Pipeline run

50ca08b0-cee7-4e72-a847-aa873bf9908b

Pipeline LLM cost (USD)

API 1: $0.0035 API 2: $0.0004 API 3: $0.0000 Total: $0.0039

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

Build and maintain scalable cloud data products: batch/streaming pipelines, data lake/warehouse ingestion, data modeling, and deployment automation. Also review and optimize data systems/infrastructure and work with analytics teams to gather requirements.

""Maintains and supports the development of streaming and batch data pipelines""

Tech stack maturity

Modern Cloud Native

A data engineer role with cloud as the primary skill most strongly aligns with modern cloud-native data platforms and workflows.

AI index (0 = no AI use, 5 = totally AI-dependent · v2.1)

0.00 / 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): —

Evidence — skills matched in JD (14)

Data Engineering Cloud Streaming Batch Processing Data Pipelines Data Lake Data Warehouse Data Modeling Data Formats Data Infrastructure Data Engineering Frameworks Data Engineering Architectures Automated Deployment Pipelines Advanced Analytics

Skill cluster (2 dimension groups, role-scoped)

Cloud Platforms

Cloud

Cross-cutting / unaligned

Data Engineering Streaming Batch Processing Data Pipelines Data Lake Data Warehouse Data Modeling Data Formats Data Infrastructure Data Engineering Frameworks Data Engineering Architectures Automated Deployment Pipelines Advanced Analytics

Show KRA description ↓

• DATA & ANALYTICAL SOLUTIONS: Develops moderately complex data products and solutions using advanced data engineering and cloud based technologies, ensuring they are designed and built to be scalable, sustainable and robust. • DATA PIPELINES: Maintains and supports the development of streaming and batch data pipelines that facilitate the seamless ingestion of data from various data sources, transform the data into information and move to data stores like data lake, data warehouse and others. • DATA SYSTEMS: Reviews existing data systems and architectures to implement the identified areas for improvement and optimization. • DATA INFRASTRUCTURE: Helps prepare data infrastructure to support the efficient storage and retrieval of data. • DATA FORMATS: Implements appropriate data formats to improve data usability and accessibility across the organization. • STAKEHOLDER MANAGEMENT: Partners with multi-functional data and advanced analytic teams to collect requirements and ensure that data solutions meet the functional and non-functional needs of various partners. • DATA FRAMEWORKS: Builds moderately complex prototypes to test new concepts and implements data engineering frameworks and architectures to support the improvement of data processing capabilities and advanced analytics initiatives. • AUTOMATED DEPLOYMENT PIPELINES: Implements automated deployment pipelines to support improving efficiency of code deployments with fit for purpose governance. • DATA MODELING: Performs moderately complex data modeling aligned with the datastore technology to ensure sustainable performance and accessibility.

Signals

Skill data-engineer

0.07

Alias data-engineer

1.00

KRA data-engineer

0.65

Post-classification

Centroidupdated · n=474

Alias collision log—

New-role queue—

New skills captured13

New KRA captured—

Captured for admin review

Data Engineering primary ↔ Data Engineer pending

Streaming primary ↔ Data Engineer pending

Batch Processing primary ↔ Data Engineer pending

Data Pipelines primary ↔ Data Engineer pending

Data Lake primary ↔ Data Engineer pending

Data Warehouse primary ↔ Data Engineer pending

Data Modeling primary ↔ Data Engineer pending

Data Formats primary ↔ Data Engineer pending

Data Infrastructure primary ↔ Data Engineer pending

Data Engineering Frameworks primary ↔ Data Engineer pending

Data Engineering Architectures primary ↔ Data Engineer pending

Automated Deployment Pipelines primary ↔ Data Engineer pending

Advanced Analytics primary ↔ Data Engineer pending

Status: completed Created: 2026-05-27T16:50:10.717706Z Updated: 2026-05-27T16:51:55.133384Z API 3 duration: 8639 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 A

slug: data-engineer · id: 2 · source: db

Exact alias hit on data-engineer (1.0) — no other alias at this confidence; skill_top data-engineer 0.07 does not contradict

Resolution: in_db — role exists in library; skill↔dim and role↔dim links saved when applicable.

New skills

Skill↔dim saved

Role↔dim saved

Skipped

Job description

Job Purpose and Impact

• The Professional, Data Engineering job designs, builds and maintains moderately complex data systems that enable data analysis and reporting. With limited supervision, this job collaborates to ensure that large sets of data are efficiently processed and made accessible for decision making.

Key Accountabilities

• DATA & ANALYTICAL SOLUTIONS: Develops moderately complex data products and solutions using advanced data engineering and cloud based technologies, ensuring they are designed and built to be scalable, sustainable and robust.
• DATA PIPELINES: Maintains and supports the development of streaming and batch data pipelines that facilitate the seamless ingestion of data from various data sources, transform the data into information and move to data stores like data lake, data warehouse and others.
• DATA SYSTEMS: Reviews existing data systems and architectures to implement the identified areas for improvement and optimization.
• DATA INFRASTRUCTURE: Helps prepare data infrastructure to support the efficient storage and retrieval of data.
• DATA FORMATS: Implements appropriate data formats to improve data usability and accessibility across the organization.
• STAKEHOLDER MANAGEMENT: Partners with multi-functional data and advanced analytic teams to collect requirements and ensure that data solutions meet the functional and non-functional needs of various partners.
• DATA FRAMEWORKS: Builds moderately complex prototypes to test new concepts and implements data engineering frameworks and architectures to support the improvement of data processing capabilities and advanced analytics initiatives.
• AUTOMATED DEPLOYMENT PIPELINES: Implements automated deployment pipelines to support improving efficiency of code deployments with fit for purpose governance.
• DATA MODELING: Performs moderately complex data modeling aligned with the datastore technology to ensure sustainable performance and accessibility.

Qualifications

• Minimum requirement of 2 years of relevant work experience. Typically reflects 3 years or more of relevant experience.

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 Engineering 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

Streaming 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

Batch Processing 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

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

Data Lake Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)

Canonical: Data Lakes id=1358 · data-lakes

Aliases — catalog

Data Lakes (CANONICAL)

Context tags (catalog)

AWS Lake Formation Azure Data Lake ETL big data data catalog data governance data ingestion data lakes vs data warehouses data modeling data pipelines data warehousing partitioning real-time analytics schema evolution serverless architecture

Stored enrichment (catalog DB)

Category: Architecture
Sub-category: Data Lake Architecture
Confidence: 0.90
Version strategy: NOT_APPLICABLE

Maturity reasoning: Data lakes are widely listed in cloud/data platform job descriptions and are a standard architecture in AWS, Azure, and GCP ecosystems; they’re a common hiring-pipeline staple rather than a niche pattern.

Skill profile (library / DB)

Skill nature: PATTERN
Volatility: STABLE
Typical lifespan: EVERGREEN
Category id: 1
Sub-category id: 1025
Extractable: True
Also category: False

Dimensions (API 2 worklist)

Cloud Storage and Data Services Catalog dimension db id 144

Library dimension (catalog)

Roles linked in library: Cloud Architect
React Frontend Development Catalog dimension db id 96

Library dimension (catalog)

API 3 link attempts (this skill)

Dimension	Skill↔dim	Role↔dim	Outcome
Cloud Storage and Data Services cloud-storage-and-data-services	—	—	Skipped — no persistable v3 meta for new skill skill_not_in_db_v3_proposed
React Frontend Development d_init_01	—	—	Skipped — no persistable v3 meta for new skill skill_not_in_db_v3_proposed

Data Warehouse 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

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 Formats 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

Data Infrastructure 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

Data Engineering Frameworks 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

Data Engineering Architectures 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

Automated Deployment 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: PRACTICE
Volatility: MEDIUM
Typical lifespan: MULTI_YEAR
Version strategy: UNVERSIONED

Advanced Analytics 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 Lake	new	Cloud Storage and Data Services cloud-storage-and-data-services	—	—	Skipped — no persistable v3 meta for new skill	skill_not_in_db_v3_proposed
Data Lake	new	React Frontend Development d_init_01	—	—	Skipped — no persistable v3 meta for new skill	skill_not_in_db_v3_proposed
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

Library artifacts (this run)

Kind	Detail	DB id
canonical_skill_proposed	Data Engineering \| type=Data Engineering Tools subtype=general nature=CONCEPT lifespan=MULTI_YEAR
canonical_skill_proposed	Streaming \| type=Data Engineering Tools subtype=general nature=CONCEPT lifespan=MULTI_YEAR
canonical_skill_proposed	Batch Processing \| type=Data Engineering Tools subtype=general nature=CONCEPT lifespan=MULTI_YEAR
canonical_skill_proposed	Data Pipelines \| type=Data Engineering Tools subtype=general nature=CONCEPT lifespan=MULTI_YEAR
canonical_skill_proposed	Data Warehouse \| type=Data Engineering Tools subtype=general nature=CONCEPT lifespan=MULTI_YEAR
canonical_skill_proposed	Data Formats \| type=Data Engineering Tools subtype=general nature=CONCEPT lifespan=MULTI_YEAR
canonical_skill_proposed	Data Infrastructure \| type=Data Engineering Tools subtype=general nature=CONCEPT lifespan=MULTI_YEAR
canonical_skill_proposed	Data Engineering Frameworks \| type=Data Engineering Tools subtype=general nature=CONCEPT lifespan=MULTI_YEAR
canonical_skill_proposed	Data Engineering Architectures \| type=Data Engineering Tools subtype=general nature=CONCEPT lifespan=MULTI_YEAR
canonical_skill_proposed	Automated Deployment Pipelines \| type=Data Engineering Tools subtype=general nature=PRACTICE lifespan=MULTI_YEAR
canonical_skill_proposed	Advanced Analytics \| type=Data Engineering Tools subtype=general nature=CONCEPT lifespan=MULTI_YEAR
dimension_skill_link_proposed	Data Lake ↔ Cloud Storage and Data Services
dimension_skill_link_proposed	Data Lake ↔ React Frontend Development
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

RoleProfessional, Data Engineering

ExperienceMinimum requirement of 2 years of relevant work experience. Typically reflects 3 years or more of relevant experience.

DomainOther

JD type pass

Show raw JSON

{
  "JD_type": "pass",
  "about_company": null,
  "certifications": [],
  "company_name": null,
  "ctc": null,
  "domain": {
    "primary": {
      "aliases": [],
      "domain": "Other"
    },
    "secondary": null
  },
  "education": [],
  "experience": {
    "max": null,
    "min": 2,
    "raw": "Minimum requirement of 2 years of relevant work experience. Typically reflects 3 years or more of relevant experience."
  },
  "job_locations": [],
  "role": "Professional, Data Engineering",
  "role_aliases": [
    "Data Engineer",
    "Data Engineering Professional"
  ],
  "role_archetype": "Data",
  "roles_and_responsibilities": [
    {
      "bullet_count": 9,
      "heading": "Key Accountabilities",
      "heading_was_present": true,
      "source_marker": {
        "first_5_words": "\u2022 DATA \u0026 ANALYTICAL SOLUTIONS:",
        "last_5_words": "performance and accessibility."
      },
      "text": "\u2022 DATA \u0026 ANALYTICAL SOLUTIONS: Develops moderately complex data products and solutions using advanced data engineering and cloud based technologies, ensuring they are designed and built to be scalable, sustainable and robust.\n\u2022 DATA PIPELINES: Maintains and supports the development of streaming and batch data pipelines that facilitate the seamless ingestion of data from various data sources, transform the data into information and move to data stores like data lake, data warehouse and others.\n\u2022 DATA SYSTEMS: Reviews existing data systems and architectures to implement the identified areas for improvement and optimization.\n\u2022 DATA INFRASTRUCTURE: Helps prepare data infrastructure to support the efficient storage and retrieval of data.\n\u2022 DATA FORMATS: Implements appropriate data formats to improve data usability and accessibility across the organization.\n\u2022 STAKEHOLDER MANAGEMENT: Partners with multi-functional data and advanced analytic teams to collect requirements and ensure that data solutions meet the functional and non-functional needs of various partners.\n\u2022 DATA FRAMEWORKS: Builds moderately complex prototypes to test new concepts and implements data engineering frameworks and architectures to support the improvement of data processing capabilities and advanced analytics initiatives.\n\u2022 AUTOMATED DEPLOYMENT PIPELINES: Implements automated deployment pipelines to support improving efficiency of code deployments with fit for purpose governance.\n\u2022 DATA MODELING: Performs moderately complex data modeling aligned with the datastore technology to ensure sustainable performance and accessibility.",
      "word_count": 233
    }
  ],
  "urls": []
}

API 1 — extract-from-jd click to toggle

{
  "final_skills": [
    {
      "is_primary": true,
      "skill_name": "Data Engineering"
    },
    {
      "is_primary": true,
      "skill_name": "Cloud"
    },
    {
      "is_primary": true,
      "skill_name": "Streaming"
    },
    {
      "is_primary": true,
      "skill_name": "Batch Processing"
    },
    {
      "is_primary": true,
      "skill_name": "Data Pipelines"
    },
    {
      "is_primary": true,
      "skill_name": "Data Lake"
    },
    {
      "is_primary": true,
      "skill_name": "Data Warehouse"
    },
    {
      "is_primary": true,
      "skill_name": "Data Modeling"
    },
    {
      "is_primary": true,
      "skill_name": "Data Formats"
    },
    {
      "is_primary": true,
      "skill_name": "Data Infrastructure"
    },
    {
      "is_primary": true,
      "skill_name": "Data Engineering Frameworks"
    },
    {
      "is_primary": true,
      "skill_name": "Data Engineering Architectures"
    },
    {
      "is_primary": true,
      "skill_name": "Automated Deployment Pipelines"
    },
    {
      "is_primary": true,
      "skill_name": "Advanced Analytics"
    }
  ],
  "jd_role": {
    "display_name": "Professional, Data Engineering",
    "rationale": null,
    "role_aliases": [
      "Data Engineer",
      "Data Engineering Professional"
    ],
    "role_archetype": "Data",
    "slug": ""
  },
  "nano_parsed": {
    "JD_type": "pass",
    "about_company": null,
    "certifications": [],
    "company_name": null,
    "ctc": null,
    "domain": {
      "primary": {
        "aliases": [],
        "domain": "Other"
      },
      "secondary": null
    },
    "education": [],
    "experience": {
      "max": null,
      "min": 2,
      "raw": "Minimum requirement of 2 years of relevant work experience. Typically reflects 3 years or more of relevant experience."
    },
    "job_locations": [],
    "role": "Professional, Data Engineering",
    "role_aliases": [
      "Data Engineer",
      "Data Engineering Professional"
    ],
    "role_archetype": "Data",
    "roles_and_responsibilities": [
      {
        "bullet_count": 9,
        "heading": "Key Accountabilities",
        "heading_was_present": true,
        "source_marker": {
          "first_5_words": "\u2022 DATA \u0026 ANALYTICAL SOLUTIONS:",
          "last_5_words": "performance and accessibility."
        },
        "text": "\u2022 DATA \u0026 ANALYTICAL SOLUTIONS: Develops moderately complex data products and solutions using advanced data engineering and cloud based technologies, ensuring they are designed and built to be scalable, sustainable and robust.\n\u2022 DATA PIPELINES: Maintains and supports the development of streaming and batch data pipelines that facilitate the seamless ingestion of data from various data sources, transform the data into information and move to data stores like data lake, data warehouse and others.\n\u2022 DATA SYSTEMS: Reviews existing data systems and architectures to implement the identified areas for improvement and optimization.\n\u2022 DATA INFRASTRUCTURE: Helps prepare data infrastructure to support the efficient storage and retrieval of data.\n\u2022 DATA FORMATS: Implements appropriate data formats to improve data usability and accessibility across the organization.\n\u2022 STAKEHOLDER MANAGEMENT: Partners with multi-functional data and advanced analytic teams to collect requirements and ensure that data solutions meet the functional and non-functional needs of various partners.\n\u2022 DATA FRAMEWORKS: Builds moderately complex prototypes to test new concepts and implements data engineering frameworks and architectures to support the improvement of data processing capabilities and advanced analytics initiatives.\n\u2022 AUTOMATED DEPLOYMENT PIPELINES: Implements automated deployment pipelines to support improving efficiency of code deployments with fit for purpose governance.\n\u2022 DATA MODELING: Performs moderately complex data modeling aligned with the datastore technology to ensure sustainable performance and accessibility.",
        "word_count": 233
      }
    ],
    "urls": []
  },
  "rejected": false,
  "rejection_reason": null,
  "run_id": "50ca08b0-cee7-4e72-a847-aa873bf9908b",
  "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": "Builds data ingestion pipelines to collect data from transactional databases, third-party APIs, event streams, and file sources into centralized data platforms.",
            "sentence": "DATA PIPELINES: Maintains and supports the development of streaming and batch data pipelines that facilitate the seamless ingestion of data from various data sources, transform the data into information and move to data stores like data lake, data warehouse and others.",
            "similarity": 0.6678
          },
          {
            "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 FRAMEWORKS: Builds moderately complex prototypes to test new concepts and implements data engineering frameworks and architectures to support the improvement of data processing capabilities and advanced analytics initiatives.",
            "similarity": 0.6437
          },
          {
            "kra_text": "Works with data analysts, data scientists, and business stakeholders to define data models, ingestion schedules, and data delivery requirements.",
            "sentence": "STAKEHOLDER MANAGEMENT: Partners with multi-functional data and advanced analytic teams to collect requirements and ensure that data solutions meet the functional and non-functional needs of various partners.",
            "similarity": 0.6268
          }
        ],
        "matched_count": null,
        "matched_skills": null,
        "role_id": 2,
        "score": 0.6461,
        "slug": "data-engineer",
        "total_count": null
      },
      {
        "display_name": "Fullstack Developer",
        "kra_matches": [
          {
            "kra_text": "Delivers features through CI/CD pipelines using automated tests, staged rollouts, feature flags, and incremental deployments.",
            "sentence": "AUTOMATED DEPLOYMENT PIPELINES: Implements automated deployment pipelines to support improving efficiency of code deployments with fit for purpose governance.",
            "similarity": 0.6616
          },
          {
            "kra_text": "Designs and queries relational databases like PostgreSQL and document stores like MongoDB, writing migrations, indexes, and optimized queries.",
            "sentence": "DATA MODELING: Performs moderately complex data modeling aligned with the datastore technology to ensure sustainable performance and accessibility.",
            "similarity": 0.4977
          },
          {
            "kra_text": "Designs and queries relational databases like PostgreSQL and document stores like MongoDB, writing migrations, indexes, and optimized queries.",
            "sentence": "DATA FRAMEWORKS: Builds moderately complex prototypes to test new concepts and implements data engineering frameworks and architectures to support the improvement of data processing capabilities and advanced analytics initiatives.",
            "similarity": 0.4641
          }
        ],
        "matched_count": null,
        "matched_skills": null,
        "role_id": 15,
        "score": 0.5411,
        "slug": "full-stack-engineer",
        "total_count": null
      },
      {
        "display_name": "Node.js Backend Developer",
        "kra_matches": [
          {
            "kra_text": "data modeling and persistence access",
            "sentence": "DATA MODELING: Performs moderately complex data modeling aligned with the datastore technology to ensure sustainable performance and accessibility.",
            "similarity": 0.6319
          },
          {
            "kra_text": "data modeling and persistence access",
            "sentence": "DATA FORMATS: Implements appropriate data formats to improve data usability and accessibility across the organization.",
            "similarity": 0.4367
          },
          {
            "kra_text": "defect diagnosis and performance tuning",
            "sentence": "DATA SYSTEMS: Reviews existing data systems and architectures to implement the identified areas for improvement and optimization.",
            "similarity": 0.4366
          }
        ],
        "matched_count": null,
        "matched_skills": null,
        "role_id": 82,
        "score": 0.5017,
        "slug": "node-backend-developer",
        "total_count": null
      },
      {
        "display_name": "Java Backend Developer",
        "kra_matches": [
          {
            "kra_text": "persistence and data modeling",
            "sentence": "DATA MODELING: Performs moderately complex data modeling aligned with the datastore technology to ensure sustainable performance and accessibility.",
            "similarity": 0.5691
          },
          {
            "kra_text": "persistence and data modeling",
            "sentence": "DATA FRAMEWORKS: Builds moderately complex prototypes to test new concepts and implements data engineering frameworks and architectures to support the improvement of data processing capabilities and advanced analytics initiatives.",
            "similarity": 0.4831
          },
          {
            "kra_text": "persistence and data modeling",
            "sentence": "DATA FORMATS: Implements appropriate data formats to improve data usability and accessibility across the organization.",
            "similarity": 0.4502
          }
        ],
        "matched_count": null,
        "matched_skills": null,
        "role_id": 79,
        "score": 0.5008,
        "slug": "java-backend-developer",
        "total_count": null
      },
      {
        "display_name": "MLOps Engineer",
        "kra_matches": [
          {
            "kra_text": "Automates ML platform operations including scheduled retraining triggers, pipeline orchestration, evaluation workflows, and alerting configuration.",
            "sentence": "AUTOMATED DEPLOYMENT PIPELINES: Implements automated deployment pipelines to support improving efficiency of code deployments with fit for purpose governance.",
            "similarity": 0.522
          },
          {
            "kra_text": "Coordinates model promotion workflows across development, staging, and production environments including integration testing and data contract validation.",
            "sentence": "DATA MODELING: Performs moderately complex data modeling aligned with the datastore technology to ensure sustainable performance and accessibility.",
            "similarity": 0.5102
          },
          {
            "kra_text": "Validates model performance benchmarks, data schema contracts, and system integration health before signing off on production release readiness.",
            "sentence": "DATA SYSTEMS: Reviews existing data systems and architectures to implement the identified areas for improvement and optimization.",
            "similarity": 0.4575
          }
        ],
        "matched_count": null,
        "matched_skills": null,
        "role_id": 16,
        "score": 0.4966,
        "slug": "ml-ops-engineer",
        "total_count": null
      }
    ],
    "skill_match_roles": [
      {
        "display_name": "Data Engineer",
        "kra_matches": null,
        "matched_count": 1,
        "matched_skills": [
          "Cloud"
        ],
        "role_id": 2,
        "score": 0.0714,
        "slug": "data-engineer",
        "total_count": 14
      },
      {
        "display_name": "ML Engineer",
        "kra_matches": null,
        "matched_count": 1,
        "matched_skills": [
          "Cloud"
        ],
        "role_id": 3,
        "score": 0.0714,
        "slug": "ml-engineer",
        "total_count": 14
      },
      {
        "display_name": "Cyber Security Engineer",
        "kra_matches": null,
        "matched_count": 1,
        "matched_skills": [
          "Cloud"
        ],
        "role_id": 5,
        "score": 0.0714,
        "slug": "cybersecurity-engineer",
        "total_count": 14
      },
      {
        "display_name": "DevOps Engineer",
        "kra_matches": null,
        "matched_count": 1,
        "matched_skills": [
          "Cloud"
        ],
        "role_id": 10,
        "score": 0.0714,
        "slug": "devops-engineer",
        "total_count": 14
      },
      {
        "display_name": "Backend Developer",
        "kra_matches": null,
        "matched_count": 1,
        "matched_skills": [
          "Cloud"
        ],
        "role_id": 1,
        "score": 0.0714,
        "slug": "backend-engineer",
        "total_count": 14
      }
    ]
  },
  "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 0.07 does not contradict",
    "sub_role": null
  },
  "stage5_updates": {
    "centroid_n_after": 474,
    "centroid_updated": true,
    "collision_log_id": null,
    "new_kra_attached": null,
    "new_skills_attached": [
      {
        "is_primary": true,
        "queue_id": 22348,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "Data Engineering",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 22349,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "Streaming",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 22350,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "Batch Processing",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 22351,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "Data Pipelines",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 22352,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "Data Lake",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 22353,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "Data Warehouse",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 22354,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "Data Modeling",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 22355,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "Data Formats",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 22356,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "Data Infrastructure",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 22357,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "Data Engineering Frameworks",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 22358,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "Data Engineering Architectures",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 22359,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "Automated Deployment Pipelines",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 22360,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "Advanced Analytics",
        "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": 2518,
      "existing_alias_text": "Cloud",
      "input_term": "Cloud",
      "matched_canonical": {
        "category_id": 37,
        "display_name": "Cloud",
        "id": 1572,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "CONCEPT",
        "slug": "cloud",
        "sub_category_id": 1177,
        "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": 2017,
      "existing_alias_text": "Data Lakes",
      "input_term": "Data Lake",
      "matched_canonical": {
        "category_id": 1,
        "display_name": "Data Lakes",
        "id": 1358,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "PATTERN",
        "slug": "data-lakes",
        "sub_category_id": 1025,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "matched_via": "embedding_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",
      "input_term": "Data Modeling",
      "matched_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"
      },
      "matched_via": "embedding_alias"
    }
  ],
  "candidate_roles": [
    {
      "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"
    },
    {
      "display_name": "Cloud Architect",
      "id": 9,
      "rationale": null,
      "role_archetype": null,
      "slug": "cloud-architect",
      "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"
    }
  ],
  "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 0.07 does not contradict",
    "role_archetype": null,
    "slug": "data-engineer",
    "source": "db"
  },
  "dimensions": [
    {
      "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"
      },
      "input_skill": "Cloud",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": ".NET Backend Developer",
          "id": 83,
          "rationale": null,
          "role_archetype": "Engineering",
          "slug": "dotnet-backend-developer",
          "source": "db"
        },
        {
          "display_name": "Backend Developer",
          "id": 1,
          "rationale": null,
          "role_archetype": "A Backend Engineer designs, builds, and maintains the server-side logic and data handling that power applications and services. They focus on implementing reliable business functionality, integrating with other systems, and ensuring the backend is scalable, maintainable, and observable.",
          "slug": "backend-engineer",
          "source": "db"
        },
        {
          "display_name": "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"
        }
      ]
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Cloud Storage and Data Services",
        "id": 144,
        "rationale": "Cloud-native storage and managed data services used to place workloads, choose durability tiers, and define platform boundaries. This is a coherent cluster because architects evaluate storage fit, access patterns, and managed service tradeoffs.",
        "slug": "cloud-storage-and-data-services",
        "source": "db"
      },
      "input_skill": "Data Lake",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": "Cloud Architect",
          "id": 9,
          "rationale": null,
          "role_archetype": null,
          "slug": "cloud-architect",
          "source": "db"
        }
      ]
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "React Frontend Development",
        "id": 96,
        "rationale": "Building interactive web user interfaces with React.js, including component composition, state management, hooks, and rendering patterns. React.js belongs here because it is a core library for client-side UI development in modern web applications.",
        "slug": "d_init_01",
        "source": "db"
      },
      "input_skill": "Data Lake",
      "llm_role": null,
      "roles_from_db": []
    },
    {
      "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_final_skills": [
    "Data Engineering",
    "Cloud",
    "Streaming",
    "Batch Processing",
    "Data Pipelines",
    "Data Lake",
    "Data Warehouse",
    "Data Modeling",
    "Data Formats",
    "Data Infrastructure",
    "Data Engineering Frameworks",
    "Data Engineering Architectures",
    "Automated Deployment Pipelines",
    "Advanced Analytics"
  ],
  "input_llm_skills": [
    "Data Engineering",
    "Cloud",
    "Streaming",
    "Batch Processing",
    "Data Pipelines",
    "Data Lake",
    "Data Warehouse",
    "Data Modeling",
    "Data Formats",
    "Data Infrastructure",
    "Data Engineering Frameworks",
    "Data Engineering Architectures",
    "Automated Deployment Pipelines",
    "Advanced Analytics"
  ],
  "new_aliases_persisted": 0,
  "run_id": "50ca08b0-cee7-4e72-a847-aa873bf9908b",
  "skills_detail": [
    {
      "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"
        },
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        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [
        {
          "alias_text": "Cloud",
          "alias_type": "CANONICAL",
          "id": 2518,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        }
      ],
      "canonical": {
        "category_id": 37,
        "display_name": "Cloud",
        "id": 1572,
        "is_also_category": false,
        "is_extractable": true,
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        "slug": "cloud",
        "sub_category_id": 1177,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "dimensions": [
        {
          "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"
          },
          "input_skill": "Cloud",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": ".NET Backend Developer",
              "id": 83,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "dotnet-backend-developer",
              "source": "db"
            },
            {
              "display_name": "Backend Developer",
              "id": 1,
              "rationale": null,
              "role_archetype": "A Backend Engineer designs, builds, and maintains the server-side logic and data handling that power applications and services. They focus on implementing reliable business functionality, integrating with other systems, and ensuring the backend is scalable, maintainable, and observable.",
              "slug": "backend-engineer",
              "source": "db"
            },
            {
              "display_name": "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": "Streaming",
      "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": "streaming",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "Batch Processing",
      "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": "batch-processing",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "Data Pipelines",
      "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-pipelines",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [
        {
          "alias_text": "Data Lakes",
          "alias_type": "CANONICAL",
          "id": 2017,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        }
      ],
      "canonical": {
        "category_id": 1,
        "display_name": "Data Lakes",
        "id": 1358,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "PATTERN",
        "slug": "data-lakes",
        "sub_category_id": 1025,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "dimensions": [
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Cloud Storage and Data Services",
            "id": 144,
            "rationale": "Cloud-native storage and managed data services used to place workloads, choose durability tiers, and define platform boundaries. This is a coherent cluster because architects evaluate storage fit, access patterns, and managed service tradeoffs.",
            "slug": "cloud-storage-and-data-services",
            "source": "db"
          },
          "input_skill": "Data Lake",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "Cloud Architect",
              "id": 9,
              "rationale": null,
              "role_archetype": null,
              "slug": "cloud-architect",
              "source": "db"
            }
          ]
        },
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "React Frontend Development",
            "id": 96,
            "rationale": "Building interactive web user interfaces with React.js, including component composition, state management, hooks, and rendering patterns. React.js belongs here because it is a core library for client-side UI development in modern web applications.",
            "slug": "d_init_01",
            "source": "db"
          },
          "input_skill": "Data Lake",
          "llm_role": null,
          "roles_from_db": []
        }
      ],
      "input_skill": "Data Lake",
      "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 Warehouse",
      "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-warehouse",
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        "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 Formats",
      "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-formats",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "Data Infrastructure",
      "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-infrastructure",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "Data Engineering Frameworks",
      "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-frameworks",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "Data Engineering Architectures",
      "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-architectures",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "Automated Deployment Pipelines",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Data Engineering Tools",
          "skill_nature": "PRACTICE",
          "sub_category": "general",
          "typical_lifespan": "MULTI_YEAR",
          "version_strategy": "UNVERSIONED",
          "volatility": "MEDIUM"
        },
        "enrichment": null,
        "keep_log": [],
        "locked_dimensions": [],
        "merge_log": [],
        "placed": null,
        "relationships": null,
        "skill_id": "automated-deployment-pipelines",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "Advanced Analytics",
      "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": "advanced-analytics",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    }
  ],
  "unmatched_skills": [
    "Data Engineering",
    "Streaming",
    "Batch Processing",
    "Data Pipelines",
    "Data Warehouse",
    "Data Formats",
    "Data Infrastructure",
    "Data Engineering Frameworks",
    "Data Engineering Architectures",
    "Automated Deployment Pipelines",
    "Advanced Analytics"
  ]
}

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 0.07 does not contradict",
    "role_archetype": null,
    "slug": "data-engineer",
    "source": "db"
  },
  "chosen_role_resolution": "in_db",
  "final_input_skills": [
    {
      "skill": "Data Engineering",
      "tag": "new"
    },
    {
      "skill": "Cloud",
      "tag": "in_db"
    },
    {
      "skill": "Streaming",
      "tag": "new"
    },
    {
      "skill": "Batch Processing",
      "tag": "new"
    },
    {
      "skill": "Data Pipelines",
      "tag": "new"
    },
    {
      "skill": "Data Lake",
      "tag": "in_db"
    },
    {
      "skill": "Data Warehouse",
      "tag": "new"
    },
    {
      "skill": "Data Modeling",
      "tag": "in_db"
    },
    {
      "skill": "Data Formats",
      "tag": "new"
    },
    {
      "skill": "Data Infrastructure",
      "tag": "new"
    },
    {
      "skill": "Data Engineering Frameworks",
      "tag": "new"
    },
    {
      "skill": "Data Engineering Architectures",
      "tag": "new"
    },
    {
      "skill": "Automated Deployment Pipelines",
      "tag": "new"
    },
    {
      "skill": "Advanced Analytics",
      "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",
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