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Pipeline run

1a5d89fb-fd68-4386-8ca8-ee7a2a39da00

Pipeline LLM cost (USD)
API 1: $0.0081 API 2: $0.0001 API 3: $0.0000 Total: $0.0083

Client output enrichment

v2 Skill cluster · Nature of work · AI index · Tech stack maturity · Evidence · KRA description
Nature of work · BI / Analytics / Reporting
Build and maintain Looker/LookML dashboards, write and tune SQL in BigQuery for large datasets, and work with stakeholders to turn BI needs into accurate, performant reporting and documentation.
"Design, develop, and maintain Looker dashboards and visualizations to meet business needs."
Tech stack maturity
Modern Cloud Native
BigQuery and Looker are cloud-native analytics tools, and the BI/development focus on SQL, dimensional modeling, and star schemas aligns with modern cloud-based data warehousing practices.
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 (8)
Looker SQL BigQuery LookML Data Modeling Star Schema Snowflake Schema Dimensional Modeling
Skill cluster (1 dimension groups, role-scoped)
Cross-cutting / unaligned
Looker SQL BigQuery LookML Data Modeling Star Schema Snowflake Schema Dimensional Modeling
Show KRA description ↓
• Design, develop, and maintain Looker dashboards and visualizations to meet business needs. • Write and optimize complex SQL queries to extract, transform, and analyze data from large datasets in BigQuery. • Develop and maintain LookML models, explores, views, and dashboards. • Collaborate with stakeholders to gather BI requirements and translate them into technical specifications. • Suggest Cost optimization solutions • Understanding current Data Architecture structure and suggesting the required changes • Ensure data accuracy, consistency, and performance in reporting and analytics solutions. • Monitor Looker performance and optimize LookML models and SQL for efficiency. • Work closely with data engineers and analysts to align on data pipelines and definitions. • Provide training and documentation for end-users on Looker dashboards and data sources. • Follow best practices for data governance, security, and compliance. • 5+ years of experience as a BI Developer or in a similar role. • Strong hands-on experience with Looker and LookML development. • Proficient in SQL with a deep understanding of data transformation and analysis. • Hands-on experience with Google BigQuery or similar cloud data warehouse solutions. • Solid understanding of data modeling concepts (star/snowflake schemas, dimensional modeling). • Experience working with large datasets and performance tuning for BI tools. • Excellent communication skills and the ability to translate business needs into technical solutions.

Signals

Skill data-engineer
0.62
Alias data-analyst
1.00
KRA data-engineer
0.63

Post-classification

Centroidupdated · n=11
Alias collision log
New-role queue
New skills captured3
New KRA capturedyes

Captured for admin review

LookML primary BI Developer pending
Data Modeling primary BI Developer pending
Snowflake Schema primary BI Developer pending
R&R fragment (sim 0.00) BI Developer pending

• Design, develop, and maintain Looker dashboards and visualizations to meet business needs. • Write and optimize complex SQL queries to extract, transform, and analyze data from large datasets in Big…

Status: completed Created: 2026-05-27T16:57:44.271444Z Updated: 2026-05-27T16:58:19.181179Z API 3 duration: 8264 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

BI Developer

domain · Data Engineering & Analytics CASE DOMAIN

slug: bi-developer · id: 147 · source: db

Domain=Data Engineering & Analytics; The JD is centered on Looker/LookML dashboard development, BI reporting, SQL analysis, and stakeholder-facing BI work, which most directly matches a BI Developer role.

Matched skills

LookerLookMLSQLBigQuerydata modelingstar/snowflake schemasdimensional modelingdata governancesecuritycompliance

Matched dimensions

BI dashboard and visualization developmentLooker semantic modelingSQL-based data transformation and analysisReporting performance optimizationBI requirements translationData accuracy and consistencyData architecture improvementBI governance and compliance

Matched KRAs

Design, develop, and maintain Looker dashboards and visualizationsWrite and optimize complex SQL queriesDevelop and maintain LookML models, explores, views, and dashboardsCollaborate with stakeholders to gather BI requirementsSuggest Cost optimization solutionsUnderstand current Data Architecture structure and suggesting the required changesEnsure data accuracy, consistency, and performanceMonitor Looker performance and optimize LookML models and SQLWork closely with data engineers and analystsProvide training and documentation for end-users

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

0
New skills
0
Skill↔dim saved
0
Role↔dim saved
2
Skipped

Job description

We are seeking an experienced Business Intelligence (BI) Developer with strong expertise in Looker, LookML, BigQuery, and SQL to join our data team. You will play a critical role in transforming data into actionable insights, building scalable data models, and delivering high-quality dashboards and reports to support business decision-making.

Responsibilities:

• Design, develop, and maintain Looker dashboards and visualizations to meet business needs.
• Write and optimize complex SQL queries to extract, transform, and analyze data from large datasets in BigQuery.
• Develop and maintain LookML models, explores, views, and dashboards.
• Collaborate with stakeholders to gather BI requirements and translate them into technical specifications.
• Suggest Cost optimization solutions 
• Understanding current Data Architecture structure and suggesting the required changes
• Ensure data accuracy, consistency, and performance in reporting and analytics solutions.
• Monitor Looker performance and optimize LookML models and SQL for efficiency.
• Work closely with data engineers and analysts to align on data pipelines and definitions.
• Provide training and documentation for end-users on Looker dashboards and data sources.
• Follow best practices for data governance, security, and compliance.


Qualification:

• 5+ years of experience as a BI Developer or in a similar role.
• Strong hands-on experience with Looker and LookML development.
• Proficient in SQL with a deep understanding of data transformation and analysis.
• Hands-on experience with Google BigQuery or similar cloud data warehouse solutions.
• Solid understanding of data modeling concepts (star/snowflake schemas, dimensional modeling).
• Experience working with large datasets and performance tuning for BI tools.
• Excellent communication skills and the ability to translate business needs into technical solutions.

Skills from this JD

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

Looker Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Looker id=152 · looker

Aliases — catalog

  • Looker (CANONICAL) primary

Context tags (catalog)

BigQuery Dimensions Explores LookML Measures PDT SQL Runner Snowflake dashboards data modeling derived table drill-down embedded analytics scheduled delivery tiles

Stored enrichment (catalog DB)

Category
Platform
Sub-category
Bi Analytics Platform
Vendor
Google Cloud
License
proprietary
Year introduced
2012
Confidence
0.95
Version strategy
NOT_APPLICABLE

Maturity reasoning: Looker appears frequently in BI/analytics job descriptions and is a standard enterprise analytics platform, especially after Google Cloud’s acquisition expanded market visibility.

Skill profile (library / DB)

Skill nature
PLATFORM
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
9
Sub-category id
111
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • BI and Visualization Tools Catalog dimension db id 31

    Library dimension (catalog)

    Roles linked in library: Data Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
BI and Visualization Tools
bi-and-visualization-tools
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
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 skipped (dimension not under chosen role)
BigQuery Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: BigQuery id=106 · bigquery

Aliases — catalog

  • BigQuery (CANONICAL) primary

Context tags (catalog)

Cloud Storage Dataflow ELT ETL GCP Google Cloud Platform Looker Pub/Sub SQL Standard SQL clustered tables data warehouse dbt partitioned tables service account

Stored enrichment (catalog DB)

Category
Service
Sub-category
Data Warehouse Service
Vendor
Google
License
proprietary
Year introduced
2011
Confidence
0.98
Version strategy
NOT_APPLICABLE

Maturity reasoning: BigQuery appears frequently in data/analytics job descriptions and is a core Google Cloud warehouse offering, with broad enterprise adoption and strong ecosystem support.

Skill profile (library / DB)

Skill nature
CLOUD_SERVICE
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
11
Sub-category id
118
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 skipped (dimension not under chosen role)
LookML 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
LANGUAGE
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
Star Schema Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Star schema id=126 · star-schema

Aliases — catalog

  • Star schema (CANONICAL) primary

Context tags (catalog)

ETL Kimball OLAP aggregate table business intelligence conformed dimensions data mart denormalization dimension table fact table grain slowly changing dimension snowflake schema star join surrogate key

Stored enrichment (catalog DB)

Category
Architecture
Sub-category
Data Warehouse Architecture
Confidence
0.95
Version strategy
NOT_APPLICABLE

Maturity reasoning: Common data-warehouse pattern in BI/analytics JDs and vendor docs; widely used alongside Snowflake/BigQuery/Redshift for dimensional modeling, with no sunset or replacement signal.

Skill profile (library / DB)

Skill nature
PATTERN
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
1
Sub-category id
70
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Data Modeling and Schema Design Catalog dimension db id 26

    Library dimension (catalog)

    Roles linked in library: Data Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Data Modeling and Schema Design
data-modeling-and-schema-design
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Snowflake Schema Primary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).

Derived legacy fields
Category
Databases
Sub-category
general
Skill nature
CONCEPT
Volatility
STABLE
Typical lifespan
EVERGREEN
Version strategy
UNVERSIONED
Dimensional Modeling Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Dimensional modeling id=125 · dimensional-modeling

Aliases — catalog

  • Dimensional modeling (CANONICAL) primary

Context tags (catalog)

ETL Kimball OLAP SCD Type 2 business intelligence conformed dimensions data warehouse dimension table drill-down fact table grain slowly changing dimension snowflake schema star schema surrogate key

Stored enrichment (catalog DB)

Category
Concept
Sub-category
Data Modeling Concept
Confidence
0.97
Version strategy
NOT_APPLICABLE

Maturity reasoning: Common in analytics/data-warehouse JDs and BI roles; star/snowflake schema terms appear frequently in job postings and vendor docs for Snowflake/BigQuery/Redshift.

Skill profile (library / DB)

Skill nature
CONCEPT
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
2
Sub-category id
10
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Data Modeling and Schema Design Catalog dimension db id 26

    Library dimension (catalog)

    Roles linked in library: Data Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Data Modeling and Schema Design
data-modeling-and-schema-design
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)

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
Looker in_db
BI and Visualization Tools
bi-and-visualization-tools
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 skipped (dimension not under chosen role)
BigQuery in_db
Cloud Data Warehouses
cloud-data-warehouses
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
Star Schema in_db
Data Modeling and Schema Design
data-modeling-and-schema-design
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Dimensional Modeling in_db
Data Modeling and Schema Design
data-modeling-and-schema-design
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)

Library artifacts (this run)

Kind Detail DB id
canonical_skill_proposed LookML | type=Data Engineering Tools subtype=general nature=LANGUAGE lifespan=MULTI_YEAR
canonical_skill_proposed Snowflake Schema | type=Databases 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
nano JD Parser — gpt-4.1-nano click to toggle
RoleBusiness Intelligence (BI) Developer
Experience5+ years of experience as a BI Developer or in a similar role.
DomainOther
JD type pass
Show raw JSON
{
  "JD_type": "pass",
  "about_company": null,
  "certifications": [],
  "company_name": null,
  "ctc": null,
  "domain": {
    "primary": {
      "aliases": [],
      "domain": "Other"
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    "secondary": null
  },
  "education": [],
  "experience": {
    "max": null,
    "min": 5,
    "raw": "5+ years of experience as a BI Developer or in a similar role."
  },
  "job_locations": [],
  "role": "Business Intelligence (BI) Developer",
  "role_aliases": [
    "BI Developer",
    "Business Intelligence Developer",
    "Data Analyst"
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  "role_archetype": "Data",
  "roles_and_responsibilities": [
    {
      "bullet_count": 11,
      "heading": "Responsibilities",
      "heading_was_present": true,
      "source_marker": {
        "first_5_words": "\u2022 Design, develop, and maintain",
        "last_5_words": "security, and compliance."
      },
      "text": "\u2022 Design, develop, and maintain Looker dashboards and visualizations to meet business needs.\n\u2022 Write and optimize complex SQL queries to extract, transform, and analyze data from large datasets in BigQuery.\n\u2022 Develop and maintain LookML models, explores, views, and dashboards.\n\u2022 Collaborate with stakeholders to gather BI requirements and translate them into technical specifications.\n\u2022 Suggest Cost optimization solutions \n\u2022 Understanding current Data Architecture structure and suggesting the required changes\n\u2022 Ensure data accuracy, consistency, and performance in reporting and analytics solutions.\n\u2022 Monitor Looker performance and optimize LookML models and SQL for efficiency.\n\u2022 Work closely with data engineers and analysts to align on data pipelines and definitions.\n\u2022 Provide training and documentation for end-users on Looker dashboards and data sources.\n\u2022 Follow best practices for data governance, security, and compliance.",
      "word_count": 134
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    {
      "bullet_count": 7,
      "heading": "Qualification",
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      "source_marker": {
        "first_5_words": "\u2022 5+ years of experience as",
        "last_5_words": "into technical solutions."
      },
      "text": "\u2022 5+ years of experience as a BI Developer or in a similar role.\n\u2022 Strong hands-on experience with Looker and LookML development.\n\u2022 Proficient in SQL with a deep understanding of data transformation and analysis.\n\u2022 Hands-on experience with Google BigQuery or similar cloud data warehouse solutions.\n\u2022 Solid understanding of data modeling concepts (star/snowflake schemas, dimensional modeling).\n\u2022 Experience working with large datasets and performance tuning for BI tools.\n\u2022 Excellent communication skills and the ability to translate business needs into technical solutions.",
      "word_count": 107
    }
  ],
  "urls": []
}
API 1 — extract-from-jd click to toggle
{
  "final_skills": [
    {
      "is_primary": true,
      "skill_name": "Looker"
    },
    {
      "is_primary": true,
      "skill_name": "SQL"
    },
    {
      "is_primary": true,
      "skill_name": "BigQuery"
    },
    {
      "is_primary": true,
      "skill_name": "LookML"
    },
    {
      "is_primary": true,
      "skill_name": "Data Modeling"
    },
    {
      "is_primary": true,
      "skill_name": "Star Schema"
    },
    {
      "is_primary": true,
      "skill_name": "Snowflake Schema"
    },
    {
      "is_primary": true,
      "skill_name": "Dimensional Modeling"
    }
  ],
  "jd_role": {
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    "rationale": null,
    "role_aliases": [
      "BI Developer",
      "Business Intelligence Developer",
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    "slug": ""
  },
  "nano_parsed": {
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    "roles_and_responsibilities": [
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        },
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  "rejection_reason": null,
  "run_id": "1a5d89fb-fd68-4386-8ca8-ee7a2a39da00",
  "stage3_signals": {
    "alias_found": true,
    "alias_match_roles": [
      {
        "display_name": "Data Analyst",
        "kra_matches": null,
        "matched_count": null,
        "matched_skills": null,
        "role_id": 143,
        "score": 1.0,
        "slug": "data-analyst",
        "total_count": null
      },
      {
        "display_name": "BI Developer",
        "kra_matches": null,
        "matched_count": null,
        "matched_skills": null,
        "role_id": 147,
        "score": 1.0,
        "slug": "bi-developer",
        "total_count": null
      }
    ],
    "kra_match_roles": [
      {
        "display_name": "Data Engineer",
        "kra_matches": [
          {
            "kra_text": "Works with data analysts, data scientists, and business stakeholders to define data models, ingestion schedules, and data delivery requirements.",
            "sentence": "Work closely with data engineers and analysts to align on data pipelines and definitions.",
            "similarity": 0.7055
          },
          {
            "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": "Experience working with large datasets and performance tuning for BI tools.",
            "similarity": 0.6043
          },
          {
            "kra_text": "Implements data quality validation rules, reconciliation checks, and anomaly detection to ensure data completeness, accuracy, and consistency.",
            "sentence": "Ensure data accuracy, consistency, and performance in reporting and analytics solutions.",
            "similarity": 0.5893
          }
        ],
        "matched_count": null,
        "matched_skills": null,
        "role_id": 2,
        "score": 0.633,
        "slug": "data-engineer",
        "total_count": null
      },
      {
        "display_name": "MLOps Engineer",
        "kra_matches": [
          {
            "kra_text": "Sets up model monitoring dashboards, data drift detection, prediction performance tracking, and alert routing for production ML systems.",
            "sentence": "Develop and maintain LookML models, explores, views, and dashboards.",
            "similarity": 0.5779
          },
          {
            "kra_text": "Sets up model monitoring dashboards, data drift detection, prediction performance tracking, and alert routing for production ML systems.",
            "sentence": "Monitor Looker performance and optimize LookML models and SQL for efficiency.",
            "similarity": 0.5106
          },
          {
            "kra_text": "Validates model performance benchmarks, data schema contracts, and system integration health before signing off on production release readiness.",
            "sentence": "Ensure data accuracy, consistency, and performance in reporting and analytics solutions.",
            "similarity": 0.5044
          }
        ],
        "matched_count": null,
        "matched_skills": null,
        "role_id": 16,
        "score": 0.5309,
        "slug": "ml-ops-engineer",
        "total_count": null
      },
      {
        "display_name": "ML Engineer",
        "kra_matches": [
          {
            "kra_text": "Translates product requirements into machine learning system specifications including feature definitions, model architecture choices, and success metric definitions.",
            "sentence": "Collaborate with stakeholders to gather BI requirements and translate them into technical specifications.",
            "similarity": 0.5319
          },
          {
            "kra_text": "Monitors production model behavior for data drift, concept drift, and prediction performance degradation using monitoring dashboards and alerting.",
            "sentence": "Develop and maintain LookML models, explores, views, and dashboards.",
            "similarity": 0.4969
          },
          {
            "kra_text": "Prepares, cleans, and transforms training datasets, manages feature stores, and builds feature engineering pipelines for model training.",
            "sentence": "Work closely with data engineers and analysts to align on data pipelines and definitions.",
            "similarity": 0.4791
          }
        ],
        "matched_count": null,
        "matched_skills": null,
        "role_id": 3,
        "score": 0.5026,
        "slug": "ml-engineer",
        "total_count": null
      },
      {
        "display_name": "Fullstack Developer",
        "kra_matches": [
          {
            "kra_text": "Designs and queries relational databases like PostgreSQL and document stores like MongoDB, writing migrations, indexes, and optimized queries.",
            "sentence": "Write and optimize complex SQL queries to extract, transform, and analyze data from large datasets in BigQuery.",
            "similarity": 0.5175
          },
          {
            "kra_text": "Works closely with product managers and UX designers to translate requirements and wireframes into working software features through iterative development.",
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          },
          {
            "kra_text": "Designs and queries relational databases like PostgreSQL and document stores like MongoDB, writing migrations, indexes, and optimized queries.",
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            "similarity": 0.4636
          }
        ],
        "matched_count": null,
        "matched_skills": null,
        "role_id": 15,
        "score": 0.4914,
        "slug": "full-stack-engineer",
        "total_count": null
      },
      {
        "display_name": "Java Backend Developer",
        "kra_matches": [
          {
            "kra_text": "backend performance tuning",
            "sentence": "Experience working with large datasets and performance tuning for BI tools.",
            "similarity": 0.5134
          },
          {
            "kra_text": "backend performance tuning",
            "sentence": "Monitor Looker performance and optimize LookML models and SQL for efficiency.",
            "similarity": 0.4825
          },
          {
            "kra_text": "persistence and data modeling",
            "sentence": "Understanding current Data Architecture structure and suggesting the required changes",
            "similarity": 0.4549
          }
        ],
        "matched_count": null,
        "matched_skills": null,
        "role_id": 79,
        "score": 0.4836,
        "slug": "java-backend-developer",
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      }
    ],
    "skill_match_roles": [
      {
        "display_name": "Data Engineer",
        "kra_matches": null,
        "matched_count": 5,
        "matched_skills": [
          "BigQuery",
          "Dimensional modeling",
          "Looker",
          "SQL",
          "Star schema"
        ],
        "role_id": 2,
        "score": 0.625,
        "slug": "data-engineer",
        "total_count": 8
      },
      {
        "display_name": "Pega Developer",
        "kra_matches": null,
        "matched_count": 1,
        "matched_skills": [
          "SQL"
        ],
        "role_id": 24,
        "score": 0.125,
        "slug": "pega-developer",
        "total_count": 8
      },
      {
        "display_name": "Engineering Manager",
        "kra_matches": null,
        "matched_count": 1,
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          "SQL"
        ],
        "role_id": 121,
        "score": 0.125,
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        "total_count": 8
      }
    ]
  },
  "stage4_decision": {
    "alias_collision_detected": false,
    "case": "DOMAIN",
    "chosen_role": {
      "display_name": "BI Developer",
      "kra_matches": null,
      "matched_count": null,
      "matched_skills": null,
      "role_id": 147,
      "score": 0.99,
      "slug": "bi-developer",
      "total_count": null
    },
    "confidence": 0.99,
    "is_new_role": false,
    "llm2_fired": false,
    "llm2_reasoning": null,
    "matched_dimensions": [
      "BI dashboard and visualization development",
      "Looker semantic modeling",
      "SQL-based data transformation and analysis",
      "Reporting performance optimization",
      "BI requirements translation",
      "Data accuracy and consistency",
      "Data architecture improvement",
      "BI governance and compliance"
    ],
    "matched_kras": [
      "Design, develop, and maintain Looker dashboards and visualizations",
      "Write and optimize complex SQL queries",
      "Develop and maintain LookML models, explores, views, and dashboards",
      "Collaborate with stakeholders to gather BI requirements",
      "Suggest Cost optimization solutions",
      "Understand current Data Architecture structure and suggesting the required changes",
      "Ensure data accuracy, consistency, and performance",
      "Monitor Looker performance and optimize LookML models and SQL",
      "Work closely with data engineers and analysts",
      "Provide training and documentation for end-users"
    ],
    "matched_skills": [
      "Looker",
      "LookML",
      "SQL",
      "BigQuery",
      "data modeling",
      "star/snowflake schemas",
      "dimensional modeling",
      "data governance",
      "security",
      "compliance"
    ],
    "new_role_display_name": null,
    "new_role_slug": null,
    "queued": false,
    "reasoning": "Domain=Data Engineering \u0026 Analytics; The JD is centered on Looker/LookML dashboard development, BI reporting, SQL analysis, and stakeholder-facing BI work, which most directly matches a BI Developer role.",
    "sub_role": null
  },
  "stage5_updates": {
    "centroid_n_after": 11,
    "centroid_updated": true,
    "collision_log_id": null,
    "new_kra_attached": {
      "best_kra_similarity": 0.0,
      "queue_id": 1747,
      "r_and_r_preview": "\u2022 Design, develop, and maintain Looker dashboards and visualizations to meet business needs.\n\u2022 Write and optimize complex SQL queries to extract, transform, and analyze data from large datasets in Big",
      "role_display_name": "BI Developer",
      "role_slug": "bi-developer",
      "status": "pending"
    },
    "new_skills_attached": [
      {
        "is_primary": true,
        "queue_id": 22808,
        "role_display_name": "BI Developer",
        "role_slug": "bi-developer",
        "skill_name": "LookML",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 22809,
        "role_display_name": "BI Developer",
        "role_slug": "bi-developer",
        "skill_name": "Data Modeling",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 22810,
        "role_display_name": "BI Developer",
        "role_slug": "bi-developer",
        "skill_name": "Snowflake Schema",
        "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_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 361,
      "existing_alias_text": "Looker",
      "input_term": "Looker",
      "matched_canonical": {
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        "display_name": "Looker",
        "id": 152,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "PLATFORM",
        "slug": "looker",
        "sub_category_id": 111,
        "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": 271,
      "existing_alias_text": "SQL",
      "input_term": "SQL",
      "matched_canonical": {
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        "display_name": "SQL",
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        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "LANGUAGE",
        "slug": "sql",
        "sub_category_id": 97,
        "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": 300,
      "existing_alias_text": "BigQuery",
      "input_term": "BigQuery",
      "matched_canonical": {
        "category_id": 11,
        "display_name": "BigQuery",
        "id": 106,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "CLOUD_SERVICE",
        "slug": "bigquery",
        "sub_category_id": 118,
        "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,
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        "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": 330,
      "existing_alias_text": "Star schema",
      "input_term": "Star Schema",
      "matched_canonical": {
        "category_id": 1,
        "display_name": "Star schema",
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        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "PATTERN",
        "slug": "star-schema",
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        "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": 329,
      "existing_alias_text": "Dimensional modeling",
      "input_term": "Dimensional Modeling",
      "matched_canonical": {
        "category_id": 2,
        "display_name": "Dimensional modeling",
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        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "CONCEPT",
        "slug": "dimensional-modeling",
        "sub_category_id": 10,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "matched_via": "alias"
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  ],
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    },
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      "display_name": "Pega Developer",
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      "role_archetype": null,
      "slug": "pega-developer",
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      "slug": "engineering-manager",
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      "display_name": "Python Backend Developer",
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      "role_archetype": "Engineering",
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    },
    {
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      "display_name": "Node.js Backend Developer",
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      "rationale": null,
      "role_archetype": "Engineering",
      "slug": "node-backend-developer",
      "source": "db"
    },
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      "display_name": "PHP Backend Developer",
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      "role_archetype": "Engineering",
      "slug": "php-backend-developer",
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    },
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      "display_name": "Ruby Backend Developer",
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      "role_archetype": "Engineering",
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    },
    {
      "display_name": "Scala Backend Developer",
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      "rationale": null,
      "role_archetype": "Engineering",
      "slug": "scala-backend-developer",
      "source": "db"
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  ],
  "chosen_role": {
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    "rationale": "Domain=Data Engineering \u0026 Analytics; The JD is centered on Looker/LookML dashboard development, BI reporting, SQL analysis, and stakeholder-facing BI work, which most directly matches a BI Developer role.",
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      "input_skill": "Looker",
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      "roles_from_db": [
        {
          "display_name": "Data Engineer",
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      ]
    },
    {
      "dimension": {
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        "source": "db"
      },
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      "roles_from_db": [
        {
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        }
      ]
    },
    {
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      },
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      "roles_from_db": [
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      ]
    },
    {
      "dimension": {
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        "source": "db"
      },
      "input_skill": "SQL",
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      "roles_from_db": [
        {
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      ]
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
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        "source": "db"
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      "input_skill": "BigQuery",
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      "roles_from_db": [
        {
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      ]
    },
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      "dimension": {
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        "source": "db"
      },
      "input_skill": "Data Modeling",
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      "roles_from_db": [
        {
          "display_name": ".NET Backend Developer",
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          "role_archetype": "Engineering",
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        },
        {
          "display_name": "Python Backend Developer",
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      ]
    },
    {
      "dimension": {
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        "source": "db"
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      "input_skill": "Data Modeling",
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        {
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          "role_archetype": "Engineering",
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  "new_aliases_persisted": 0,
  "run_id": "1a5d89fb-fd68-4386-8ca8-ee7a2a39da00",
  "skills_detail": [
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      "aliases_in_db": [
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          "alias_text": "Looker",
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          "is_primary": true,
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      ],
      "canonical": {
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          "roles_from_db": [
            {
              "display_name": "Data Engineer",
              "id": 2,
              "rationale": null,
              "role_archetype": null,
              "slug": "data-engineer",
              "source": "db"
            }
          ]
        }
      ],
      "input_skill": "Looker",
      "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": "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": [
        {
          "alias_text": "BigQuery",
          "alias_type": "CANONICAL",
          "id": 300,
          "is_primary": true,
          "match_strategy": "CASE_INSENSITIVE"
        }
      ],
      "canonical": {
        "category_id": 11,
        "display_name": "BigQuery",
        "id": 106,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "CLOUD_SERVICE",
        "slug": "bigquery",
        "sub_category_id": 118,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "dimensions": [
        {
          "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"
          },
          "input_skill": "BigQuery",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "Data Engineer",
              "id": 2,
              "rationale": null,
              "role_archetype": null,
              "slug": "data-engineer",
              "source": "db"
            }
          ]
        }
      ],
      "input_skill": "BigQuery",
      "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": "LookML",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Data Engineering Tools",
          "skill_nature": "LANGUAGE",
          "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": "lookml",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [
        {
          "alias_text": "domain modeling",
          "alias_type": "CANONICAL",
          "id": 3675,
          "is_primary": true,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "Domain Modeling",
          "alias_type": "CANONICAL",
          "id": 5644,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        }
      ],
      "canonical": {
        "category_id": 8,
        "display_name": "domain modeling",
        "id": 2379,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "METHODOLOGY",
        "slug": "domain-modeling",
        "sub_category_id": 2831,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "dimensions": [
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Application Architecture Patterns",
            "id": 293,
            "rationale": "Structural patterns for organizing Python backend code into maintainable modules, layers, and feature boundaries. This is a coherent cluster because senior backend developers are expected to refactor and shape service internals over time.",
            "slug": "application-architecture-patterns",
            "source": "db"
          },
          "input_skill": "Data Modeling",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": ".NET Backend Developer",
              "id": 83,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "dotnet-backend-developer",
              "source": "db"
            },
            {
              "display_name": "Python Backend Developer",
              "id": 80,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "python-backend-developer",
              "source": "db"
            }
          ]
        },
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Service Architecture and Design Patterns",
            "id": 18,
            "rationale": "Reusable backend design patterns used to structure service code and boundaries. Covers layering, dependency management, domain modeling, and maintainable service organization.",
            "slug": "service-architecture-and-design-patterns",
            "source": "db"
          },
          "input_skill": "Data Modeling",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "Backend Developer",
              "id": 1,
              "rationale": null,
              "role_archetype": "A Backend Engineer designs, builds, and maintains the server-side logic and data handling that power applications and services. They focus on implementing reliable business functionality, integrating with other systems, and ensuring the backend is scalable, maintainable, and observable.",
              "slug": "backend-engineer",
              "source": "db"
            },
            {
              "display_name": "Java Backend Developer",
              "id": 79,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "java-backend-developer",
              "source": "db"
            },
            {
              "display_name": "Kotlin Backend Developer",
              "id": 84,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "kotlin-server-backend-developer",
              "source": "db"
            },
            {
              "display_name": "Node.js Backend Developer",
              "id": 82,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "node-backend-developer",
              "source": "db"
            },
            {
              "display_name": "PHP Backend Developer",
              "id": 86,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "php-backend-developer",
              "source": "db"
            },
            {
              "display_name": "Ruby Backend Developer",
              "id": 85,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "ruby-backend-developer",
              "source": "db"
            },
            {
              "display_name": "Scala Backend Developer",
              "id": 87,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "scala-backend-developer",
              "source": "db"
            }
          ]
        }
      ],
      "input_skill": "Data Modeling",
      "matched_via": "embedding_alias",
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": null,
      "source_tag": "db",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [
        {
          "alias_text": "Star schema",
          "alias_type": "CANONICAL",
          "id": 330,
          "is_primary": true,
          "match_strategy": "CASE_INSENSITIVE"
        }
      ],
      "canonical": {
        "category_id": 1,
        "display_name": "Star schema",
        "id": 126,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "PATTERN",
        "slug": "star-schema",
        "sub_category_id": 70,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "dimensions": [
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Data Modeling and Schema Design",
            "id": 26,
            "rationale": "Designing curated data structures for analytics and downstream consumption. Covers dimensional modeling, normalization tradeoffs, slowly changing dimensions, and schema evolution for durable datasets.",
            "slug": "data-modeling-and-schema-design",
            "source": "db"
          },
          "input_skill": "Star Schema",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "Data Engineer",
              "id": 2,
              "rationale": null,
              "role_archetype": null,
              "slug": "data-engineer",
              "source": "db"
            }
          ]
        }
      ],
      "input_skill": "Star Schema",
      "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": "Snowflake Schema",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Databases",
          "skill_nature": "CONCEPT",
          "sub_category": "general",
          "typical_lifespan": "EVERGREEN",
          "version_strategy": "UNVERSIONED",
          "volatility": "STABLE"
        },
        "enrichment": null,
        "keep_log": [],
        "locked_dimensions": [],
        "merge_log": [],
        "placed": null,
        "relationships": null,
        "skill_id": "snowflake-schema",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [
        {
          "alias_text": "Dimensional modeling",
          "alias_type": "CANONICAL",
          "id": 329,
          "is_primary": true,
          "match_strategy": "CASE_INSENSITIVE"
        }
      ],
      "canonical": {
        "category_id": 2,
        "display_name": "Dimensional modeling",
        "id": 125,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "CONCEPT",
        "slug": "dimensional-modeling",
        "sub_category_id": 10,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "dimensions": [
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Data Modeling and Schema Design",
            "id": 26,
            "rationale": "Designing curated data structures for analytics and downstream consumption. Covers dimensional modeling, normalization tradeoffs, slowly changing dimensions, and schema evolution for durable datasets.",
            "slug": "data-modeling-and-schema-design",
            "source": "db"
          },
          "input_skill": "Dimensional Modeling",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "Data Engineer",
              "id": 2,
              "rationale": null,
              "role_archetype": null,
              "slug": "data-engineer",
              "source": "db"
            }
          ]
        }
      ],
      "input_skill": "Dimensional Modeling",
      "matched_via": "alias",
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": null,
      "source_tag": "db",
      "was_in_llm_skills": true
    }
  ],
  "unmatched_skills": [
    "LookML",
    "Snowflake Schema"
  ]
}
API 3 — final-role-output
{
  "chosen_role": {
    "display_name": "BI Developer",
    "id": 147,
    "rationale": "Domain=Data Engineering \u0026 Analytics; The JD is centered on Looker/LookML dashboard development, BI reporting, SQL analysis, and stakeholder-facing BI work, which most directly matches a BI Developer role.",
    "role_archetype": null,
    "slug": "bi-developer",
    "source": "db"
  },
  "chosen_role_resolution": "in_db",
  "final_input_skills": [
    {
      "skill": "Looker",
      "tag": "in_db"
    },
    {
      "skill": "SQL",
      "tag": "in_db"
    },
    {
      "skill": "BigQuery",
      "tag": "in_db"
    },
    {
      "skill": "LookML",
      "tag": "new"
    },
    {
      "skill": "Data Modeling",
      "tag": "in_db"
    },
    {
      "skill": "Star Schema",
      "tag": "in_db"
    },
    {
      "skill": "Snowflake Schema",
      "tag": "new"
    },
    {
      "skill": "Dimensional Modeling",
      "tag": "in_db"
    }
  ],
  "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": 147,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "BI and Visualization Tools",
          "id": 31,
          "rationale": "Tools used to expose curated data to analysts and business users through dashboards, reports, and semantic exploration. Data engineers support these tools by shaping reliable datasets and performant models.",
          "slug": "bi-and-visualization-tools",
          "source": "db"
        },
        "dimension_id": 31,
        "input_skill": "Looker",
        "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": "Data Engineer",
            "id": 2,
            "rationale": null,
            "role_archetype": null,
            "slug": "data-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 152,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 147,
        "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"
        },
        "dimension_id": 267,
        "input_skill": "SQL",
        "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": "Pega Developer",
            "id": 24,
            "rationale": null,
            "role_archetype": null,
            "slug": "pega-developer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 101,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 147,
        "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"
        },
        "dimension_id": 475,
        "input_skill": "SQL",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "Engineering Manager",
            "id": 121,
            "rationale": null,
            "role_archetype": null,
            "slug": "engineering-manager",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 101,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 147,
        "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"
        },
        "dimension_id": 21,
        "input_skill": "SQL",
        "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": "Data Engineer",
            "id": 2,
            "rationale": null,
            "role_archetype": null,
            "slug": "data-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 101,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 147,
        "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": "BigQuery",
        "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": "Data Engineer",
            "id": 2,
            "rationale": null,
            "role_archetype": null,
            "slug": "data-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 106,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 147,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Application Architecture Patterns",
          "id": 293,
          "rationale": "Structural patterns for organizing Python backend code into maintainable modules, layers, and feature boundaries. This is a coherent cluster because senior backend developers are expected to refactor and shape service internals over time.",
          "slug": "application-architecture-patterns",
          "source": "db"
        },
        "dimension_id": 293,
        "input_skill": "Data Modeling",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Skipped \u2014 no persistable v3 meta for new skill",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": ".NET Backend Developer",
            "id": 83,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "dotnet-backend-developer",
            "source": "db"
          },
          {
            "display_name": "Python Backend Developer",
            "id": 80,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "python-backend-developer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": false,
        "skill_id": null,
        "skill_tag": "new",
        "skipped_reason": "skill_not_in_db_v3_proposed"
      },
      {
        "chosen_role_id": 147,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Service Architecture and Design Patterns",
          "id": 18,
          "rationale": "Reusable backend design patterns used to structure service code and boundaries. Covers layering, dependency management, domain modeling, and maintainable service organization.",
          "slug": "service-architecture-and-design-patterns",
          "source": "db"
        },
        "dimension_id": 18,
        "input_skill": "Data Modeling",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Skipped \u2014 no persistable v3 meta for new skill",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "Backend Developer",
            "id": 1,
            "rationale": null,
            "role_archetype": "A Backend Engineer designs, builds, and maintains the server-side logic and data handling that power applications and services. They focus on implementing reliable business functionality, integrating with other systems, and ensuring the backend is scalable, maintainable, and observable.",
            "slug": "backend-engineer",
            "source": "db"
          },
          {
            "display_name": "Java Backend Developer",
            "id": 79,
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