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Pipeline run

4f3db78c-1948-496b-ad86-aaf3919e8fa9

Pipeline LLM cost (USD)
API 1: $0.0028 API 2: $0.0006 API 3: $0.0000 Total: $0.0033

Client output enrichment

v2 Skill cluster · Nature of work · AI index · Tech stack maturity · Evidence · KRA description
SPARSE JD role baseline loaded sources · ai_index: role_baseline · nature_of_work: no_kras · tech_stack_maturity: jd
Nature of work no kras
Vague JD — no KRAs present to derive a specific nature of work.
Tech stack maturity
Mainstream Modern
AI index (0 = no AI use, 5 = totally AI-dependent · v2.1)
1.20 / 5
· Title match
· Has AI skill
· AI skill (primary)
· AI skill (secondary)
· On AI team
· Builds AI products
vocab breakdown (legacy)
Assistants (×1):
Frameworks (×2):
Models / concepts (×3):
Evidence — skills matched in JD (10)
Snowflake Airflow ETL JSON DAG Data Validation Unit Testing QA SQL Data Warehousing
Skill cluster (4 dimension groups, role-scoped)
API Interface and Contract Design
JSON
Cloud Data Warehouses
Snowflake
Programming Languages for Data Work
SQL
Cross-cutting / unaligned
Airflow ETL DAG Data Validation Unit Testing QA Data Warehousing

Signals

Skill pega-developer
0.20
Alias
KRA data-engineer
0.59
Status: completed Created: 2026-06-03T22:54:05.754262Z Updated: 2026-06-07T08:00:22.682925Z API 3 duration: 112 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 E

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

The primary skills strongly align with the responsibilities of a Data Engineer.

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

0
New skills
0
Skill↔dim saved
0
Role↔dim saved
1
Skipped

Job description

Responsibilities:

Designed and developed Snowflake Cloud Data Warehouse solutions.
Built ETL pipelines using Airflow to ingest JSON and variant data.
Created database objects including tables, views, and streams in Snowflake. Developed scripts to transform and load data from staging to DWH layers Processed semi-structured (JSON/Variant) data into structured DWH models. Designed and implemented DAG workflows for reporting pipelines. Performed data validation, unit testing, and QA code deployment.
Optimized queries and improved reporting performance.
Supported reporting and analytics teams with accurate data delivery. Resolved production issues and improved system reliability.

Skills from this JD

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

Snowflake Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Snowflake id=105 · snowflake

Aliases — catalog

  • Snowflake (CANONICAL) primary

Context tags (catalog)

ELT ETL SQL Snowpark Snowpipe Streams Tasks Time Travel VARIANT data sharing data warehouse dbt semi-structured data virtual warehouse zero-copy cloning

Stored enrichment (catalog DB)

Category
Platform
Sub-category
Data Cloud Platform
Vendor
Snowflake Inc.
License
proprietary
Year introduced
2012
Confidence
0.98
Version strategy
NOT_APPLICABLE

Maturity reasoning: Snowflake appears frequently in data/analytics job postings and is a standard cloud data warehouse platform alongside BigQuery and Redshift.

Skill profile (library / DB)

Skill nature
PLATFORM
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
9
Sub-category id
113
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Cloud Data Warehouses Catalog dimension db id 22

    Library dimension (catalog)

    Roles linked in library: Data Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Cloud Data Warehouses
cloud-data-warehouses
Existing dimension (library) · Role↔dimension saved
Airflow Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Airflow id=265 · airflow

Aliases — catalog

  • Airflow (CANONICAL) primary
  • airflow 2 (VERSION)
  • airflow-2 (VERSION)
  • airflow2 (VERSION)
  • airflow2.x (VERSION)
  • apache airflow 2 (VERSION)

Context tags (catalog)

Apache Celery CeleryExecutor DAG ETL Executor Jinja templating Python SLA Sensors UI XCom backfill connections data pipeline executor hooks logging monitoring operators plugins scheduler task dependencies task instance variables

Stored enrichment (catalog DB)

Category
Tool
Sub-category
Workflow Orchestration Tool
Vendor
Apache Software Foundation
License
apache_2
Year introduced
2014
Confidence
0.95
Version strategy
SEPARATE_ENTITY
Version tag
2.x

Maturity reasoning: Apache Airflow appears in many data engineering job postings and is a common orchestration choice in production stacks; its GitHub activity and ecosystem remain strong, with no vendor sunset or clear replacement dominating JDs.

Skill profile (library / DB)

Skill nature
TOOL
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
13
Sub-category id
130
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Workflow Orchestration for ML Pipelines Catalog dimension db id 54

    Library dimension (catalog)

    Roles linked in library: ML Engineer, MLOps Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Workflow Orchestration for ML Pipelines
workflow-orchestration-for-ml-pipelines
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
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
JSON Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: JSON id=1984 · json

Aliases — catalog

  • JSON (CANONICAL) primary

Context tags (catalog)

AJAX API JSON Schema JSON-LD JSONP JavaScript NoSQL REST configuration data binding data format data interchange data structure deserialization interoperability key-value pairs lightweight object notation schema serialization text-based

Stored enrichment (catalog DB)

Category
Format
Sub-category
Data Interchange Format
Confidence
0.99
Version strategy
NOT_APPLICABLE

Maturity reasoning: JSON is a default data interchange format in APIs and web stacks; it appears in a very high volume of job descriptions and is supported by every major language/runtime.

Skill profile (library / DB)

Skill nature
STANDARD
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
4
Sub-category id
1457
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, Fullstack Developer, React Frontend Developer, Svelte Frontend Developer, Vue Frontend Developer, Web Developer

  • API Interface and Contract Design Catalog dimension db id 289

    Library dimension (catalog)

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

  • Integration Protocols & Standards Catalog dimension db id 271

    Library dimension (catalog)

    Roles linked in library: Pega 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)
API Interface and Contract Design
api-interface-and-contract-design
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Integration Protocols & Standards
integration-protocols-standards
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
DAG Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: DAG design id=1557 · dag-design

Aliases — catalog

  • DAG design (CANONICAL) primary

Context tags (catalog)

Airflow ETL Luigi data lineage data pipeline dependency graph event-driven execution plan job monitoring orchestration parallel processing resource allocation scalability task scheduling workflow

Stored enrichment (catalog DB)

Category
Architecture
Sub-category
Dag Based Architecture
Confidence
0.86
Version strategy
NOT_APPLICABLE

Maturity reasoning: Common in JDs for Airflow, dbt, and data engineering roles; DAG-based orchestration is a standard pattern for pipelines and workflow scheduling.

Skill profile (library / DB)

Skill nature
PATTERN
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
1
Sub-category id
1113
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Workflow Orchestration for ML Pipelines Catalog dimension db id 54

    Library dimension (catalog)

    Roles linked in library: ML Engineer, MLOps Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Workflow Orchestration for ML Pipelines
workflow-orchestration-for-ml-pipelines
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
Unit Testing Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Unit Testing id=517 · unit-testing

Aliases — catalog

  • Unit Testing (CANONICAL)

Context tags (catalog)

JUnit NUnit TDD arrange-act-assert assertions code coverage fixtures mocking pytest regression stubs test cases test doubles test runner xUnit

Stored enrichment (catalog DB)

Category
Methodology
Sub-category
Testing Methodology
Confidence
0.98
Version strategy
NOT_APPLICABLE

Maturity reasoning: Unit testing is a standard hiring requirement across software JDs and appears in mainstream frameworks/docs; GitHub and Stack Overflow usage remain consistently high, with no successor replacing it.

Skill profile (library / DB)

Skill nature
METHODOLOGY
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
8
Sub-category id
44
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • React Frontend Development Catalog dimension db id 96

    Library dimension (catalog)

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
React Frontend Development
d_init_01
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
QA 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
Testing Tools
Sub-category
general
Skill nature
PRACTICE
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
SQL Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: SQL id=101 · sql

Aliases — catalog

  • SQL (CANONICAL) primary

Context tags (catalog)

ACID CTE DDL DML ETL JOIN MySQL NoSQL OLAP ORM PostgreSQL SQL injection SQLite T-SQL data modeling data warehousing database normalization execution plan indexing joins normalization query optimization stored procedures subquery transaction isolation transaction management window functions

Stored enrichment (catalog DB)

Category
Language
Sub-category
Query Language
Vendor
ANSI
License
unknown
Year introduced
1974
Confidence
0.99
Version strategy
NOT_APPLICABLE

Maturity reasoning: SQL appears in a large share of data, backend, and analytics job descriptions and remains the default query language for PostgreSQL, MySQL, and cloud warehouses like Snowflake/BigQuery.

Skill profile (library / DB)

Skill nature
LANGUAGE
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
6
Sub-category id
97
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Pega Programming Languages & DSLs Catalog dimension db id 267

    Library dimension (catalog)

    Roles linked in library: Pega Developer

  • Programming Languages & DSLs Catalog dimension db id 475

    Library dimension (catalog)

    Roles linked in library: Engineering Manager

  • Programming Languages for Data Work Catalog dimension db id 21

    Library dimension (catalog)

    Roles linked in library: Data Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Pega Programming Languages & DSLs
pega-programming-languages-dsls
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Programming Languages & DSLs
programming-languages-dsls
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Programming Languages for Data Work
programming-languages-for-data-work
Existing dimension (library) · Role↔dimension saved
Data Warehousing 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
Snowflake in_db
Cloud Data Warehouses
cloud-data-warehouses
Existing dimension (library) · Role↔dimension saved
Airflow in_db
Workflow Orchestration for ML Pipelines
workflow-orchestration-for-ml-pipelines
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
JSON in_db
API Integration and Data Fetching
api-integration-and-data-fetching
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
JSON in_db
API Interface and Contract Design
api-interface-and-contract-design
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
JSON in_db
Integration Protocols & Standards
integration-protocols-standards
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
DAG new
Workflow Orchestration for ML Pipelines
workflow-orchestration-for-ml-pipelines
Skipped — no persistable v3 meta for new skill skill_not_in_db_v3_proposed
Unit Testing in_db
React Frontend Development
d_init_01
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
SQL in_db
Pega Programming Languages & DSLs
pega-programming-languages-dsls
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
SQL in_db
Programming Languages & DSLs
programming-languages-dsls
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
SQL in_db
Programming Languages for Data Work
programming-languages-for-data-work
Existing dimension (library) · Role↔dimension saved

Library artifacts (this run)

Kind Detail DB id
canonical_skill_proposed ETL | type=Data Engineering Tools subtype=general nature=PRACTICE lifespan=MULTI_YEAR
canonical_skill_proposed Data Validation | type=Data Engineering Tools subtype=general nature=PRACTICE lifespan=MULTI_YEAR
canonical_skill_proposed QA | type=Testing Tools subtype=general nature=PRACTICE lifespan=MULTI_YEAR
canonical_skill_proposed Data Warehousing | type=Data Engineering Tools subtype=general nature=CONCEPT lifespan=MULTI_YEAR
dimension_skill_link_proposed DAG ↔ Workflow Orchestration for ML Pipelines
nano JD Parser — gpt-4.1-nano click to toggle
JD type fail
Show raw JSON
{
  "JD_type": "fail",
  "archetype_override_applied": true,
  "archetype_override_matched_skills": [
    "Snowflake",
    "Unit Testing",
    "staging",
    "production",
    "Analytics",
    "Airflow",
    "Cloud",
    "Models",
    "Streams",
    "Views",
    "JSON"
  ],
  "role_archetype": "Engineering"
}
API 1 — extract-from-jd click to toggle
{
  "final_skills": [
    {
      "is_primary": true,
      "skill_name": "Snowflake"
    },
    {
      "is_primary": true,
      "skill_name": "Airflow"
    },
    {
      "is_primary": true,
      "skill_name": "ETL"
    },
    {
      "is_primary": true,
      "skill_name": "JSON"
    },
    {
      "is_primary": true,
      "skill_name": "DAG"
    },
    {
      "is_primary": true,
      "skill_name": "Data Validation"
    },
    {
      "is_primary": true,
      "skill_name": "Unit Testing"
    },
    {
      "is_primary": true,
      "skill_name": "QA"
    },
    {
      "is_primary": true,
      "skill_name": "SQL"
    },
    {
      "is_primary": true,
      "skill_name": "Data Warehousing"
    }
  ],
  "jd_role": null,
  "nano_parsed": {
    "JD_type": "fail",
    "archetype_override_applied": true,
    "archetype_override_matched_skills": [
      "Snowflake",
      "Unit Testing",
      "staging",
      "production",
      "Analytics",
      "Airflow",
      "Cloud",
      "Models",
      "Streams",
      "Views",
      "JSON"
    ],
    "role_archetype": "Engineering"
  },
  "rejected": false,
  "rejection_reason": null,
  "run_id": "4f3db78c-1948-496b-ad86-aaf3919e8fa9",
  "stage3_signals": {
    "alias_found": false,
    "alias_match_roles": [],
    "kra_match_roles": [
      {
        "display_name": "Data Engineer",
        "kra_matches": [
          {
            "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": "Developed scripts to transform and load data from staging to DWH layers Processed semi-structured (JSON/Variant) data into structured DWH models.",
            "similarity": 0.5941
          },
          {
            "kra_text": "Optimizes pipeline throughput, partitioning strategies, and query performance across cloud data warehouses like Snowflake, BigQuery, or Redshift.",
            "sentence": "Designed and developed Snowflake Cloud Data Warehouse solutions.",
            "similarity": 0.5882
          },
          {
            "kra_text": "Works with data analysts, data scientists, and business stakeholders to define data models, ingestion schedules, and data delivery requirements.",
            "sentence": "Supported reporting and analytics teams with accurate data delivery.",
            "similarity": 0.5801
          }
        ],
        "matched_count": null,
        "matched_skills": null,
        "role_id": 2,
        "score": 0.5875,
        "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": "Performed data validation, unit testing, and QA code deployment.",
            "similarity": 0.6061
          },
          {
            "kra_text": "Coordinates model promotion workflows across development, staging, and production environments including integration testing and data contract validation.",
            "sentence": "Developed scripts to transform and load data from staging to DWH layers Processed semi-structured (JSON/Variant) data into structured DWH models.",
            "similarity": 0.5058
          },
          {
            "kra_text": "Maintains model versioning, experiment lineage, and artifact tracking using MLflow, DVC, or Weights \u0026 Biases for reproducibility and auditability.",
            "sentence": "Built ETL pipelines using Airflow to ingest JSON and variant data.",
            "similarity": 0.4699
          }
        ],
        "matched_count": null,
        "matched_skills": null,
        "role_id": 16,
        "score": 0.5273,
        "slug": "ml-ops-engineer",
        "total_count": null
      },
      {
        "display_name": "Ruby Backend Developer",
        "kra_matches": [
          {
            "kra_text": "performance and reliability improvements",
            "sentence": "Resolved production issues and improved system reliability.",
            "similarity": 0.6138
          },
          {
            "kra_text": "performance and reliability improvements",
            "sentence": "Optimized queries and improved reporting performance.",
            "similarity": 0.5174
          },
          {
            "kra_text": "automated backend checks",
            "sentence": "Performed data validation, unit testing, and QA code deployment.",
            "similarity": 0.4308
          }
        ],
        "matched_count": null,
        "matched_skills": null,
        "role_id": 85,
        "score": 0.5207,
        "slug": "ruby-backend-developer",
        "total_count": null
      },
      {
        "display_name": "AI Engineer",
        "kra_matches": [
          {
            "kra_text": "Designs and implements prompt engineering workflows, few-shot examples, chain-of-thought patterns, and structured output parsing for AI feature pipelines.",
            "sentence": "Designed and implemented DAG workflows for reporting pipelines.",
            "similarity": 0.5648
          },
          {
            "kra_text": "Defines evaluation frameworks, automated test suites, and human feedback loops to measure AI feature quality, accuracy, and consistency.",
            "sentence": "Performed data validation, unit testing, and QA code deployment.",
            "similarity": 0.4643
          },
          {
            "kra_text": "Optimizes AI pipeline efficiency by tuning model selection, context window usage, prompt caching, and batching strategies to reduce cost and latency.",
            "sentence": "Optimized queries and improved reporting performance.",
            "similarity": 0.4492
          }
        ],
        "matched_count": null,
        "matched_skills": null,
        "role_id": 13,
        "score": 0.4928,
        "slug": "ai-engineer",
        "total_count": null
      },
      {
        "display_name": "Java Backend Developer",
        "kra_matches": [
          {
            "kra_text": "backend performance tuning",
            "sentence": "Optimized queries and improved reporting performance.",
            "similarity": 0.5301
          },
          {
            "kra_text": "code refactoring and defect fixes",
            "sentence": "Performed data validation, unit testing, and QA code deployment.",
            "similarity": 0.4948
          },
          {
            "kra_text": "persistence and data modeling",
            "sentence": "Developed scripts to transform and load data from staging to DWH layers Processed semi-structured (JSON/Variant) data into structured DWH models.",
            "similarity": 0.4498
          }
        ],
        "matched_count": null,
        "matched_skills": null,
        "role_id": 79,
        "score": 0.4916,
        "slug": "java-backend-developer",
        "total_count": null
      }
    ],
    "skill_match_roles": [
      {
        "display_name": "Pega Developer",
        "kra_matches": null,
        "matched_count": 2,
        "matched_skills": [
          "JSON",
          "SQL"
        ],
        "role_id": 24,
        "score": 0.2,
        "slug": "pega-developer",
        "total_count": 10
      },
      {
        "display_name": "Data Engineer",
        "kra_matches": null,
        "matched_count": 2,
        "matched_skills": [
          "SQL",
          "Snowflake"
        ],
        "role_id": 2,
        "score": 0.2,
        "slug": "data-engineer",
        "total_count": 10
      },
      {
        "display_name": "Fullstack Developer",
        "kra_matches": null,
        "matched_count": 1,
        "matched_skills": [
          "JSON"
        ],
        "role_id": 15,
        "score": 0.1,
        "slug": "full-stack-engineer",
        "total_count": 10
      },
      {
        "display_name": "Frontend Developer",
        "kra_matches": null,
        "matched_count": 1,
        "matched_skills": [
          "JSON"
        ],
        "role_id": 7,
        "score": 0.1,
        "slug": "frontend-engineer",
        "total_count": 10
      },
      {
        "display_name": "MLOps Engineer",
        "kra_matches": null,
        "matched_count": 1,
        "matched_skills": [
          "Airflow"
        ],
        "role_id": 16,
        "score": 0.1,
        "slug": "ml-ops-engineer",
        "total_count": 10
      }
    ]
  },
  "stage4_decision": {
    "alias_collision_detected": false,
    "case": "E",
    "chosen_role": null,
    "confidence": 0.0,
    "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": true,
    "reasoning": "Stage 4 crashed: FileNotFoundError: [Errno 2] No such file or directory: \u0027/app/domain_role_classifier_prompt.md\u0027",
    "sub_role": null
  },
  "stage5_updates": 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": 299,
      "existing_alias_text": "Snowflake",
      "input_term": "Snowflake",
      "matched_canonical": {
        "category_id": 9,
        "display_name": "Snowflake",
        "id": 105,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "PLATFORM",
        "slug": "snowflake",
        "sub_category_id": 113,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 526,
      "existing_alias_text": "Airflow",
      "input_term": "Airflow",
      "matched_canonical": {
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          "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": "etl",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [
        {
          "alias_text": "JSON",
          "alias_type": "CANONICAL",
          "id": 3018,
          "is_primary": true,
          "match_strategy": "CASE_INSENSITIVE"
        }
      ],
      "canonical": {
        "category_id": 4,
        "display_name": "JSON",
        "id": 1984,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "STANDARD",
        "slug": "json",
        "sub_category_id": 1457,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "dimensions": [
        {
          "dimension": {
            "difficulty_hint": "well_known",
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            "source": "db"
          },
          "input_skill": "JSON",
          "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": 435,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "fullstack-developer",
              "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"
            }
          ]
        },
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "API Interface and Contract Design",
            "id": 289,
            "rationale": "Designing backend service interfaces and contracts that other systems consume, including endpoint and operation shape, request/response payloads, schema and validation, pagination, filtering, idempotency, versioning, status codes, and backward compatibility across REST, GraphQL, gRPC, and OpenAPI-based APIs.",
            "slug": "api-interface-and-contract-design",
            "source": "db"
          },
          "input_skill": "JSON",
          "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": "Go Backend Developer",
              "id": 81,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "go-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": "Python Backend Developer",
              "id": 80,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "python-backend-developer",
              "source": "db"
            },
            {
              "display_name": "Ruby Backend Developer",
              "id": 85,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "ruby-backend-developer",
              "source": "db"
            },
            {
              "display_name": "Scala Backend Developer",
              "id": 87,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "scala-backend-developer",
              "source": "db"
            }
          ]
        },
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Integration Protocols \u0026 Standards",
            "id": 271,
            "rationale": "Standards and protocols for integrating Pega applications.",
            "slug": "integration-protocols-standards",
            "source": "db"
          },
          "input_skill": "JSON",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "Pega Developer",
              "id": 24,
              "rationale": null,
              "role_archetype": null,
              "slug": "pega-developer",
              "source": "db"
            }
          ]
        }
      ],
      "input_skill": "JSON",
      "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": [
        {
          "alias_text": "DAG design",
          "alias_type": "CANONICAL",
          "id": 2484,
          "is_primary": true,
          "match_strategy": "CASE_INSENSITIVE"
        }
      ],
      "canonical": {
        "category_id": 1,
        "display_name": "DAG design",
        "id": 1557,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "PATTERN",
        "slug": "dag-design",
        "sub_category_id": 1113,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "dimensions": [
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Workflow Orchestration for ML Pipelines",
            "id": 54,
            "rationale": "Workflow engines used to coordinate training, evaluation, deployment, and retraining jobs. This cluster covers dependencies, retries, scheduling, and pipeline composition for ML lifecycle automation.",
            "slug": "workflow-orchestration-for-ml-pipelines",
            "source": "db"
          },
          "input_skill": "DAG",
          "llm_role": null,
          "roles_from_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"
            }
          ]
        }
      ],
      "input_skill": "DAG",
      "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 Validation",
      "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": "data-validation",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [
        {
          "alias_text": "Unit Testing",
          "alias_type": "CANONICAL",
          "id": 865,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        }
      ],
      "canonical": {
        "category_id": 8,
        "display_name": "Unit Testing",
        "id": 517,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "METHODOLOGY",
        "slug": "unit-testing",
        "sub_category_id": 44,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "dimensions": [
        {
          "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": "Unit Testing",
          "llm_role": null,
          "roles_from_db": []
        }
      ],
      "input_skill": "Unit Testing",
      "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": "QA",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Testing 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": "qa",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [
        {
          "alias_text": "SQL",
          "alias_type": "CANONICAL",
          "id": 271,
          "is_primary": true,
          "match_strategy": "CASE_INSENSITIVE"
        }
      ],
      "canonical": {
        "category_id": 6,
        "display_name": "SQL",
        "id": 101,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "LANGUAGE",
        "slug": "sql",
        "sub_category_id": 97,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "dimensions": [
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Pega Programming Languages \u0026 DSLs",
            "id": 267,
            "rationale": "Programming languages and domain-specific languages used in Pega development.",
            "slug": "pega-programming-languages-dsls",
            "source": "db"
          },
          "input_skill": "SQL",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "Pega Developer",
              "id": 24,
              "rationale": null,
              "role_archetype": null,
              "slug": "pega-developer",
              "source": "db"
            }
          ]
        },
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Programming Languages \u0026 DSLs",
            "id": 475,
            "rationale": "Oversee and guide the selection and effective use of programming and domain\u2010specific languages in software projects.",
            "slug": "programming-languages-dsls",
            "source": "db"
          },
          "input_skill": "SQL",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "Engineering Manager",
              "id": 121,
              "rationale": null,
              "role_archetype": null,
              "slug": "engineering-manager",
              "source": "db"
            }
          ]
        },
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Programming Languages for Data Work",
            "id": 21,
            "rationale": "Languages used to implement data pipelines, transformations, and operational glue. This is the primary coding surface for building ingestion, enrichment, and automation logic in data engineering.",
            "slug": "programming-languages-for-data-work",
            "source": "db"
          },
          "input_skill": "SQL",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "Data Engineer",
              "id": 2,
              "rationale": null,
              "role_archetype": null,
              "slug": "data-engineer",
              "source": "db"
            }
          ]
        }
      ],
      "input_skill": "SQL",
      "matched_via": "alias",
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": null,
      "source_tag": "db",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "Data Warehousing",
      "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-warehousing",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    }
  ],
  "unmatched_skills": [
    "ETL",
    "Data Validation",
    "QA",
    "Data Warehousing"
  ]
}
API 3 — final-role-output
{
  "chosen_role": {
    "display_name": "Data Engineer",
    "id": 2,
    "rationale": "The primary skills strongly align with the responsibilities of a Data Engineer.",
    "role_archetype": null,
    "slug": "data-engineer",
    "source": "db"
  },
  "chosen_role_resolution": "in_db",
  "final_input_skills": [
    {
      "skill": "Snowflake",
      "tag": "in_db"
    },
    {
      "skill": "Airflow",
      "tag": "in_db"
    },
    {
      "skill": "ETL",
      "tag": "new"
    },
    {
      "skill": "JSON",
      "tag": "in_db"
    },
    {
      "skill": "DAG",
      "tag": "in_db"
    },
    {
      "skill": "Data Validation",
      "tag": "new"
    },
    {
      "skill": "Unit Testing",
      "tag": "in_db"
    },
    {
      "skill": "QA",
      "tag": "new"
    },
    {
      "skill": "SQL",
      "tag": "in_db"
    },
    {
      "skill": "Data Warehousing",
      "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 Data Warehouses",
          "id": 22,
          "rationale": "Managed analytical storage and compute platforms used for curated datasets, reporting, and downstream analytics. These systems are central to data modeling, performance tuning, and cost-aware query design.",
          "slug": "cloud-data-warehouses",
          "source": "db"
        },
        "dimension_id": 22,
        "input_skill": "Snowflake",
        "llm_role": null,
        "matched_chosen_role": true,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension saved",
        "role_dimension_saved": true,
        "roles_from_db": [
          {
            "display_name": "Data Engineer",
            "id": 2,
            "rationale": null,
            "role_archetype": null,
            "slug": "data-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 105,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 2,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Workflow Orchestration for ML Pipelines",
          "id": 54,
          "rationale": "Workflow engines used to coordinate training, evaluation, deployment, and retraining jobs. This cluster covers dependencies, retries, scheduling, and pipeline composition for ML lifecycle automation.",
          "slug": "workflow-orchestration-for-ml-pipelines",
          "source": "db"
        },
        "dimension_id": 54,
        "input_skill": "Airflow",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "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"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 265,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 2,
        "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"
        },
        "dimension_id": 127,
        "input_skill": "JSON",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "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": 435,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "fullstack-developer",
            "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"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 1984,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 2,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "API Interface and Contract Design",
          "id": 289,
          "rationale": "Designing backend service interfaces and contracts that other systems consume, including endpoint and operation shape, request/response payloads, schema and validation, pagination, filtering, idempotency, versioning, status codes, and backward compatibility across REST, GraphQL, gRPC, and OpenAPI-based APIs.",
          "slug": "api-interface-and-contract-design",
          "source": "db"
        },
        "dimension_id": 289,
        "input_skill": "JSON",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": ".NET Backend Developer",
            "id": 83,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "dotnet-backend-developer",
            "source": "db"
          },
          {
            "display_name": "Go Backend Developer",
            "id": 81,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "go-backend-developer",
            "source": "db"
          },
          {
            "display_name": "Kotlin Backend Developer",
            "id": 84,
            "rationale": null,
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