Pipeline run
1db860ae-2365-41f2-8066-6642e3b2e67b
Pipeline LLM cost (USD)
API 1: $0.0034
API 2: $0.0003
API 3: $0.0000
Total: $0.0037
Client output enrichment
v2 Skill cluster · Nature of work · AI index · Tech stack maturity · Evidence · KRA description
role baseline loaded
sources · ai_index: jd · nature_of_work: jd · tech_stack_maturity: jd
Nature of work
· Data pipeline development
Designs, builds, and optimizes cloud/on-prem/hybrid data pipelines, while creating reusable components, shaping data architecture, and enforcing governance through metadata, automation, and collaboration with engineers and stakeholders.
"Design, build, optimize, and maintain robust data pipelines across cloud, on-premises, or hybrid environments"
Tech stack maturity
Modern Cloud Native
A data engineer focused on cloud and metadata management most strongly aligns with modern cloud-native data platform practices.
AI index (0 = no AI use, 5 = totally AI-dependent · v2.1)
0.20 / 5
· Title match
✓ Has AI skill
· AI skill (primary)
· AI skill (secondary)
· On AI team
· Builds AI products
vocab breakdown (legacy)
Assistants (×1):
—
Frameworks (×2):
—
Models / concepts (×3):
AI, ML, Generative AI
Evidence — skills matched in JD (10)
Data Pipelines
Cloud
On-Premises
Hybrid Environments
Data Modeling
Data Storage
Data Retrieval
Metadata Management
Automation
Data Governance
Skill cluster (3 dimension groups, role-scoped)
Cloud Platforms
Cloud
Data Lineage and Metadata
Metadata Management
Cross-cutting / unaligned
Data Pipelines
On-Premises
Hybrid Environments
Data Modeling
Data Storage
Data Retrieval
Automation
Data Governance
Show KRA description ↓
End-to-End Data Pipeline Development:Design, build, optimize, and maintain robust data pipelines across cloud, on-premises, or hybrid environments, ensuring performance, scalability, and seamless data flow.
Reusable Components & Frameworks:Develop reusable data pipeline components and contribute to the team's data pipeline framework evolution.
Data Architecture & Solutions:Contribute to data architecture design, applying data modelling, storage, and retrieval expertise.
Data Governance & Automation:Champion data integrity, security, and efficiency through metadata management, automation, and data governance best practices.
Collaborative Problem Solving:Partner with stakeholders, data teams, and engineers to define requirements, troubleshoot, optimize, and deliver data-driven insights.
Mentorship & Knowledge Transfer:Guide and mentor junior data engineers, fostering knowledge sharing and professional growth.
Signals
Skill
data-engineer
0.20
Alias
data-engineer
1.00
KRA
data-engineer
0.62
Post-classification
Centroidupdated · n=266
Alias collision log—
New-role queue—
New skills captured8
New KRA captured—
Captured for admin review
Data Pipelines
primary
↔
Data Engineer
pending
On-Premises
primary
↔
Data Engineer
pending
Hybrid Environments
primary
↔
Data Engineer
pending
Data Modeling
primary
↔
Data Engineer
pending
Data Storage
primary
↔
Data Engineer
pending
Data Retrieval
primary
↔
Data Engineer
pending
Automation
primary
↔
Data Engineer
pending
Data Governance
primary
↔
Data Engineer
pending
Status:
completed
Created: 2026-05-27T15:13:59.951080Z
Updated: 2026-06-12T16:40:00.832689Z
API 3 duration: 10047 ms
Flow
Current 3-step pipeline
1 POST /skills/extract-from-jd
2 POST /skills/extract-details
3 POST /skills/final-role-output
Role
Chosen role & resolution
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 0.20 does not contradict
Resolution:
in_db
— role exists in library; skill↔dim and role↔dim links saved when applicable.
0
New skills
0
Skill↔dim saved
0
Role↔dim saved
2
Skipped
Job description
Overview Of The Company Jio Platforms Ltd. is a revolutionary Indian multinational tech company, often referred to as India's biggest startup, headquartered in Mumbai. Launched in 2019, it's the powerhouse behind Jio, India's largest mobile network with over 400 million users. But Jio Platforms is more than just telecom. It's a comprehensive digital ecosystem, developing cutting-edge solutions across media, entertainment, and enterprise services through popular brands like JioMart, JioFiber, and JioSaavn. Join us at Jio Platforms and be part of a fast-paced, dynamic environment at the forefront of India's digital transformation. Collaborate with brilliant minds to develop next-gen solutions that empower millions and revolutionize industries. Team Overview The Data Platforms Team is the launchpad for a data-driven future, empowering the Reliance Group of Companies. We're a passionate group of experts architecting an enterprise-scale data mesh to unlock the power of big data, generative AI, and ML modelling across various domains. We don't just manage data we transform it into intelligent actions that fuel strategic decision-making. Imagine crafting a platform that automates data flow, fuels intelligent insights, and empowers the organization that's what we do. Join our collaborative and innovative team, and be a part of shaping the future of data for India's biggest digital revolution! About the role. Job Title: Data Engineer Department/Business: Analytics COE Location: Mumbai, Hyderabad, Gurgaon, Bangalore Experience: 4 to 6 years of Experience Remote/On-site/Hybrid: On - Site Responsibilities End-to-End Data Pipeline Development:Design, build, optimize, and maintain robust data pipelines across cloud, on-premises, or hybrid environments, ensuring performance, scalability, and seamless data flow. Reusable Components & Frameworks:Develop reusable data pipeline components and contribute to the team's data pipeline framework evolution. Data Architecture & Solutions:Contribute to data architecture design, applying data modelling, storage, and retrieval expertise. Data Governance & Automation:Champion data integrity, security, and efficiency through metadata management, automation, and data governance best practices. Collaborative Problem Solving:Partner with stakeholders, data teams, and engineers to define requirements, troubleshoot, optimize, and deliver data-driven insights. Mentorship & Knowledge Transfer:Guide and mentor junior data engineers, fostering knowledge sharing and professional growth. Qualification Details Education: Bachelor's degree or higher in Computer Science, Data Science, Engineering, or a related technical field. Core Programming: Excellent command of a primary data engineering language (Scala, Python, or Java) with a strong foundation in OOPS and functional programming concepts. Big Data Technologies: Hands-on experience with data processing frameworks (e.g., Hadoop, Spark, Apache Hive, NiFi, Ozone, Kudu), ideally including streaming technologies (Kafka, Spark Streaming, Flink, etc.). Database Expertise: Excellent querying skills (SQL) and strong understanding of relational databases (e.g., MySQL, PostgreSQL). Experience with NoSQL databases (e.g., MongoDB, Cassandra) is a plus. End-to-End Pipelines: Demonstrated experience in implementing, optimizing, and maintaining complete data pipelines, integrating varied sources and sinks including streaming real-time data. Cloud Expertise: Knowledge of Cloud Technologies like Azure HDInsights, Synapse, EventHub and GCP DataProc, Dataflow, BigQuery. CI/CD Expertise: Experience with CI/CD methodologies and tools, including strong Linux and shell scripting skills for automation. Desired Skills & Attributes Problem-Solving & Troubleshooting: Proven ability to analyze and solve complex data problems, troubleshoot data pipeline issues effectively. Communication & Collaboration: Excellent communication skills, both written and verbal, with the ability to collaborate across teams (data scientists, engineers, stakeholders). Continuous Learning & Adaptability: A demonstrated passion for staying up-to-date with emerging data technologies and a willingness to adapt to new tools.
Skills from this JD
Each row merges API 1 extraction, API 2 library match / v3 orchestration (dimensions + locked dims), and API 3 persistence tags.
Data Pipelines
Primary
New / orchestrated
API 3: new canonical path (new)
New / unmatched skill (orchestrated in API 2)
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
Derived legacy fields
- Category
- Data Engineering Tools
- Sub-category
- general
- Skill nature
- CONCEPT
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Cloud
Primary
Library skill
API 3: existing canonical (in_db)
Existing skill (matched library)
Canonical: Cloud
id=1572
· cloud
Aliases — catalog
- Cloud (CANONICAL)
Context tags (catalog)
AWS
Azure
DevOps
Docker
Google Cloud
IaaS
Kubernetes
PaaS
SaaS
cloud migration
cloud security
hybrid cloud
microservices
multi-cloud
serverless
Stored enrichment (catalog DB)
- Category
- Domain
- Sub-category
- Cloud Computing
- Confidence
- 0.93
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Cloud is a hiring-pipeline staple: AWS, Azure, and GCP appear in a large share of modern infrastructure JDs, and major vendors continue expanding cloud services rather than sunsetting them.
Skill profile (library / DB)
- Skill nature
- CONCEPT
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 37
- Sub-category id
- 1177
- 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
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Cloud Platforms
cloud-platforms
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved |
On-Premises
Primary
New / orchestrated
API 3: new canonical path (new)
New / unmatched skill (orchestrated in API 2)
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
Derived legacy fields
- Category
- Infrastructure Tools
- Sub-category
- general
- Skill nature
- CONCEPT
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Version strategy
- UNVERSIONED
Hybrid Environments
Primary
New / orchestrated
API 3: new canonical path (new)
New / unmatched skill (orchestrated in API 2)
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
Derived legacy fields
- Category
- Infrastructure Tools
- Sub-category
- general
- Skill nature
- CONCEPT
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Data Modeling
Primary
Library skill
API 3: existing canonical (in_db)
Existing skill (matched library)
Canonical: domain modeling
id=2379
· domain-modeling
Aliases — catalog
- domain modeling (CANONICAL) primary
- Domain Modeling (CANONICAL)
Context tags (catalog)
CQRS
DDD
ERD
UML
aggregate
bounded context
business logic
context map
context mapping
data modeling
domain events
domain-driven design
entities
entity
event sourcing
event storming
microservices
repositories
repository pattern
service layer
services
value object
value objects
Stored enrichment (catalog DB)
- Category
- Methodology
- Sub-category
- Domain Modeling
- Confidence
- 0.90
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Common in software JDs under DDD/business analysis; many roles ask for domain modeling or domain-driven design, and it remains a standard design skill rather than a niche tool.
Skill profile (library / DB)
- Skill nature
- METHODOLOGY
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 8
- Sub-category id
- 2831
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
Application Architecture Patterns Catalog dimension db id 293
Library dimension (catalog)
Roles linked in library: .NET Backend Developer, Python Backend Developer
-
Service Architecture and Design Patterns Catalog dimension db id 18
Library dimension (catalog)
Roles linked in library: Backend Developer, Java Backend Developer, Kotlin Backend Developer, Node.js Backend Developer, PHP Backend Developer, Ruby Backend Developer, Scala Backend Developer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Application Architecture Patterns
application-architecture-patterns
|
— | — |
Skipped — no persistable v3 meta for new skill
skill_not_in_db_v3_proposed
|
|
Service Architecture and Design Patterns
service-architecture-and-design-patterns
|
— | — |
Skipped — no persistable v3 meta for new skill
skill_not_in_db_v3_proposed
|
Data Storage
Primary
New / orchestrated
API 3: new canonical path (new)
New / unmatched skill (orchestrated in API 2)
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
Derived legacy fields
- Category
- Databases
- Sub-category
- general
- Skill nature
- CONCEPT
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Version strategy
- UNVERSIONED
Data Retrieval
Primary
New / orchestrated
API 3: new canonical path (new)
New / unmatched skill (orchestrated in API 2)
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
Derived legacy fields
- Category
- Databases
- Sub-category
- general
- Skill nature
- CONCEPT
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Version strategy
- UNVERSIONED
Metadata Management
Primary
Library skill
API 3: existing canonical (in_db)
Existing skill (matched library)
Canonical: Metadata management
id=137
· metadata-management
Aliases — catalog
- Metadata management (CANONICAL) primary
Context tags (catalog)
business glossary
cataloging
controlled vocabulary
data catalog
data classification
data dictionary
data governance
data lineage
data quality
data stewardship
master data management
metadata repository
ontology
schema registry
taxonomy
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 |
Automation
Primary
New / orchestrated
API 3: new canonical path (new)
New / unmatched skill (orchestrated in API 2)
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
Derived legacy fields
- Category
- Infrastructure Tools
- Sub-category
- general
- Skill nature
- PRACTICE
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Data Governance
Primary
New / orchestrated
API 3: new canonical path (new)
New / unmatched skill (orchestrated in API 2)
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
Derived legacy fields
- 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 |
|---|---|---|---|---|---|---|
| Cloud | in_db |
Cloud Platforms
cloud-platforms
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved | |
| Data Modeling | new |
Application Architecture Patterns
application-architecture-patterns
|
— | — | Skipped — no persistable v3 meta for new skill | skill_not_in_db_v3_proposed |
| Data Modeling | new |
Service Architecture and Design Patterns
service-architecture-and-design-patterns
|
— | — | Skipped — no persistable v3 meta for new skill | skill_not_in_db_v3_proposed |
| 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=CONCEPT lifespan=MULTI_YEAR | |
| canonical_skill_proposed | On-Premises | type=Infrastructure Tools subtype=general nature=CONCEPT lifespan=EVERGREEN | |
| canonical_skill_proposed | Hybrid Environments | type=Infrastructure Tools subtype=general nature=CONCEPT lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Data Storage | type=Databases subtype=general nature=CONCEPT lifespan=EVERGREEN | |
| canonical_skill_proposed | Data Retrieval | type=Databases subtype=general nature=CONCEPT lifespan=EVERGREEN | |
| canonical_skill_proposed | Automation | type=Infrastructure Tools subtype=general nature=PRACTICE lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Data Governance | type=Data Engineering Tools subtype=general nature=CONCEPT lifespan=MULTI_YEAR | |
| dimension_skill_link_proposed | Data Modeling ↔ Application Architecture Patterns | |
| dimension_skill_link_proposed | Data Modeling ↔ Service Architecture and Design Patterns |
nano JD Parser — gpt-4.1-nano click to toggle
RoleData Engineer
CompanyJio Platforms Ltd.
Experience4 to 6 years of Experience
DomainIT Services & Consulting
Location
Mumbai, India
(onsite)
JD type
pass
Show raw JSON
{
"JD_type": "pass",
"about_company": {
"source_marker": {
"first_5_words": "Jio Platforms Ltd. is a",
"last_5_words": "JioMart, JioFiber, and JioSaavn."
},
"text": "Jio Platforms Ltd. is a revolutionary Indian multinational tech company, often referred to as India\u0027s biggest startup, headquartered in Mumbai. Launched in 2019, it\u0027s the powerhouse behind Jio, India\u0027s largest mobile network with over 400 million users. But Jio Platforms is more than just telecom. It\u0027s a comprehensive digital ecosystem, developing cutting-edge solutions across media, entertainment, and enterprise services through popular brands like JioMart, JioFiber, and JioSaavn.",
"word_count": 64
},
"certifications": [],
"company_name": "Jio Platforms Ltd.",
"ctc": null,
"domain": {
"primary": {
"aliases": [
"ITES",
"BPO",
"Tech Consulting"
],
"domain": "IT Services \u0026 Consulting"
},
"secondary": null
},
"education": [
{
"level": "Bachelor\u0027s",
"qualification": "BTECH/BE/BSC - Computer Science / Data Science / Engineering (or related)",
"raw": "Bachelor\u0027s degree or higher in Computer Science, Data Science, Engineering, or a related technical field.",
"requirement": "required"
}
],
"experience": {
"max": 6,
"min": 4,
"raw": "4 to 6 years of Experience"
},
"job_locations": [
{
"aliases": [
"Bombay"
],
"city": "Mumbai",
"country": "India",
"state": "Maharashtra",
"work_mode": "onsite"
},
{
"aliases": [],
"city": "Hyderabad",
"country": "India",
"state": "Telangana",
"work_mode": "onsite"
},
{
"aliases": [
"Gurugram"
],
"city": "Gurgaon",
"country": "India",
"state": "Haryana",
"work_mode": "onsite"
},
{
"aliases": [
"Bengaluru"
],
"city": "Bangalore",
"country": "India",
"state": "Karnataka",
"work_mode": "onsite"
}
],
"role": "Data Engineer",
"role_aliases": [
"Data Engineer",
"Data Developer",
"Big Data Engineer"
],
"role_archetype": "Data",
"roles_and_responsibilities": [
{
"bullet_count": 6,
"heading": "Responsibilities",
"heading_was_present": true,
"source_marker": {
"first_5_words": "End-to-End Data Pipeline Development:Design,",
"last_5_words": "knowledge sharing and professional growth."
},
"text": "End-to-End Data Pipeline Development:Design, build, optimize, and maintain robust data pipelines across cloud, on-premises, or hybrid environments, ensuring performance, scalability, and seamless data flow.\n\nReusable Components \u0026 Frameworks:Develop reusable data pipeline components and contribute to the team\u0027s data pipeline framework evolution.\n\nData Architecture \u0026 Solutions:Contribute to data architecture design, applying data modelling, storage, and retrieval expertise.\n\nData Governance \u0026 Automation:Champion data integrity, security, and efficiency through metadata management, automation, and data governance best practices.\n\nCollaborative Problem Solving:Partner with stakeholders, data teams, and engineers to define requirements, troubleshoot, optimize, and deliver data-driven insights.\n\nMentorship \u0026 Knowledge Transfer:Guide and mentor junior data engineers, fostering knowledge sharing and professional growth.",
"word_count": 134
}
],
"urls": []
}
API 1 — extract-from-jd click to toggle
{
"final_skills": [
{
"is_primary": true,
"skill_name": "Data Pipelines"
},
{
"is_primary": true,
"skill_name": "Cloud"
},
{
"is_primary": true,
"skill_name": "On-Premises"
},
{
"is_primary": true,
"skill_name": "Hybrid Environments"
},
{
"is_primary": true,
"skill_name": "Data Modeling"
},
{
"is_primary": true,
"skill_name": "Data Storage"
},
{
"is_primary": true,
"skill_name": "Data Retrieval"
},
{
"is_primary": true,
"skill_name": "Metadata Management"
},
{
"is_primary": true,
"skill_name": "Automation"
},
{
"is_primary": true,
"skill_name": "Data Governance"
}
],
"jd_role": {
"display_name": "Data Engineer",
"rationale": null,
"role_aliases": [
"Data Engineer",
"Data Developer",
"Big Data Engineer"
],
"role_archetype": "Data",
"slug": ""
},
"nano_parsed": {
"JD_type": "pass",
"about_company": {
"source_marker": {
"first_5_words": "Jio Platforms Ltd. is a",
"last_5_words": "JioMart, JioFiber, and JioSaavn."
},
"text": "Jio Platforms Ltd. is a revolutionary Indian multinational tech company, often referred to as India\u0027s biggest startup, headquartered in Mumbai. Launched in 2019, it\u0027s the powerhouse behind Jio, India\u0027s largest mobile network with over 400 million users. But Jio Platforms is more than just telecom. It\u0027s a comprehensive digital ecosystem, developing cutting-edge solutions across media, entertainment, and enterprise services through popular brands like JioMart, JioFiber, and JioSaavn.",
"word_count": 64
},
"certifications": [],
"company_name": "Jio Platforms Ltd.",
"ctc": null,
"domain": {
"primary": {
"aliases": [
"ITES",
"BPO",
"Tech Consulting"
],
"domain": "IT Services \u0026 Consulting"
},
"secondary": null
},
"education": [
{
"level": "Bachelor\u0027s",
"qualification": "BTECH/BE/BSC - Computer Science / Data Science / Engineering (or related)",
"raw": "Bachelor\u0027s degree or higher in Computer Science, Data Science, Engineering, or a related technical field.",
"requirement": "required"
}
],
"experience": {
"max": 6,
"min": 4,
"raw": "4 to 6 years of Experience"
},
"job_locations": [
{
"aliases": [
"Bombay"
],
"city": "Mumbai",
"country": "India",
"state": "Maharashtra",
"work_mode": "onsite"
},
{
"aliases": [],
"city": "Hyderabad",
"country": "India",
"state": "Telangana",
"work_mode": "onsite"
},
{
"aliases": [
"Gurugram"
],
"city": "Gurgaon",
"country": "India",
"state": "Haryana",
"work_mode": "onsite"
},
{
"aliases": [
"Bengaluru"
],
"city": "Bangalore",
"country": "India",
"state": "Karnataka",
"work_mode": "onsite"
}
],
"role": "Data Engineer",
"role_aliases": [
"Data Engineer",
"Data Developer",
"Big Data Engineer"
],
"role_archetype": "Data",
"roles_and_responsibilities": [
{
"bullet_count": 6,
"heading": "Responsibilities",
"heading_was_present": true,
"source_marker": {
"first_5_words": "End-to-End Data Pipeline Development:Design,",
"last_5_words": "knowledge sharing and professional growth."
},
"text": "End-to-End Data Pipeline Development:Design, build, optimize, and maintain robust data pipelines across cloud, on-premises, or hybrid environments, ensuring performance, scalability, and seamless data flow.\n\nReusable Components \u0026 Frameworks:Develop reusable data pipeline components and contribute to the team\u0027s data pipeline framework evolution.\n\nData Architecture \u0026 Solutions:Contribute to data architecture design, applying data modelling, storage, and retrieval expertise.\n\nData Governance \u0026 Automation:Champion data integrity, security, and efficiency through metadata management, automation, and data governance best practices.\n\nCollaborative Problem Solving:Partner with stakeholders, data teams, and engineers to define requirements, troubleshoot, optimize, and deliver data-driven insights.\n\nMentorship \u0026 Knowledge Transfer:Guide and mentor junior data engineers, fostering knowledge sharing and professional growth.",
"word_count": 134
}
],
"urls": []
},
"rejected": false,
"rejection_reason": null,
"run_id": "1db860ae-2365-41f2-8066-6642e3b2e67b",
"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
}
],
"kra_match_roles": [
{
"display_name": "Data Engineer",
"kra_matches": [
{
"kra_text": "Works with data analysts, data scientists, and business stakeholders to define data models, ingestion schedules, and data delivery requirements.",
"sentence": "Collaborative Problem Solving:Partner with stakeholders, data teams, and engineers to define requirements, troubleshoot, optimize, and deliver data-driven insights.",
"similarity": 0.6505
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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": "Data Architecture \u0026 Solutions:Contribute to data architecture design, applying data modelling, storage, and retrieval expertise.",
"similarity": 0.6276
},
{
"kra_text": "Maintains data catalog entries, column-level data lineage, and technical documentation to support data discoverability and governance across the organization.",
"sentence": "Data Governance \u0026 Automation:Champion data integrity, security, and efficiency through metadata management, automation, and data governance best practices.",
"similarity": 0.579
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],
"matched_count": null,
"matched_skills": null,
"role_id": 2,
"score": 0.6191,
"slug": "data-engineer",
"total_count": null
},
{
"display_name": "Fullstack Developer",
"kra_matches": [
{
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"similarity": 0.5202
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{
"kra_text": "Builds and integrates client-side React or Vue components with server-side Node.js or Django APIs, managing bidirectional data flow across frontend and backend layers.",
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"similarity": 0.4782
},
{
"kra_text": "Works closely with product managers and UX designers to translate requirements and wireframes into working software features through iterative development.",
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],
"matched_count": null,
"matched_skills": null,
"role_id": 15,
"score": 0.4895,
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},
{
"display_name": "Flutter Developer",
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{
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"similarity": 0.5383
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{
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"similarity": 0.4793
},
{
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],
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},
{
"display_name": "Java Backend Developer",
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{
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},
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},
{
"display_name": "Svelte Frontend Developer",
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{
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"similarity": 0.4935
},
{
"kra_text": "backend data integration",
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},
{
"kra_text": "backend data integration",
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],
"skill_match_roles": [
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"matched_count": 2,
"matched_skills": [
"Cloud",
"Metadata management"
],
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},
{
"display_name": "ML Engineer",
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"Cloud"
],
"role_id": 3,
"score": 0.1,
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},
{
"display_name": "Cyber Security Engineer",
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"matched_skills": [
"Cloud"
],
"role_id": 5,
"score": 0.1,
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},
{
"display_name": "DevOps Engineer",
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"matched_skills": [
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],
"role_id": 10,
"score": 0.1,
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},
{
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],
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"score": 0.1,
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]
},
"stage4_decision": {
"alias_collision_detected": false,
"case": "A",
"chosen_role": {
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"matched_count": null,
"matched_skills": null,
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},
"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 0.20 does not contradict",
"sub_role": null
},
"stage5_updates": {
"centroid_n_after": 266,
"centroid_updated": true,
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"new_kra_attached": null,
"new_skills_attached": [
{
"is_primary": true,
"queue_id": 13317,
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"status": "pending"
},
{
"is_primary": true,
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},
{
"is_primary": true,
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"role_display_name": "Data Engineer",
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},
{
"is_primary": true,
"queue_id": 13320,
"role_display_name": "Data Engineer",
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},
{
"is_primary": true,
"queue_id": 13321,
"role_display_name": "Data Engineer",
"role_slug": "data-engineer",
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},
{
"is_primary": true,
"queue_id": 13322,
"role_display_name": "Data Engineer",
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},
{
"is_primary": true,
"queue_id": 13323,
"role_display_name": "Data Engineer",
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"skill_name": "Automation",
"status": "pending"
},
{
"is_primary": true,
"queue_id": 13324,
"role_display_name": "Data Engineer",
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"skill_name": "Data Governance",
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}
],
"queue_entry_id": null,
"v3_pipeline_triggered": false,
"v3_role_slug": null,
"v3_run_id": null
}
}
API 2 — extract-details
{
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"is_extractable": true,
"skill_nature": "CONCEPT",
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"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": 5644,
"existing_alias_text": "Domain Modeling",
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"matched_canonical": {
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},
"matched_via": "embedding_alias"
},
{
"alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
"alias_persisted": false,
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"matched_via": "alias"
}
],
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{
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},
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],
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]
},
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]
},
{
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]
},
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},
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],
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"input_llm_skills": [
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"new_aliases_persisted": 0,
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{
"aliases_in_db": [],
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"new_skill_meta": {
"derived": {
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},
"source_tag": "llm",
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},
{
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"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"
}
]
}
],
"input_skill": "Cloud",
"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": "On-Premises",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": {
"derived": {
"category": "Infrastructure Tools",
"skill_nature": "CONCEPT",
"sub_category": "general",
"typical_lifespan": "EVERGREEN",
"version_strategy": "UNVERSIONED",
"volatility": "STABLE"
},
"enrichment": null,
"keep_log": [],
"locked_dimensions": [],
"merge_log": [],
"placed": null,
"relationships": null,
"skill_id": "on-premises",
"split_log": [],
"typed": null,
"warnings": []
},
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [],
"input_skill": "Hybrid Environments",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": {
"derived": {
"category": "Infrastructure 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": "hybrid-environments",
"split_log": [],
"typed": null,
"warnings": []
},
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [
{
"alias_text": "domain modeling",
"alias_type": "CANONICAL",
"id": 3675,
"is_primary": true,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Domain Modeling",
"alias_type": "CANONICAL",
"id": 5644,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
}
],
"canonical": {
"category_id": 8,
"display_name": "domain modeling",
"id": 2379,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "METHODOLOGY",
"slug": "domain-modeling",
"sub_category_id": 2831,
"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"dimensions": [
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Application Architecture Patterns",
"id": 293,
"rationale": "Structural patterns for organizing Python backend code into maintainable modules, layers, and feature boundaries. This is a coherent cluster because senior backend developers are expected to refactor and shape service internals over time.",
"slug": "application-architecture-patterns",
"source": "db"
},
"input_skill": "Data Modeling",
"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": "Python Backend Developer",
"id": 80,
"rationale": null,
"role_archetype": "Engineering",
"slug": "python-backend-developer",
"source": "db"
}
]
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Service Architecture and Design Patterns",
"id": 18,
"rationale": "Reusable backend design patterns used to structure service code and boundaries. Covers layering, dependency management, domain modeling, and maintainable service organization.",
"slug": "service-architecture-and-design-patterns",
"source": "db"
},
"input_skill": "Data Modeling",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Backend Developer",
"id": 1,
"rationale": null,
"role_archetype": "A Backend Engineer designs, builds, and maintains the server-side logic and data handling that power applications and services. They focus on implementing reliable business functionality, integrating with other systems, and ensuring the backend is scalable, maintainable, and observable.",
"slug": "backend-engineer",
"source": "db"
},
{
"display_name": "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": "Node.js Backend Developer",
"id": 82,
"rationale": null,
"role_archetype": "Engineering",
"slug": "node-backend-developer",
"source": "db"
},
{
"display_name": "PHP Backend Developer",
"id": 86,
"rationale": null,
"role_archetype": "Engineering",
"slug": "php-backend-developer",
"source": "db"
},
{
"display_name": "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": "Data Modeling",
"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": "Data Storage",
"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": "EVERGREEN",
"version_strategy": "UNVERSIONED",
"volatility": "STABLE"
},
"enrichment": null,
"keep_log": [],
"locked_dimensions": [],
"merge_log": [],
"placed": null,
"relationships": null,
"skill_id": "data-storage",
"split_log": [],
"typed": null,
"warnings": []
},
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [],
"input_skill": "Data Retrieval",
"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": "EVERGREEN",
"version_strategy": "UNVERSIONED",
"volatility": "STABLE"
},
"enrichment": null,
"keep_log": [],
"locked_dimensions": [],
"merge_log": [],
"placed": null,
"relationships": null,
"skill_id": "data-retrieval",
"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": "Automation",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": {
"derived": {
"category": "Infrastructure 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
},
{
"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",
"On-Premises",
"Hybrid Environments",
"Data Storage",
"Data Retrieval",
"Automation",
"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 0.20 does not contradict",
"role_archetype": null,
"slug": "data-engineer",
"source": "db"
},
"chosen_role_resolution": "in_db",
"final_input_skills": [
{
"skill": "Data Pipelines",
"tag": "new"
},
{
"skill": "Cloud",
"tag": "in_db"
},
{
"skill": "On-Premises",
"tag": "new"
},
{
"skill": "Hybrid Environments",
"tag": "new"
},
{
"skill": "Data Modeling",
"tag": "in_db"
},
{
"skill": "Data Storage",
"tag": "new"
},
{
"skill": "Data Retrieval",
"tag": "new"
},
{
"skill": "Metadata Management",
"tag": "in_db"
},
{
"skill": "Automation",
"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": "Cloud",
"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": 1572,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 2,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Application Architecture Patterns",
"id": 293,
"rationale": "Structural patterns for organizing Python backend code into maintainable modules, layers, and feature boundaries. This is a coherent cluster because senior backend developers are expected to refactor and shape service internals over time.",
"slug": "application-architecture-patterns",
"source": "db"
},
"dimension_id": 293,
"input_skill": "Data Modeling",
"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": "Python Backend Developer",
"id": 80,
"rationale": null,
"role_archetype": "Engineering",
"slug": "python-backend-developer",
"source": "db"
}
],
"skill_dimension_saved": false,
"skill_id": null,
"skill_tag": "new",
"skipped_reason": "skill_not_in_db_v3_proposed"
},
{
"chosen_role_id": 2,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Service Architecture and Design Patterns",
"id": 18,
"rationale": "Reusable backend design patterns used to structure service code and boundaries. Covers layering, dependency management, domain modeling, and maintainable service organization.",
"slug": "service-architecture-and-design-patterns",
"source": "db"
},
"dimension_id": 18,
"input_skill": "Data Modeling",
"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": "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": "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": "Node.js Backend Developer",
"id": 82,
"rationale": null,
"role_archetype": "Engineering",
"slug": "node-backend-developer",
"source": "db"
},
{
"display_name": "PHP Backend Developer",
"id": 86,
"rationale": null,
"role_archetype": "Engineering",
"slug": "php-backend-developer",
"source": "db"
},
{
"display_name": "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"
},
{
"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": 2
},
"planner_output": null,
"run_id": "1db860ae-2365-41f2-8066-6642e3b2e67b"
}
LLM Calls
Every model call made for this run, in pipeline order. Click a card to see the model's response.
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