Pipeline run

0710d2b4-74dd-4916-ae8a-836268d30811

Pipeline LLM cost (USD)

API 1: $0.0033 API 2: $0.0003 API 3: $0.0000 Total: $0.0036

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 data connectors from diverse sources, defining mappings/transformations and ETL rules, then test, validate, and troubleshoot integrations to keep data accurate, secure, and reliable. Also document standards and support others with self-service integration.

"developing connectors that can extract, transform, and load data effectively"

Tech stack maturity

Mainstream Modern

The role focuses on standard data engineering capabilities like APIs, data mapping, and reconciliation, which are common in current enterprise data stacks rather than legacy-only or bleeding-edge cloud-native patterns.

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 (12)

API Protocol Data Modeling Data Integration ETL Data Mapping Data Transformation Data Validation Data Reconciliation Technical Documentation Data Governance Data Security

Skill cluster (2 dimension groups, role-scoped)

Data Quality and Reconciliation

Data Reconciliation

Cross-cutting / unaligned

API Protocol Data Modeling Data Integration ETL Data Mapping Data Transformation Data Validation Technical Documentation Data Governance Data Security

Show KRA description ↓

• This involves understanding the data models, APIs, and protocols of various data sources and developing connectors that can extract, transform, and load data effectively. • Define data mappings, transformations, and business rules to ensure consistency and accuracy of integrated data. • Identify opportunities to optimize data integration processes, reduce latency, and enhance data quality. • Conduct rigorous testing of data connectors and integration processes to identify and resolve any issues or bugs. Ensure data integrity, reliability, and consistency by performing regular data validation and reconciliation tasks. • Create and maintain technical documentation, including specifications, user guides, and best practices for data integration and connector development. • Share knowledge and provide training to other team members or stakeholders to promote self-service data integration capabilities. • Provide technical support and troubleshooting assistance to resolve integration-related issues. • Adhere to data governance policies, standards, and regulatory requirements while handling sensitive and confidential data. Implement necessary security measures to protect data during integration and transit.

Signals

Skill full-stack-engineer

0.08

Alias data-engineer

1.00

KRA data-engineer

0.67

Post-classification

Centroidupdated · n=87

Alias collision log—

New-role queue—

New skills captured9

New KRA captured—

Captured for admin review

Protocol primary ↔ Data Engineer pending

Data Modeling primary ↔ Data Engineer pending

Data Integration primary ↔ Data Engineer pending

ETL primary ↔ Data Engineer pending

Data Transformation primary ↔ Data Engineer pending

Data Validation primary ↔ Data Engineer pending

Technical Documentation primary ↔ Data Engineer pending

Data Governance primary ↔ Data Engineer pending

Data Security primary ↔ Data Engineer pending

Status: completed Created: 2026-05-27T13:53:12.461903Z Updated: 2026-05-27T13:55:02.837178Z API 3 duration: 7375 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 full-stack-engineer 0.08 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

Position Overview: As a Data Connector Developer, your role is crucial in building and maintaining efficient data integration solutions that connect various data sources and systems within an organization. You will be responsible for designing, developing, and implementing data connectors to enable seamless data flow and integration across different platforms, databases, APIs, and third-party applications. Your expertise in data connectivity and integration will contribute to the organization's overall data strategy and help ensure the availability and accuracy of data for various business functions.

Years of Experience: 2 to 4 years

Key Responsibilities:

• This involves understanding the data models, APIs, and protocols of various data sources and developing connectors that can extract, transform, and load data effectively.
• Define data mappings, transformations, and business rules to ensure consistency and accuracy of integrated data.
• Identify opportunities to optimize data integration processes, reduce latency, and enhance data quality.
• Conduct rigorous testing of data connectors and integration processes to identify and resolve any issues or bugs. Ensure data integrity, reliability, and consistency by performing regular data validation and reconciliation tasks.
• Create and maintain technical documentation, including specifications, user guides, and best practices for data integration and connector development.
• Share knowledge and provide training to other team members or stakeholders to promote self-service data integration capabilities.
• Provide technical support and troubleshooting assistance to resolve integration-related issues.
• Adhere to data governance policies, standards, and regulatory requirements while handling sensitive and confidential data. Implement necessary security measures to protect data during integration and transit.

Qualifications and Skills:

• Bachelor’s degree in computer science, Information Systems, or a related field (or equivalent experience).
• Strong programming skills in languages such as Python/Java, and SQL.
• Proven experience in developing microservices-based applications using languages like Java, Python, or Node.js.
• Strong understanding of microservices architecture, design patterns, and best practices.
• Familiarity with data modelling concepts and experience working with diverse data formats (e.g., JSON, XML, CSV, relational databases).
• Solid understanding of integration patterns, protocols, and APIs (e.g., REST, SOAP, GraphQL).
• Experience with version control systems (e.g., Git) and continuous integration/continuous deployment (CI/CD) practices.
• Excellent problem-solving skills and attention to detail.
• Strong communication and collaboration skills to work effectively with cross-functional teams and stakeholders.
• Familiarity with data governance principles, data security, and compliance standards is a plus.

Skills from this JD

Each row merges API 1 extraction, API 2 library match / v3 orchestration (dimensions + locked dims), and API 3 persistence tags.

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

Canonical: API id=1568 · api

Aliases — catalog

API (CANONICAL)

Context tags (catalog)

API gateway GraphQL JSON OAuth REST SDK SOAP XML authentication endpoint microservices rate limiting throttling versioning webhooks

Stored enrichment (catalog DB)

Category: Concept
Sub-category: Application Programming Interface
Confidence: 0.93
Version strategy: NOT_APPLICABLE

Maturity reasoning: APIs are a core requirement in most software engineering JDs and underpin common integrations across cloud, mobile, and web stacks; major vendors like AWS, Stripe, and Google Cloud center products on API-first usage.

Skill profile (library / DB)

Skill nature: CONCEPT
Volatility: STABLE
Typical lifespan: EVERGREEN
Category id: 2
Sub-category id: 1174
Extractable: True
Also category: False

Dimensions (API 2 worklist)

API Integration and Data Fetching Catalog dimension db id 127

Library dimension (catalog)

Roles linked in library: Angular Frontend Developer, Frontend Developer, Fullstack Developer, React Frontend Developer, Svelte Frontend Developer, Vue Frontend Developer, Web Developer

API 3 link attempts (this skill)

Dimension	Skill↔dim	Role↔dim	Outcome
API Integration and Data Fetching api-integration-and-data-fetching	✓	—	Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)

Protocol 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: Concepts
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 Integration 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

ETL 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

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

Canonical: data mapping id=2487 · data-mapping

Aliases — catalog

data mapping (CANONICAL) primary

Context tags (catalog)

API integration ETL data cleansing data governance data integration data lakes data lineage data migration data modeling data profiling data quality data transformation data visualization data warehouse mapping rules mapping tools metadata management schema mapping

Stored enrichment (catalog DB)

Category: Concept
Sub-category: Data Mapping
Confidence: 0.90
Version strategy: NOT_APPLICABLE

Maturity reasoning: Data mapping appears broadly in ETL/ELT, integration, and MDM job descriptions across BI and cloud data stacks; vendors like Informatica, dbt, and Azure Data Factory all center it as a core capability.

Skill profile (library / DB)

Skill nature: CONCEPT
Volatility: STABLE
Typical lifespan: EVERGREEN
Category id: 2
Sub-category id: 3239
Extractable: True
Also category: False

Dimensions (API 2 worklist)

External System Integrations Catalog dimension db id 14

Library dimension (catalog)

Roles linked in library: .NET Backend Developer, Backend Developer, Drupal Dev, Java Backend Developer, Kotlin Backend Developer, Node.js Backend Developer, PHP Backend Developer, Python Backend Developer, Ruby Backend Developer, Scala Backend Developer

API 3 link attempts (this skill)

Dimension	Skill↔dim	Role↔dim	Outcome
External System Integrations external-system-integrations	✓	—	Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)

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

Canonical: Data Transform id=1890 · data-transform

Aliases — catalog

Data Transform (CANONICAL) primary

Context tags (catalog)

ETL batch processing data aggregation data cleansing data integration data lineage data mapping data modeling data pipeline data quality data visualization data wrangling real-time processing schema evolution transformation logic

Stored enrichment (catalog DB)

Category: Concept
Sub-category: Transformation Concept
Confidence: 0.90
Version strategy: NOT_APPLICABLE

Maturity reasoning: Data transformation is a core ETL/ELT concept and appears broadly across job descriptions for analytics, data engineering, and BI roles; it’s a standard pipeline requirement rather than a niche tool.

Skill profile (library / DB)

Skill nature: CONCEPT
Volatility: STABLE
Typical lifespan: EVERGREEN
Category id: 2
Sub-category id: 1445
Extractable: True
Also category: False

Dimensions (API 2 worklist)

Data Pages and Data Modeling Catalog dimension db id 254

Library dimension (catalog)

Roles linked in library: Pega Developer

API 3 link attempts (this skill)

Dimension	Skill↔dim	Role↔dim	Outcome
Data Pages and Data Modeling data-pages-and-data-modeling	—	—	Skipped — no persistable v3 meta for new skill skill_not_in_db_v3_proposed

Data Validation 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

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

Canonical: Data reconciliation id=133 · data-reconciliation

Aliases — catalog

Data reconciliation (CANONICAL) primary

Context tags (catalog)

ETL audit trail balancing control totals cross-system validation data quality duplicate detection exception handling ledger master data management reconciliation report record matching reference data source-to-target mapping variance analysis

Stored enrichment (catalog DB)

Category: Methodology
Sub-category: Data Validation Methodology
Confidence: 0.88
Version strategy: NOT_APPLICABLE

Maturity reasoning: Common in finance, payments, and ETL JDs; often listed as a core data quality/control requirement alongside SQL and ETL, with many vendor docs and job postings referencing reconciliation workflows.

Skill profile (library / DB)

Skill nature: METHODOLOGY
Volatility: STABLE
Typical lifespan: EVERGREEN
Category id: 8
Sub-category id: 108
Extractable: True
Also category: False

Dimensions (API 2 worklist)

Data Quality and Reconciliation Catalog dimension db id 27

Library dimension (catalog)

Roles linked in library: Data Engineer

API 3 link attempts (this skill)

Dimension	Skill↔dim	Role↔dim	Outcome
Data Quality and Reconciliation data-quality-and-reconciliation	✓	✓	Existing dimension (library) · Role↔dimension saved

Technical Documentation 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: Soft Skills
Sub-category: general
Skill nature: PRACTICE
Volatility: STABLE
Typical lifespan: EVERGREEN
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: PRACTICE
Volatility: MEDIUM
Typical lifespan: MULTI_YEAR
Version strategy: UNVERSIONED

Data Security 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: Security Tools
Sub-category: general
Skill nature: CONCEPT
Volatility: STABLE
Typical lifespan: EVERGREEN
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
API	in_db	API Integration and Data Fetching api-integration-and-data-fetching	✓	—	Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
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
Data Mapping	in_db	External System Integrations external-system-integrations	✓	—	Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Data Transformation	new	Data Pages and Data Modeling data-pages-and-data-modeling	—	—	Skipped — no persistable v3 meta for new skill	skill_not_in_db_v3_proposed
Data Reconciliation	in_db	Data Quality and Reconciliation data-quality-and-reconciliation	✓	✓	Existing dimension (library) · Role↔dimension saved

Library artifacts (this run)

Kind	Detail	DB id
canonical_skill_proposed	Protocol \| type=Concepts subtype=general nature=CONCEPT lifespan=MULTI_YEAR
canonical_skill_proposed	Data Integration \| type=Data Engineering Tools subtype=general nature=PRACTICE lifespan=MULTI_YEAR
canonical_skill_proposed	ETL \| type=Data Engineering Tools subtype=general nature=PRACTICE lifespan=MULTI_YEAR
canonical_skill_proposed	Data Validation \| type=Data Engineering Tools subtype=general nature=PRACTICE lifespan=MULTI_YEAR
canonical_skill_proposed	Technical Documentation \| type=Soft Skills subtype=general nature=PRACTICE lifespan=EVERGREEN
canonical_skill_proposed	Data Governance \| type=Data Engineering Tools subtype=general nature=PRACTICE lifespan=MULTI_YEAR
canonical_skill_proposed	Data Security \| type=Security Tools subtype=general nature=CONCEPT lifespan=EVERGREEN
dimension_skill_link_proposed	Data Modeling ↔ Application Architecture Patterns
dimension_skill_link_proposed	Data Modeling ↔ Service Architecture and Design Patterns
dimension_skill_link_proposed	Data Transformation ↔ Data Pages and Data Modeling

nano JD Parser — gpt-4.1-nano click to toggle

RoleData Connector Developer

Experience2 to 4 years

DomainOther

JD type pass

Show raw JSON

{
  "JD_type": "pass",
  "about_company": null,
  "certifications": [],
  "company_name": null,
  "ctc": null,
  "domain": {
    "primary": {
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      "domain": "Other"
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    "secondary": null
  },
  "education": [
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      "level": "Bachelor\u0027s",
      "qualification": "BTECH/BE - Computer Science (or related)",
      "raw": "Bachelor\u2019s degree in computer science, Information Systems, or a related field (or equivalent experience).",
      "requirement": "required"
    }
  ],
  "experience": {
    "max": 4,
    "min": 2,
    "raw": "2 to 4 years"
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  "job_locations": [],
  "role": "Data Connector Developer",
  "role_aliases": [
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  "role_archetype": "Engineering",
  "roles_and_responsibilities": [
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      "bullet_count": 8,
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      "heading_was_present": true,
      "source_marker": {
        "first_5_words": "\u2022 This involves understanding the",
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      },
      "text": "\u2022 This involves understanding the data models, APIs, and protocols of various data sources and developing connectors that can extract, transform, and load data effectively.\n\u2022 Define data mappings, transformations, and business rules to ensure consistency and accuracy of integrated data.\n\u2022 Identify opportunities to optimize data integration processes, reduce latency, and enhance data quality.\n\u2022 Conduct rigorous testing of data connectors and integration processes to identify and resolve any issues or bugs. Ensure data integrity, reliability, and consistency by performing regular data validation and reconciliation tasks.\n\u2022 Create and maintain technical documentation, including specifications, user guides, and best practices for data integration and connector development.\n\u2022 Share knowledge and provide training to other team members or stakeholders to promote self-service data integration capabilities.\n\u2022 Provide technical support and troubleshooting assistance to resolve integration-related issues.\n\u2022 Adhere to data governance policies, standards, and regulatory requirements while handling sensitive and confidential data. Implement necessary security measures to protect data during integration and transit.",
      "word_count": 202
    }
  ],
  "urls": []
}

API 1 — extract-from-jd click to toggle

{
  "final_skills": [
    {
      "is_primary": true,
      "skill_name": "API"
    },
    {
      "is_primary": true,
      "skill_name": "Protocol"
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      "is_primary": true,
      "skill_name": "Data Modeling"
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    {
      "is_primary": true,
      "skill_name": "Data Integration"
    },
    {
      "is_primary": true,
      "skill_name": "ETL"
    },
    {
      "is_primary": true,
      "skill_name": "Data Mapping"
    },
    {
      "is_primary": true,
      "skill_name": "Data Transformation"
    },
    {
      "is_primary": true,
      "skill_name": "Data Validation"
    },
    {
      "is_primary": true,
      "skill_name": "Data Reconciliation"
    },
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      "skill_name": "Technical Documentation"
    },
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      "is_primary": true,
      "skill_name": "Data Governance"
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      "skill_name": "Data Security"
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      "Data Engineer",
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    "slug": ""
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  "nano_parsed": {
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  "stage3_signals": {
    "alias_found": true,
    "alias_match_roles": [
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        "kra_matches": null,
        "matched_count": null,
        "matched_skills": null,
        "role_id": 2,
        "score": 1.0,
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    "kra_match_roles": [
      {
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        "kra_matches": [
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            "kra_text": "Implements data quality validation rules, reconciliation checks, and anomaly detection to ensure data completeness, accuracy, and consistency.",
            "sentence": "Ensure data integrity, reliability, and consistency by performing regular data validation and reconciliation tasks.",
            "similarity": 0.7629
          },
          {
            "kra_text": "Implements data quality validation rules, reconciliation checks, and anomaly detection to ensure data completeness, accuracy, and consistency.",
            "sentence": "Define data mappings, transformations, and business rules to ensure consistency and accuracy of integrated data.",
            "similarity": 0.6372
          },
          {
            "kra_text": "Works with data analysts, data scientists, and business stakeholders to define data models, ingestion schedules, and data delivery requirements.",
            "sentence": "This involves understanding the data models, APIs, and protocols of various data sources and developing connectors that can extract, transform, and load data effectively.",
            "similarity": 0.6209
          }
        ],
        "matched_count": null,
        "matched_skills": null,
        "role_id": 2,
        "score": 0.6737,
        "slug": "data-engineer",
        "total_count": null
      },
      {
        "display_name": "MLOps Engineer",
        "kra_matches": [
          {
            "kra_text": "Validates model performance benchmarks, data schema contracts, and system integration health before signing off on production release readiness.",
            "sentence": "Ensure data integrity, reliability, and consistency by performing regular data validation and reconciliation tasks.",
            "similarity": 0.5797
          },
          {
            "kra_text": "Validates model performance benchmarks, data schema contracts, and system integration health before signing off on production release readiness.",
            "sentence": "Conduct rigorous testing of data connectors and integration processes to identify and resolve any issues or bugs.",
            "similarity": 0.5763
          },
          {
            "kra_text": "Supports ML platform incidents by diagnosing model serving failures, feature store pipeline breaks, and training environment configuration issues.",
            "sentence": "Provide technical support and troubleshooting assistance to resolve integration-related issues.",
            "similarity": 0.4731
          }
        ],
        "matched_count": null,
        "matched_skills": null,
        "role_id": 16,
        "score": 0.5431,
        "slug": "ml-ops-engineer",
        "total_count": null
      },
      {
        "display_name": "Svelte Frontend Developer",
        "kra_matches": [
          {
            "kra_text": "backend data integration",
            "sentence": "Implement necessary security measures to protect data during integration and transit.",
            "similarity": 0.5382
          },
          {
            "kra_text": "backend data integration",
            "sentence": "Identify opportunities to optimize data integration processes, reduce latency, and enhance data quality.",
            "similarity": 0.5294
          },
          {
            "kra_text": "backend data integration",
            "sentence": "This involves understanding the data models, APIs, and protocols of various data sources and developing connectors that can extract, transform, and load data effectively.",
            "similarity": 0.4962
          }
        ],
        "matched_count": null,
        "matched_skills": null,
        "role_id": 92,
        "score": 0.5213,
        "slug": "svelte-frontend-developer",
        "total_count": null
      },
      {
        "display_name": "Backend Developer",
        "kra_matches": [
          {
            "kra_text": "Investigates and resolves production incidents, API bugs, and service degradation through root cause analysis, hotfixes, and post-mortems.",
            "sentence": "Provide technical support and troubleshooting assistance to resolve integration-related issues.",
            "similarity": 0.5044
          },
          {
            "kra_text": "Identifies and resolves backend performance bottlenecks through query optimization, indexing strategies, connection pooling, and distributed caching with Redis.",
            "sentence": "Identify opportunities to optimize data integration processes, reduce latency, and enhance data quality.",
            "similarity": 0.4958
          },
          {
            "kra_text": "Investigates and resolves production incidents, API bugs, and service degradation through root cause analysis, hotfixes, and post-mortems.",
            "sentence": "Conduct rigorous testing of data connectors and integration processes to identify and resolve any issues or bugs.",
            "similarity": 0.4902
          }
        ],
        "matched_count": null,
        "matched_skills": null,
        "role_id": 1,
        "score": 0.4968,
        "slug": "backend-engineer",
        "total_count": null
      },
      {
        "display_name": "Pega Developer",
        "kra_matches": [
          {
            "kra_text": "data capture and validation logic",
            "sentence": "Ensure data integrity, reliability, and consistency by performing regular data validation and reconciliation tasks.",
            "similarity": 0.4985
          },
          {
            "kra_text": "external system integration implementation",
            "sentence": "Implement necessary security measures to protect data during integration and transit.",
            "similarity": 0.4966
          },
          {
            "kra_text": "defect troubleshooting and resolution",
            "sentence": "Provide technical support and troubleshooting assistance to resolve integration-related issues.",
            "similarity": 0.4863
          }
        ],
        "matched_count": null,
        "matched_skills": null,
        "role_id": 24,
        "score": 0.4938,
        "slug": "pega-developer",
        "total_count": null
      }
    ],
    "skill_match_roles": [
      {
        "display_name": "Fullstack Developer",
        "kra_matches": null,
        "matched_count": 1,
        "matched_skills": [
          "API"
        ],
        "role_id": 15,
        "score": 0.0833,
        "slug": "full-stack-engineer",
        "total_count": 12
      },
      {
        "display_name": "Data Engineer",
        "kra_matches": null,
        "matched_count": 1,
        "matched_skills": [
          "Data reconciliation"
        ],
        "role_id": 2,
        "score": 0.0833,
        "slug": "data-engineer",
        "total_count": 12
      },
      {
        "display_name": "Frontend Developer",
        "kra_matches": null,
        "matched_count": 1,
        "matched_skills": [
          "API"
        ],
        "role_id": 7,
        "score": 0.0833,
        "slug": "frontend-engineer",
        "total_count": 12
      },
      {
        "display_name": "Web Developer",
        "kra_matches": null,
        "matched_count": 1,
        "matched_skills": [
          "API"
        ],
        "role_id": 25,
        "score": 0.0833,
        "slug": "web-developer",
        "total_count": 12
      },
      {
        "display_name": "Backend Developer",
        "kra_matches": null,
        "matched_count": 1,
        "matched_skills": [
          "data mapping"
        ],
        "role_id": 1,
        "score": 0.0833,
        "slug": "backend-engineer",
        "total_count": 12
      }
    ]
  },
  "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 full-stack-engineer 0.08 does not contradict",
    "sub_role": null
  },
  "stage5_updates": {
    "centroid_n_after": 87,
    "centroid_updated": true,
    "collision_log_id": null,
    "new_kra_attached": null,
    "new_skills_attached": [
      {
        "is_primary": true,
        "queue_id": 5447,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "Protocol",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 5448,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "Data Modeling",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 5449,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "Data Integration",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 5450,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "ETL",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 5451,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "Data Transformation",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 5452,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "Data Validation",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 5453,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "Technical Documentation",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 5454,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "Data Governance",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 5455,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "Data Security",
        "status": "pending"
      }
    ],
    "queue_entry_id": null,
    "v3_pipeline_triggered": false,
    "v3_role_slug": null,
    "v3_run_id": null
  }
}

API 2 — extract-details

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      "alias_persisted": false,
      "existing_alias_id": 2514,
      "existing_alias_text": "API",
      "input_term": "API",
      "matched_canonical": {
        "category_id": 2,
        "display_name": "API",
        "id": 1568,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "CONCEPT",
        "slug": "api",
        "sub_category_id": 1174,
        "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",
      "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"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 3824,
      "existing_alias_text": "data mapping",
      "input_term": "Data Mapping",
      "matched_canonical": {
        "category_id": 2,
        "display_name": "data mapping",
        "id": 2487,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "CONCEPT",
        "slug": "data-mapping",
        "sub_category_id": 3239,
        "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": 2894,
      "existing_alias_text": "Data Transform",
      "input_term": "Data Transformation",
      "matched_canonical": {
        "category_id": 2,
        "display_name": "Data Transform",
        "id": 1890,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "CONCEPT",
        "slug": "data-transform",
        "sub_category_id": 1445,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "matched_via": "embedding_alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 337,
      "existing_alias_text": "Data reconciliation",
      "input_term": "Data Reconciliation",
      "matched_canonical": {
        "category_id": 8,
        "display_name": "Data reconciliation",
        "id": 133,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "METHODOLOGY",
        "slug": "data-reconciliation",
        "sub_category_id": 108,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "matched_via": "alias"
    }
  ],
  "candidate_roles": [
    {
      "display_name": "Angular Frontend Developer",
      "id": 90,
      "rationale": null,
      "role_archetype": "Engineering",
      "slug": "angular-frontend-developer",
      "source": "db"
    },
    {
      "display_name": "Frontend Developer",
      "id": 7,
      "rationale": null,
      "role_archetype": null,
      "slug": "frontend-engineer",
      "source": "db"
    },
    {
      "display_name": "Fullstack Developer",
      "id": 15,
      "rationale": null,
      "role_archetype": null,
      "slug": "full-stack-engineer",
      "source": "db"
    },
    {
      "display_name": "React Frontend Developer",
      "id": 89,
      "rationale": null,
      "role_archetype": "Engineering",
      "slug": "react-frontend-developer",
      "source": "db"
    },
    {
      "display_name": "Svelte Frontend Developer",
      "id": 92,
      "rationale": null,
      "role_archetype": "Engineering",
      "slug": "svelte-frontend-developer",
      "source": "db"
    },
    {
      "display_name": "Vue Frontend Developer",
      "id": 91,
      "rationale": null,
      "role_archetype": "Engineering",
      "slug": "vue-frontend-developer",
      "source": "db"
    },
    {
      "display_name": "Web Developer",
      "id": 25,
      "rationale": null,
      "role_archetype": null,
      "slug": "web-developer",
      "source": "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"
    },
    {
      "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"
    },
    {
      "display_name": "Drupal Dev",
      "id": 228,
      "rationale": null,
      "role_archetype": "Engineering",
      "slug": "drupal-dev",
      "source": "db"
    },
    {
      "display_name": "Pega Developer",
      "id": 24,
      "rationale": null,
      "role_archetype": null,
      "slug": "pega-developer",
      "source": "db"
    },
    {
      "display_name": "Data Engineer",
      "id": 2,
      "rationale": null,
      "role_archetype": null,
      "slug": "data-engineer",
      "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 full-stack-engineer 0.08 does not contradict",
    "role_archetype": null,
    "slug": "data-engineer",
    "source": "db"
  },
  "dimensions": [
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "API Integration and Data Fetching",
        "id": 127,
        "rationale": "Client-side integration with backend endpoints and third-party services, including request shaping, response handling, and synchronization with UI state. This is central to frontend work because most screens depend on remote data.",
        "slug": "api-integration-and-data-fetching",
        "source": "db"
      },
      "input_skill": "API",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": "Angular Frontend Developer",
          "id": 90,
          "rationale": null,
          "role_archetype": "Engineering",
          "slug": "angular-frontend-developer",
          "source": "db"
        },
        {
          "display_name": "Frontend Developer",
          "id": 7,
          "rationale": null,
          "role_archetype": null,
          "slug": "frontend-engineer",
          "source": "db"
        },
        {
          "display_name": "Fullstack Developer",
          "id": 15,
          "rationale": null,
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          "slug": "full-stack-engineer",
          "source": "db"
        },
        {
          "display_name": "React Frontend Developer",
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          "rationale": null,
          "role_archetype": "Engineering",
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          "source": "db"
        },
        {
          "display_name": "Svelte Frontend Developer",
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          "rationale": null,
          "role_archetype": "Engineering",
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          "source": "db"
        },
        {
          "display_name": "Vue Frontend Developer",
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          "rationale": null,
          "role_archetype": "Engineering",
          "slug": "vue-frontend-developer",
          "source": "db"
        },
        {
          "display_name": "Web Developer",
          "id": 25,
          "rationale": null,
          "role_archetype": null,
          "slug": "web-developer",
          "source": "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",
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          "rationale": null,
          "role_archetype": "Engineering",
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        },
        {
          "display_name": "Python Backend Developer",
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          "rationale": null,
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      ]
    },
    {
      "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": [
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          "source": "db"
        },
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          "rationale": null,
          "role_archetype": "Engineering",
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        },
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        },
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        {
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      ]
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
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        "rationale": "Connecting backend services to third-party APIs and internal enterprise systems. This includes client libraries, webhooks, retries, data mapping, and integration failure handling.",
        "slug": "external-system-integrations",
        "source": "db"
      },
      "input_skill": "Data Mapping",
      "llm_role": null,
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        },
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          "rationale": null,
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        },
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        },
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          "role_archetype": "Engineering",
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        },
        {
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        },
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      ]
    },
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      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Data Pages and Data Modeling",
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        "rationale": "Defines how Pega applications source, shape, and expose data for cases and UI components. This includes declarative data access, parameterized data pages, and the data objects used to support process execution.",
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        "source": "db"
      },
      "input_skill": "Data Transformation",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": "Pega Developer",
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          "dimension": {
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          ]
        }
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      ],
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          },
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    },
    {
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      "was_in_llm_skills": true
    },
    {
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      "input_skill": "Data Security",
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      "new_alias_persisted": false,
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      "new_skill_meta": {
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      },
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      "was_in_llm_skills": true
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  ],
  "unmatched_skills": [
    "Protocol",
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    "Data Validation",
    "Technical Documentation",
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    "Data Security"
  ]
}

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 full-stack-engineer 0.08 does not contradict",
    "role_archetype": null,
    "slug": "data-engineer",
    "source": "db"
  },
  "chosen_role_resolution": "in_db",
  "final_input_skills": [
    {
      "skill": "API",
      "tag": "in_db"
    },
    {
      "skill": "Protocol",
      "tag": "new"
    },
    {
      "skill": "Data Modeling",
      "tag": "in_db"
    },
    {
      "skill": "Data Integration",
      "tag": "new"
    },
    {
      "skill": "ETL",
      "tag": "new"
    },
    {
      "skill": "Data Mapping",
      "tag": "in_db"
    },
    {
      "skill": "Data Transformation",
      "tag": "in_db"
    },
    {
      "skill": "Data Validation",
      "tag": "new"
    },
    {
      "skill": "Data Reconciliation",
      "tag": "in_db"
    },
    {
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}

LLM Calls

Every model call made for this run, in pipeline order. Click a card to see the model's response.

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