source": "db"
},
{
"display_name": "Java Backend Developer",
"id": 79,
"rationale": null,
"role_archetype": "Engineering",
"slug": "java-backend-developer",
"source": "db"
},
{
"display_name": "Kotlin Backend Developer",
"id": 84,
"rationale": null,
"role_archetype": "Engineering",
"slug": "kotlin-server-backend-developer",
"source": "db"
},
{
"display_name": "ML Engineer",
"id": 3,
"rationale": null,
"role_archetype": null,
"slug": "ml-engineer",
"source": "db"
},
{
"display_name": "MLOps Engineer",
"id": 16,
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"role_archetype": null,
"slug": "ml-ops-engineer",
"source": "db"
},
{
"display_name": "Node.js Backend Developer",
"id": 82,
"rationale": null,
"role_archetype": "Engineering",
"slug": "node-backend-developer",
"source": "db"
},
{
"display_name": "Python Backend Developer",
"id": 80,
"rationale": null,
"role_archetype": "Engineering",
"slug": "python-backend-developer",
"source": "db"
},
{
"display_name": "Scala Backend Developer",
"id": 87,
"rationale": null,
"role_archetype": "Engineering",
"slug": "scala-backend-developer",
"source": "db"
},
{
"display_name": "AI Engineer",
"id": 13,
"rationale": null,
"role_archetype": null,
"slug": "ai-engineer",
"source": "db"
},
{
"display_name": "Cloud Architect",
"id": 9,
"rationale": null,
"role_archetype": null,
"slug": "cloud-architect",
"source": "db"
},
{
"display_name": "Cloud Security Engineer",
"id": 23,
"rationale": null,
"role_archetype": null,
"slug": "cloud-security-engineer",
"source": "db"
},
{
"display_name": "Engineering Manager",
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"rationale": null,
"role_archetype": null,
"slug": "engineering-manager",
"source": "db"
}
],
"chosen_role": {
"display_name": "Data Engineer",
"id": 2,
"rationale": "Exact alias hit on data-engineer (1.0) \u2014 no other alias at this confidence; skill_top data-engineer 0.25 does not contradict",
"role_archetype": null,
"slug": "data-engineer",
"source": "db"
},
"dimensions": [
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Cloud Platforms",
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"rationale": "Underlying cloud providers that host the managed services or infrastructure used by the role, such as AWS, Azure, and GCP.",
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},
"input_skill": "Azure",
"llm_role": null,
"roles_from_db": [
{
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"rationale": null,
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},
{
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},
{
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"slug": "cybersecurity-engineer",
"source": "db"
},
{
"display_name": "Data Engineer",
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"rationale": null,
"role_archetype": null,
"slug": "data-engineer",
"source": "db"
},
{
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"slug": "devops-engineer",
"source": "db"
},
{
"display_name": "Fullstack Developer",
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"role_archetype": null,
"slug": "full-stack-engineer",
"source": "db"
},
{
"display_name": "Go Backend Developer",
"id": 81,
"rationale": null,
"role_archetype": "Engineering",
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"source": "db"
},
{
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"rationale": null,
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},
{
"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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"slug": "ml-engineer",
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},
{
"display_name": "MLOps Engineer",
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"slug": "ml-ops-engineer",
"source": "db"
},
{
"display_name": "Node.js Backend Developer",
"id": 82,
"rationale": null,
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"slug": "node-backend-developer",
"source": "db"
},
{
"display_name": "Python Backend Developer",
"id": 80,
"rationale": null,
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"slug": "python-backend-developer",
"source": "db"
},
{
"display_name": "Scala Backend Developer",
"id": 87,
"rationale": null,
"role_archetype": "Engineering",
"slug": "scala-backend-developer",
"source": "db"
}
]
},
{
"dimension": {
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"display_name": "Cloud Platforms \u0026 Managed Services",
"id": 221,
"rationale": "Operates and integrates vendor-specific cloud compute, storage, and hosting services.",
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"source": "db"
},
"input_skill": "Azure",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Fullstack Developer",
"id": 15,
"rationale": null,
"role_archetype": null,
"slug": "full-stack-engineer",
"source": "db"
},
{
"display_name": "Go Backend Developer",
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"rationale": null,
"role_archetype": "Engineering",
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},
{
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"rationale": null,
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}
]
},
{
"dimension": {
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"source": "db"
},
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{
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}
]
},
{
"dimension": {
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"rationale": "Major cloud platforms and their core service ecosystems used to design target-state architectures, choose deployment boundaries, and evaluate managed capabilities. This is the primary substrate for cloud architecture decisions.",
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},
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"roles_from_db": [
{
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},
{
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}
]
},
{
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},
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{
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},
{
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}
]
},
{
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"rationale": "Coordinate usage, licensing, and architecture decisions for major vendor software and cloud product families.",
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"source": "db"
},
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"roles_from_db": [
{
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}
]
},
{
"dimension": {
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"display_name": "Data Lineage and Metadata",
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"rationale": "Cataloging, documenting, and tracing how data moves and changes across systems. This dimension supports impact analysis, governance, discoverability, and operational understanding of datasets.",
"slug": "data-lineage-and-metadata",
"source": "db"
},
"input_skill": "Metadata Management",
"llm_role": null,
"roles_from_db": [
{
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}
]
}
],
"input_final_skills": [
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"Data Quality",
"Data Lineage",
"Metadata Management",
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"Data Governance"
],
"input_llm_skills": [
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"Workflow Automation",
"Data Quality",
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"Data Engineering",
"Data Governance"
],
"new_aliases_persisted": 0,
"run_id": "f3dba03c-7407-4d0f-9d31-56804513e8aa",
"skills_detail": [
{
"aliases_in_db": [
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"is_primary": true,
"match_strategy": "CASE_INSENSITIVE"
}
],
"canonical": {
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"is_extractable": true,
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},
"dimensions": [
{
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},
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{
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},
{
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},
{
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},
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{
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},
{
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},
{
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}
]
},
{
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}
]
},
{
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},
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}
]
},
{
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},
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{
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}
]
},
{
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},
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},
{
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}
]
},
{
"dimension": {
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},
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}
]
}
],
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},
{
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},
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"keep_log": [],
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"warnings": []
},
"source_tag": "llm",
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},
{
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"input_skill": "Workflow Automation",
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"new_alias_persisted": false,
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"typical_lifespan": "MULTI_YEAR",
"version_strategy": "UNVERSIONED",
"volatility": "MEDIUM"
},
"enrichment": null,
"keep_log": [],
"locked_dimensions": [],
"merge_log": [],
"placed": null,
"relationships": null,
"skill_id": "workflow-automation",
"split_log": [],
"typed": null,
"warnings": []
},
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [],
"input_skill": "Data Quality",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": {
"derived": {
"category": "Data Engineering Tools",
"skill_nature": "CONCEPT",
"sub_category": "general",
"typical_lifespan": "MULTI_YEAR",
"version_strategy": "UNVERSIONED",
"volatility": "MEDIUM"
},
"enrichment": null,
"keep_log": [],
"locked_dimensions": [],
"merge_log": [],
"placed": null,
"relationships": null,
"skill_id": "data-quality",
"split_log": [],
"typed": null,
"warnings": []
},
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [],
"input_skill": "Data Lineage",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": {
"derived": {
"category": "Data Engineering Tools",
"skill_nature": "CONCEPT",
"sub_category": "general",
"typical_lifespan": "MULTI_YEAR",
"version_strategy": "UNVERSIONED",
"volatility": "MEDIUM"
},
"enrichment": null,
"keep_log": [],
"locked_dimensions": [],
"merge_log": [],
"placed": null,
"relationships": null,
"skill_id": "data-lineage",
"split_log": [],
"typed": null,
"warnings": []
},
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [
{
"alias_text": "Metadata management",
"alias_type": "CANONICAL",
"id": 341,
"is_primary": true,
"match_strategy": "CASE_INSENSITIVE"
}
],
"canonical": {
"category_id": 8,
"display_name": "Metadata management",
"id": 137,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "METHODOLOGY",
"slug": "metadata-management",
"sub_category_id": 109,
"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"dimensions": [
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Data Lineage and Metadata",
"id": 28,
"rationale": "Cataloging, documenting, and tracing how data moves and changes across systems. This dimension supports impact analysis, governance, discoverability, and operational understanding of datasets.",
"slug": "data-lineage-and-metadata",
"source": "db"
},
"input_skill": "Metadata Management",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Data Engineer",
"id": 2,
"rationale": null,
"role_archetype": null,
"slug": "data-engineer",
"source": "db"
}
]
}
],
"input_skill": "Metadata Management",
"matched_via": "alias",
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": null,
"source_tag": "db",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [],
"input_skill": "Data Engineering",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": {
"derived": {
"category": "Data Engineering Tools",
"skill_nature": "CONCEPT",
"sub_category": "general",
"typical_lifespan": "MULTI_YEAR",
"version_strategy": "UNVERSIONED",
"volatility": "MEDIUM"
},
"enrichment": null,
"keep_log": [],
"locked_dimensions": [],
"merge_log": [],
"placed": null,
"relationships": null,
"skill_id": "data-engineering",
"split_log": [],
"typed": null,
"warnings": []
},
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [],
"input_skill": "Data Governance",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": {
"derived": {
"category": "Data Engineering Tools",
"skill_nature": "CONCEPT",
"sub_category": "general",
"typical_lifespan": "MULTI_YEAR",
"version_strategy": "UNVERSIONED",
"volatility": "MEDIUM"
},
"enrichment": null,
"keep_log": [],
"locked_dimensions": [],
"merge_log": [],
"placed": null,
"relationships": null,
"skill_id": "data-governance",
"split_log": [],
"typed": null,
"warnings": []
},
"source_tag": "llm",
"was_in_llm_skills": true
}
],
"unmatched_skills": [
"Data Pipelines",
"Workflow Automation",
"Data Quality",
"Data Lineage",
"Data Engineering",
"Data Governance"
]
}
API 3 — final-role-output
{
"chosen_role": {
"display_name": "Data Engineer",
"id": 2,
"rationale": "Exact alias hit on data-engineer (1.0) \u2014 no other alias at this confidence; skill_top data-engineer 0.25 does not contradict",
"role_archetype": null,
"slug": "data-engineer",
"source": "db"
},
"chosen_role_resolution": "in_db",
"final_input_skills": [
{
"skill": "Azure",
"tag": "in_db"
},
{
"skill": "Data Pipelines",
"tag": "new"
},
{
"skill": "Workflow Automation",
"tag": "new"
},
{
"skill": "Data Quality",
"tag": "new"
},
{
"skill": "Data Lineage",
"tag": "new"
},
{
"skill": "Metadata Management",
"tag": "in_db"
},
{
"skill": "Data Engineering",
"tag": "new"
},
{
"skill": "Data Governance",
"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 Platforms",
"id": 20,
"rationale": "Underlying cloud providers that host the managed services or infrastructure used by the role, such as AWS, Azure, and GCP.",
"slug": "cloud-platforms",
"source": "db"
},
"dimension_id": 20,
"input_skill": "Azure",
"llm_role": null,
"matched_chosen_role": true,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension saved",
"role_dimension_saved": true,
"roles_from_db": [
{
"display_name": ".NET Backend Developer",
"id": 83,
"rationale": null,
"role_archetype": "Engineering",
"slug": "dotnet-backend-developer",
"source": "db"
},
{
"display_name": "Backend Developer",
"id": 1,
"rationale": null,
"role_archetype": "A Backend Engineer designs, builds, and maintains the server-side logic and data handling that power applications and services. They focus on implementing reliable business functionality, integrating with other systems, and ensuring the backend is scalable, maintainable, and observable.",
"slug": "backend-engineer",
"source": "db"
},
{
"display_name": "Cyber Security Engineer",
"id": 5,
"rationale": null,
"role_archetype": null,
"slug": "cybersecurity-engineer",
"source": "db"
},
{
"display_name": "Data Engineer",
"id": 2,
"rationale": null,
"role_archetype": null,
"slug": "data-engineer",
"source": "db"
},
{
"display_name": "DevOps Engineer",
"id": 10,
"rationale": null,
"role_archetype": null,
"slug": "devops-engineer",
"source": "db"
},
{
"display_name": "Fullstack Developer",
"id": 15,
"rationale": null,
"role_archetype": null,
"slug": "full-stack-engineer",
"source": "db"
},
{
"display_name": "Go Backend Developer",
"id": 81,
"rationale": null,
"role_archetype": "Engineering",
"slug": "go-backend-developer",
"source": "db"
},
{
"display_name": "Java Backend Developer",
"id": 79,
"rationale": null,
"role_archetype": "Engineering",
"slug": "java-backend-developer",
"source": "db"
},
{
"display_name": "Kotlin Backend Developer",
"id": 84,
"rationale": null,
"role_archetype": "Engineering",
"slug": "kotlin-server-backend-developer",
"source": "db"
},
{
"display_name": "ML Engineer",
"id": 3,
"rationale": null,
"role_archetype": null,
"slug": "ml-engineer",
"source": "db"
},
{
"display_name": "MLOps Engineer",
"id": 16,
"rationale": null,
"role_archetype": null,
"slug": "ml-ops-engineer",
"source": "db"
},
{
"display_name": "Node.js Backend Developer",
"id": 82,
"rationale": null,
"role_archetype": "Engineering",
"slug": "node-backend-developer",
"source": "db"
},
{
"display_name": "Python Backend Developer",
"id": 80,
"rationale": null,
"role_archetype": "Engineering",
"slug": "python-backend-developer",
"source": "db"
},
{
"display_name": "Scala Backend Developer",
"id": 87,
"rationale": null,
"role_archetype": "Engineering",
"slug": "scala-backend-developer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 188,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 2,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Cloud Platforms \u0026 Managed Services",
"id": 221,
"rationale": "Operates and integrates vendor-specific cloud compute, storage, and hosting services.",
"slug": "cloud-platforms-managed-services",
"source": "db"
},
"dimension_id": 221,
"input_skill": "Azure",
"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": "Fullstack Developer",
"id": 15,
"rationale": null,
"role_archetype": null,
"slug": "full-stack-engineer",
"source": "db"
},
{
"display_name": "Go Backend Developer",
"id": 81,
"rationale": null,
"role_archetype": "Engineering",
"slug": "go-backend-developer",
"source": "db"
},
{
"display_name": "Node.js Backend Developer",
"id": 82,
"rationale": null,
"role_archetype": "Engineering",
"slug": "node-backend-developer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 188,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 2,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Cloud Platforms for AI Deployment",
"id": 211,
"rationale": "Major cloud services that provide infrastructure and managed services for AI workloads.",
"slug": "cloud-platforms-for-ai-deployment",
"source": "db"
},
"dimension_id": 211,
"input_skill": "Azure",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "AI Engineer",
"id": 13,
"rationale": null,
"role_archetype": null,
"slug": "ai-engineer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 188,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 2,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Cloud Provider Platforms",
"id": 131,
"rationale": "Major cloud platforms and their core service ecosystems used to design target-state architectures, choose deployment boundaries, and evaluate managed capabilities. This is the primary substrate for cloud architecture decisions.",
"slug": "cloud-provider-platforms",
"source": "db"
},
"dimension_id": 131,
"input_skill": "Azure",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "Cloud Architect",
"id": 9,
"rationale": null,
"role_archetype": null,
"slug": "cloud-architect",
"source": "db"
},
{
"display_name": "Cloud Security Engineer",
"id": 23,
"rationale": null,
"role_archetype": null,
"slug": "cloud-security-engineer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 188,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 2,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Cloud Security Posture Tools",
"id": 64,
"rationale": "Cloud-native security platforms used to assess misconfiguration, workload exposure, and cloud control coverage. This dimension includes the major CNAPP/CSPM/CWPP vendors and cloud security services the role reviews and tunes.",
"slug": "cloud-security-posture-tools",
"source": "db"
},
"dimension_id": 64,
"input_skill": "Azure",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "Cloud Security Engineer",
"id": 23,
"rationale": null,
"role_archetype": null,
"slug": "cloud-security-engineer",
"source": "db"
},
{
"display_name": "Cyber Security Engineer",
"id": 5,
"rationale": null,
"role_archetype": null,
"slug": "cybersecurity-engineer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 188,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 2,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Vendor Product Families",
"id": 477,
"rationale": "Coordinate usage, licensing, and architecture decisions for major vendor software and cloud product families.",
"slug": "vendor-product-families",
"source": "db"
},
"dimension_id": 477,
"input_skill": "Azure",
"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": 188,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 2,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Data Lineage and Metadata",
"id": 28,
"rationale": "Cataloging, documenting, and tracing how data moves and changes across systems. This dimension supports impact analysis, governance, discoverability, and operational understanding of datasets.",
"slug": "data-lineage-and-metadata",
"source": "db"
},
"dimension_id": 28,
"input_skill": "Metadata Management",
"llm_role": null,
"matched_chosen_role": true,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension saved",
"role_dimension_saved": true,
"roles_from_db": [
{
"display_name": "Data Engineer",
"id": 2,
"rationale": null,
"role_archetype": null,
"slug": "data-engineer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 137,
"skill_tag": "in_db",
"skipped_reason": null
}
],
"new_skills_created": 0,
"role_dimension_saved": 0,
"skill_dimension_saved": 0,
"skipped": 0
},
"planner_output": null,
"run_id": "f3dba03c-7407-4d0f-9d31-56804513e8aa"
}