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Pipeline run

39cf4d23-848c-4261-a968-cb764de2537f

Pipeline LLM cost (USD)
API 1: $0.0083 API 2: $0.0003 API 3: $0.0000 Total: $0.0086

Client output enrichment

v2 Skill cluster · Nature of work · AI index · Tech stack maturity · Evidence · KRA description
Nature of work · BigQuery / SQL / ETL
Build and optimize BigQuery ETL/data-loading pipelines, write cost-aware SQL on partitioned/clustered tables, and manage access with roles/authorized views; also explore data and apply BigQuery ML when needed.
""Create ETL pipeline using Bigquery""
Tech stack maturity
Modern Cloud Native
BigQuery is a cloud-native data warehouse, and clustering plus SQL are standard modern analytics engineering practices.
AI index (0 = no AI use, 5 = totally AI-dependent · v2.1)
1.70 / 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): ML
Evidence — skills matched in JD (11)
BigQuery SQL ETL Partitioning Clustering Streaming Role-Based Access Control Authorized Views Denormalized Data Structures Nested Repeated Fields BigQuery ML
Skill cluster (2 dimension groups, role-scoped)
Performance and Cost Optimization
Clustering
Cross-cutting / unaligned
BigQuery SQL ETL Partitioning Streaming Role-Based Access Control Authorized Views Denormalized Data Structures Nested Repeated Fields BigQuery ML
Show KRA description ↓
1 Create ETL pipeline using Bigquery, create best performing tables with partitioning/clustering etc enabled keeping best practices in mind 2 Write Complex SQL queries keeping execution cost in mind 3 Load data into BigQuery using files or by streaming one record at a time 4 Create, load, and query partitioned tables for daily time-series data 5 Implement fine-grained access control using roles and authorized views 1 Should have indepth understanding of Bigquery architecture, table partitioning, clustering, best practices, type of tables, best practices etc 2 Should know how to reduce BigQuery costs by reducing the amount of data processed by your queries 3 Should be able to speed up queries by using denormalized data structures, with or without nested repeated fields 4 Exploring and Preparing data using BigQuery 5 Implementing ETL jobs using Bigquery 6 Understanding of Bigquery ML 1 Good communication and interpersonal skills 2 Strong writing skills and stakeholder management 3 Excellent problem-solving skills and mitigate technical issues

Signals

Skill data-engineer
0.27
Alias backend-engineer
1.00
KRA data-engineer
0.49

Post-classification

Centroidupdated · n=8
Alias collision log
New-role queue
New skills captured8
New KRA capturedyes

Captured for admin review

ETL primary Data Warehouse Engineer pending
Partitioning primary Data Warehouse Engineer pending
Streaming primary Data Warehouse Engineer pending
Role-Based Access Control primary Data Warehouse Engineer pending
Authorized Views primary Data Warehouse Engineer pending
Denormalized Data Structures primary Data Warehouse Engineer pending
Nested Repeated Fields primary Data Warehouse Engineer pending
BigQuery ML primary Data Warehouse Engineer pending
R&R fragment (sim 0.00) Data Warehouse Engineer pending

1 Create ETL pipeline using Bigquery, create best performing tables with partitioning/clustering etc enabled keeping best practices in mind 2 Write Complex SQL queries keeping execution cost in mind 3…

Status: completed Created: 2026-05-27T14:52:53.347747Z Updated: 2026-06-12T17:07:44.336830Z API 3 duration: 6938 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 Warehouse Engineer

domain · Data Engineering & Analytics CASE DOMAIN

slug: data-warehouse-engineer · id: 144 · source: db

Domain=Data Engineering & Analytics; The JD is centered on BigQuery warehouse design, partitioning/clustering, SQL performance, loading/querying tables, access control, and BigQuery ML, which best matches a Data Warehouse Engineer.

Matched skills

BigqueryETL pipelineComplex SQLpartitioningclusteringstreamingpartitioned tablesauthorized viewsBigquery MLnested repeated fields

Matched dimensions

BigQuery data warehousingETL pipeline engineeringSQL query performance optimizationTable design and partitioning strategyData loading and streaming ingestionData access control and governanceData exploration and preparationBigQuery ML usage

Matched KRAs

Create ETL pipeline using BigqueryWrite Complex SQL queries keeping execution cost in mindLoad data into BigQuery using files or by streamingCreate, load, and query partitioned tablesImplement fine-grained access control using rolesReduce BigQuery costs by reducing data processedSpeed up queries by using denormalized data structuresExploring and Preparing data using BigQueryImplementing ETL jobs using BigqueryUnderstanding of Bigquery ML

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

About Accenture: Accenture is a global professional services company with leading capabilities in digital, cloud and security. Combining unmatched experience and specialized skills across more than 40 industries, we offer Strategy and Consulting, Interactive, Technology and Operations services-all powered by the world's largest network of Advanced Technology and Intelligent Operations centers. Our 514,000 people deliver on the promise of technology and human ingenuity every day, serving clients in more than 120 countries. We embrace the power of change to create value and shared success for our clients, people, shareholders, partners and communities. Visit us at www.accenture.com  Accenture | Let there be change We embrace change to create 360-degree value www.accenture.com

Project Role :Application Developer

Project Role Description :Design, build and configure applications to meet business process and application requirements.

Management Level :10

Work Experience :4-6 years

Work location :Bengaluru

Must Have Skills :Google BigQuery

Good To Have Skills :Apache Spark,Google Cloud Platform Architecture,Java Enterprise Edition

Job Requirements : 

Key Responsibilities : 1 Create ETL pipeline using Bigquery, create best performing tables with partitioning/clustering etc enabled keeping best practices in mind 2 Write Complex SQL queries keeping execution cost in mind 3 Load data into BigQuery using files or by streaming one record at a time 4 Create, load, and query partitioned tables for daily time-series data 5 Implement fine-grained access control using roles and authorized views

Technical Experience : 1 Should have indepth understanding of Bigquery architecture, table partitioning, clustering, best practices, type of tables, best practices etc 2 Should know how to reduce BigQuery costs by reducing the amount of data processed by your queries 3 Should be able to speed up queries by using denormalized data structures, with or without nested repeated fields 4 Exploring and Preparing data using BigQuery 5 Implementing ETL jobs using Bigquery 6 Understanding of Bigquery ML

Professional Attributes : 1 Good communication and interpersonal skills 2 Strong writing skills and stakeholder management 3 Excellent problem-solving skills and mitigate technical issues

Educational Qualification : 15 years of full time education

Additional Information : desired skills -GCP, Big Query, Presto

15 years of full time education

Skills from this JD

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

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)
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 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 for Data Work
programming-languages-for-data-work
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
Partitioning 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
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Clustering Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: clustering id=162 · clustering

Aliases — catalog

  • clustering (CANONICAL) primary
  • Clustering (CANONICAL)

Context tags (catalog)

DBSCAN Gaussian mixture Gaussian mixture model PCA agglomerative centroid cluster analysis clustering algorithms data partitioning dendrogram density-based dimensionality reduction distance metric elbow method feature extraction feature scaling hierarchical hierarchical clustering k-means outlier detection silhouette score spectral clustering unsupervised learning

Stored enrichment (catalog DB)

Category
Concept
Sub-category
Distributed Systems Concept
Confidence
0.72
Version strategy
NOT_APPLICABLE

Maturity reasoning: Clustering is a standard distributed-systems concept and appears broadly in JDs for databases, Kubernetes, and load-balanced services; vendor docs for AWS, Kubernetes, and PostgreSQL all treat clustering as a common production pattern.

Skill profile (library / DB)

Skill nature
CONCEPT
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
2
Sub-category id
1053
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Concurrency and Parallel Processing Catalog dimension db id 17

    Library dimension (catalog)

    Roles linked in library: Backend Developer, Java Backend Developer, Node.js Backend Developer, Ruby Backend Developer, Scala Backend Developer

  • Performance and Cost Optimization Catalog dimension db id 33

    Library dimension (catalog)

    Roles linked in library: Data Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Concurrency and Parallel Processing
concurrency-and-parallel-processing
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Performance and Cost Optimization
performance-and-cost-optimization
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Streaming Primary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

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

Derived legacy fields
Category
Data Engineering Tools
Sub-category
general
Skill nature
CONCEPT
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Role-Based Access Control Primary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

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

Derived legacy fields
Category
Security Tools
Sub-category
general
Skill nature
CONCEPT
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Authorized Views 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
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Denormalized Data Structures 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
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Nested Repeated Fields 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
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
BigQuery ML 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
Skipped — no persistable v3 meta for new skill
skill_not_in_db_v3_proposed

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
BigQuery in_db
Cloud Data Warehouses
cloud-data-warehouses
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 for Data Work
programming-languages-for-data-work
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Clustering in_db
Concurrency and Parallel Processing
concurrency-and-parallel-processing
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Clustering in_db
Performance and Cost Optimization
performance-and-cost-optimization
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
BigQuery ML new
Cloud Data Warehouses
cloud-data-warehouses
Skipped — no persistable v3 meta for new skill skill_not_in_db_v3_proposed

Library artifacts (this run)

Kind Detail DB id
canonical_skill_proposed ETL | type=Data Engineering Tools subtype=general nature=PRACTICE lifespan=MULTI_YEAR
canonical_skill_proposed Partitioning | type=Databases subtype=general nature=CONCEPT lifespan=MULTI_YEAR
canonical_skill_proposed Streaming | type=Data Engineering Tools subtype=general nature=CONCEPT lifespan=MULTI_YEAR
canonical_skill_proposed Role-Based Access Control | type=Security Tools subtype=general nature=CONCEPT lifespan=MULTI_YEAR
canonical_skill_proposed Authorized Views | type=Databases subtype=general nature=CONCEPT lifespan=MULTI_YEAR
canonical_skill_proposed Denormalized Data Structures | type=Databases subtype=general nature=CONCEPT lifespan=MULTI_YEAR
canonical_skill_proposed Nested Repeated Fields | type=Databases subtype=general nature=CONCEPT lifespan=MULTI_YEAR
dimension_skill_link_proposed BigQuery ML ↔ Cloud Data Warehouses
nano JD Parser — gpt-4.1-nano click to toggle
RoleApplication Developer
CompanyAccenture
Experience4-6 years
DomainIT Services & Consulting
Location Bengaluru, India
JD type pass
Show raw JSON
{
  "JD_type": "pass",
  "about_company": {
    "source_marker": {
      "first_5_words": "Accenture is a global professional",
      "last_5_words": "and shared success for our clients"
    },
    "text": "Accenture is a global professional services company with leading capabilities in digital, cloud and security. Combining unmatched experience and specialized skills across more than 40 industries, we offer Strategy and Consulting, Interactive, Technology and Operations services-all powered by the world\u0027s largest network of Advanced Technology and Intelligent Operations centers. Our 514,000 people deliver on the promise of technology and human ingenuity every day, serving clients in more than 120 countries. We embrace the power of change to create value and shared success for our clients, people, shareholders, partners and communities.",
    "word_count": 84
  },
  "certifications": [],
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  "ctc": null,
  "domain": {
    "primary": {
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        "ITES",
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      "domain": "IT Services \u0026 Consulting"
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    "secondary": null
  },
  "education": [
    {
      "level": "Bachelor\u0027s",
      "qualification": "Bachelor\u0027s - Any Discipline",
      "raw": "15 years of full time education",
      "requirement": "required"
    }
  ],
  "experience": {
    "max": 6,
    "min": 4,
    "raw": "4-6 years"
  },
  "job_locations": [
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        "Bangalore"
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      "state": null,
      "work_mode": null
    }
  ],
  "role": "Application Developer",
  "role_aliases": [
    "App Developer",
    "Software Developer",
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  ],
  "role_archetype": "Engineering",
  "roles_and_responsibilities": [
    {
      "bullet_count": 5,
      "heading": "Key Responsibilities",
      "heading_was_present": true,
      "source_marker": {
        "first_5_words": "1 Create ETL pipeline using",
        "last_5_words": "roles and authorized views"
      },
      "text": "1 Create ETL pipeline using Bigquery, create best performing tables with partitioning/clustering etc enabled keeping best practices in mind 2 Write Complex SQL queries keeping execution cost in mind 3 Load data into BigQuery using files or by streaming one record at a time 4 Create, load, and query partitioned tables for daily time-series data 5 Implement fine-grained access control using roles and authorized views",
      "word_count": 56
    },
    {
      "bullet_count": 6,
      "heading": "Technical Experience",
      "heading_was_present": true,
      "source_marker": {
        "first_5_words": "1 Should have indepth understanding",
        "last_5_words": "of Bigquery ML"
      },
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      "bullet_count": 3,
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      "word_count": 27
    }
  ],
  "urls": [
    {
      "type": "website",
      "url": "www.accenture.com"
    }
  ]
}
API 1 — extract-from-jd click to toggle
{
  "final_skills": [
    {
      "is_primary": true,
      "skill_name": "BigQuery"
    },
    {
      "is_primary": true,
      "skill_name": "SQL"
    },
    {
      "is_primary": true,
      "skill_name": "ETL"
    },
    {
      "is_primary": true,
      "skill_name": "Partitioning"
    },
    {
      "is_primary": true,
      "skill_name": "Clustering"
    },
    {
      "is_primary": true,
      "skill_name": "Streaming"
    },
    {
      "is_primary": true,
      "skill_name": "Role-Based Access Control"
    },
    {
      "is_primary": true,
      "skill_name": "Authorized Views"
    },
    {
      "is_primary": true,
      "skill_name": "Denormalized Data Structures"
    },
    {
      "is_primary": true,
      "skill_name": "Nested Repeated Fields"
    },
    {
      "is_primary": true,
      "skill_name": "BigQuery ML"
    }
  ],
  "jd_role": {
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    "rationale": null,
    "role_aliases": [
      "App Developer",
      "Software Developer",
      "Application Engineer"
    ],
    "role_archetype": "Engineering",
    "slug": ""
  },
  "nano_parsed": {
    "JD_type": "pass",
    "about_company": {
      "source_marker": {
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      "word_count": 84
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    "certifications": [],
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    "ctc": null,
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    "experience": {
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      }
    ],
    "role": "Application Developer",
    "role_aliases": [
      "App Developer",
      "Software Developer",
      "Application Engineer"
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    "role_archetype": "Engineering",
    "roles_and_responsibilities": [
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        "bullet_count": 5,
        "heading": "Key Responsibilities",
        "heading_was_present": true,
        "source_marker": {
          "first_5_words": "1 Create ETL pipeline using",
          "last_5_words": "roles and authorized views"
        },
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        "word_count": 56
      },
      {
        "bullet_count": 6,
        "heading": "Technical Experience",
        "heading_was_present": true,
        "source_marker": {
          "first_5_words": "1 Should have indepth understanding",
          "last_5_words": "of Bigquery ML"
        },
        "text": "1 Should have indepth understanding of Bigquery architecture, table partitioning, clustering, best practices, type of tables, best practices etc 2 Should know how to reduce BigQuery costs by reducing the amount of data processed by your queries 3 Should be able to speed up queries by using denormalized data structures, with or without nested repeated fields 4 Exploring and Preparing data using BigQuery 5 Implementing ETL jobs using Bigquery 6 Understanding of Bigquery ML",
        "word_count": 83
      },
      {
        "bullet_count": 3,
        "heading": "Professional Attributes",
        "heading_was_present": true,
        "source_marker": {
          "first_5_words": "1 Good communication and interpersonal",
          "last_5_words": "mitigate technical issues"
        },
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        "word_count": 27
      }
    ],
    "urls": [
      {
        "type": "website",
        "url": "www.accenture.com"
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  },
  "rejected": false,
  "rejection_reason": null,
  "run_id": "39cf4d23-848c-4261-a968-cb764de2537f",
  "stage3_signals": {
    "alias_found": true,
    "alias_match_roles": [
      {
        "display_name": "Backend Developer",
        "kra_matches": null,
        "matched_count": null,
        "matched_skills": null,
        "role_id": 1,
        "score": 1.0,
        "slug": "backend-engineer",
        "total_count": null
      }
    ],
    "kra_match_roles": [
      {
        "display_name": "Data Engineer",
        "kra_matches": [
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            "kra_text": "Optimizes pipeline throughput, partitioning strategies, and query performance across cloud data warehouses like Snowflake, BigQuery, or Redshift.",
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      {
        "display_name": "Cloud Architect",
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        "total_count": null
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      {
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        "kra_matches": [
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            "kra_text": "Automates ML platform operations including scheduled retraining triggers, pipeline orchestration, evaluation workflows, and alerting configuration.",
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    ],
    "skill_match_roles": [
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        "display_name": "Data Engineer",
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          "BigQuery",
          "SQL",
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        ],
        "role_id": 2,
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      },
      {
        "display_name": "Pega Developer",
        "kra_matches": null,
        "matched_count": 1,
        "matched_skills": [
          "SQL"
        ],
        "role_id": 24,
        "score": 0.0909,
        "slug": "pega-developer",
        "total_count": 11
      },
      {
        "display_name": "Backend Developer",
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          "clustering"
        ],
        "role_id": 1,
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      },
      {
        "display_name": "Node.js Backend Developer",
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      {
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          "clustering"
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    ]
  },
  "stage4_decision": {
    "alias_collision_detected": false,
    "case": "DOMAIN",
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    },
    "confidence": 0.92,
    "is_new_role": false,
    "llm2_fired": false,
    "llm2_reasoning": null,
    "matched_dimensions": [
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      "Table design and partitioning strategy",
      "Data loading and streaming ingestion",
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    "matched_kras": [
      "Create ETL pipeline using Bigquery",
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      "Load data into BigQuery using files or by streaming",
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      "Implement fine-grained access control using roles",
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    "matched_skills": [
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      "nested repeated fields"
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    "new_role_display_name": null,
    "new_role_slug": null,
    "queued": false,
    "reasoning": "Domain=Data Engineering \u0026 Analytics; The JD is centered on BigQuery warehouse design, partitioning/clustering, SQL performance, loading/querying tables, access control, and BigQuery ML, which best matches a Data Warehouse Engineer.",
    "sub_role": null
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  "stage5_updates": {
    "centroid_n_after": 8,
    "centroid_updated": true,
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    "new_kra_attached": {
      "best_kra_similarity": 0.0,
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      "status": "pending"
    },
    "new_skills_attached": [
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        "is_primary": true,
        "queue_id": 11481,
        "role_display_name": "Data Warehouse Engineer",
        "role_slug": "data-warehouse-engineer",
        "skill_name": "ETL",
        "status": "pending"
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      {
        "is_primary": true,
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        "role_display_name": "Data Warehouse Engineer",
        "role_slug": "data-warehouse-engineer",
        "skill_name": "Partitioning",
        "status": "pending"
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      {
        "is_primary": true,
        "queue_id": 11483,
        "role_display_name": "Data Warehouse Engineer",
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        "skill_name": "Streaming",
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      },
      {
        "is_primary": true,
        "queue_id": 11484,
        "role_display_name": "Data Warehouse Engineer",
        "role_slug": "data-warehouse-engineer",
        "skill_name": "Role-Based Access Control",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 11485,
        "role_display_name": "Data Warehouse Engineer",
        "role_slug": "data-warehouse-engineer",
        "skill_name": "Authorized Views",
        "status": "pending"
      },
      {
        "is_primary": true,
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        "role_display_name": "Data Warehouse Engineer",
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        "is_primary": true,
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        "skill_name": "Nested Repeated Fields",
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      },
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    "queue_entry_id": null,
    "v3_pipeline_triggered": false,
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    "v3_run_id": null
  }
}
API 2 — extract-details
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        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "CLOUD_SERVICE",
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        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
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      "matched_via": "alias"
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      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
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        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "LANGUAGE",
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        "typical_lifespan": "EVERGREEN",
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      "matched_via": "alias"
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    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
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        "is_extractable": true,
        "skill_nature": "CONCEPT",
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        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "TODO: REMOVE AFTER TESTING \u2014 alias DB write disabled",
      "alias_persisted": false,
      "existing_alias_id": 300,
      "existing_alias_text": "BigQuery",
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        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
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      "matched_via": "embedding_alias"
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  "candidate_roles": [
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    },
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      "input_skill": "Clustering",
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    "Nested Repeated Fields",
    "BigQuery ML"
  ],
  "new_aliases_persisted": 0,
  "run_id": "39cf4d23-848c-4261-a968-cb764de2537f",
  "skills_detail": [
    {
      "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",
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        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "CLOUD_SERVICE",
        "slug": "bigquery",
        "sub_category_id": 118,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "dimensions": [
        {
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          },
          "input_skill": "BigQuery",
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          "roles_from_db": [
            {
              "display_name": "Data Engineer",
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            }
          ]
        }
      ],
      "input_skill": "BigQuery",
      "matched_via": "alias",
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      "new_alias_text": null,
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      "source_tag": "db",
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    },
    {
      "aliases_in_db": [
        {
          "alias_text": "SQL",
          "alias_type": "CANONICAL",
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          "match_strategy": "CASE_INSENSITIVE"
        }
      ],
      "canonical": {
        "category_id": 6,
        "display_name": "SQL",
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        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "LANGUAGE",
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        "sub_category_id": 97,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "dimensions": [
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Pega Programming Languages \u0026 DSLs",
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            "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 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": "ETL",
      "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": [],
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        "relationships": null,
        "skill_id": "etl",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "Partitioning",
      "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": "MULTI_YEAR",
          "version_strategy": "UNVERSIONED",
          "volatility": "MEDIUM"
        },
        "enrichment": null,
        "keep_log": [],
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        "skill_id": "partitioning",
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        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [
        {
          "alias_text": "clustering",
          "alias_type": "CANONICAL",
          "id": 3841,
          "is_primary": true,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "Clustering",
          "alias_type": "CANONICAL",
          "id": 371,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        }
      ],
      "canonical": {
        "category_id": 2,
        "display_name": "clustering",
        "id": 162,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "CONCEPT",
        "slug": "clustering",
        "sub_category_id": 1053,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "dimensions": [
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Concurrency and Parallel Processing",
            "id": 17,
            "rationale": "Programming techniques for handling multiple requests and background work safely and efficiently. Includes synchronization, async execution, and coordination of concurrent tasks.",
            "slug": "concurrency-and-parallel-processing",
            "source": "db"
          },
          "input_skill": "Clustering",
          "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": "Node.js Backend Developer",
              "id": 82,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "node-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": "Performance and Cost Optimization",
            "id": 33,
            "rationale": "Techniques for improving the speed, reliability, and cost efficiency of data workloads. This includes query tuning, partitioning, file sizing, compute right-sizing, and workload management.",
            "slug": "performance-and-cost-optimization",
            "source": "db"
          },
          "input_skill": "Clustering",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "Data Engineer",
              "id": 2,
              "rationale": null,
              "role_archetype": null,
              "slug": "data-engineer",
              "source": "db"
            }
          ]
        }
      ],
      "input_skill": "Clustering",
      "matched_via": "alias",
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": null,
      "source_tag": "db",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "Streaming",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Data Engineering Tools",
          "skill_nature": "CONCEPT",
          "sub_category": "general",
          "typical_lifespan": "MULTI_YEAR",
          "version_strategy": "UNVERSIONED",
          "volatility": "MEDIUM"
        },
        "enrichment": null,
        "keep_log": [],
        "locked_dimensions": [],
        "merge_log": [],
        "placed": null,
        "relationships": null,
        "skill_id": "streaming",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "Role-Based Access Control",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Security 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": "role-based-access-control",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "Authorized Views",
      "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": "MULTI_YEAR",
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          "volatility": "MEDIUM"
        },
        "enrichment": null,
        "keep_log": [],
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        "merge_log": [],
        "placed": null,
        "relationships": null,
        "skill_id": "authorized-views",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "Denormalized Data Structures",
      "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": "MULTI_YEAR",
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          "volatility": "MEDIUM"
        },
        "enrichment": null,
        "keep_log": [],
        "locked_dimensions": [],
        "merge_log": [],
        "placed": null,
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        "skill_id": "denormalized-data-structures",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "Nested Repeated Fields",
      "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": "MULTI_YEAR",
          "version_strategy": "UNVERSIONED",
          "volatility": "MEDIUM"
        },
        "enrichment": null,
        "keep_log": [],
        "locked_dimensions": [],
        "merge_log": [],
        "placed": null,
        "relationships": null,
        "skill_id": "nested-repeated-fields",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "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 ML",
          "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 ML",
      "matched_via": "embedding_alias",
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": null,
      "source_tag": "db",
      "was_in_llm_skills": true
    }
  ],
  "unmatched_skills": [
    "ETL",
    "Partitioning",
    "Streaming",
    "Role-Based Access Control",
    "Authorized Views",
    "Denormalized Data Structures",
    "Nested Repeated Fields"
  ]
}
API 3 — final-role-output
{
  "chosen_role": {
    "display_name": "Data Warehouse Engineer",
    "id": 144,
    "rationale": "Domain=Data Engineering \u0026 Analytics; The JD is centered on BigQuery warehouse design, partitioning/clustering, SQL performance, loading/querying tables, access control, and BigQuery ML, which best matches a Data Warehouse Engineer.",
    "role_archetype": null,
    "slug": "data-warehouse-engineer",
    "source": "db"
  },
  "chosen_role_resolution": "in_db",
  "final_input_skills": [
    {
      "skill": "BigQuery",
      "tag": "in_db"
    },
    {
      "skill": "SQL",
      "tag": "in_db"
    },
    {
      "skill": "ETL",
      "tag": "new"
    },
    {
      "skill": "Partitioning",
      "tag": "new"
    },
    {
      "skill": "Clustering",
      "tag": "in_db"
    },
    {
      "skill": "Streaming",
      "tag": "new"
    },
    {
      "skill": "Role-Based Access Control",
      "tag": "new"
    },
    {
      "skill": "Authorized Views",
      "tag": "new"
    },
    {
      "skill": "Denormalized Data Structures",
      "tag": "new"
    },
    {
      "skill": "Nested Repeated Fields",
      "tag": "new"
    },
    {
      "skill": "BigQuery ML",
      "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": 144,
        "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": 144,
        "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": 144,
        "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": 144,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Concurrency and Parallel Processing",
          "id": 17,
          "rationale": "Programming techniques for handling multiple requests and background work safely and efficiently. Includes synchronization, async execution, and coordination of concurrent tasks.",
          "slug": "concurrency-and-parallel-processing",
          "source": "db"
        },
        "dimension_id": 17,
        "input_skill": "Clustering",
        "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": "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": "Node.js Backend Developer",
            "id": 82,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "node-backend-developer",
            "source": "db"
          },
          {
            "display_name": "Ruby Backend Developer",
            "id": 85,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "ruby-backend-developer",
            "source": "db"
          },
          {
            "display_name": "Scala Backend Developer",
            "id": 87,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "scala-backend-developer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 162,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 144,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Performance and Cost Optimization",
          "id": 33,
          "rationale": "Techniques for improving the speed, reliability, and cost efficiency of data workloads. This includes query tuning, partitioning, file sizing, compute right-sizing, and workload management.",
          "slug": "performance-and-cost-optimization",
          "source": "db"
        },
        "dimension_id": 33,
        "input_skill": "Clustering",
        "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": 162,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 144,
        "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 ML",
        "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": "Data Engineer",
            "id": 2,
            "rationale": null,
            "role_archetype": null,
            "slug": "data-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": false,
        "skill_id": null,
        "skill_tag": "new",
        "skipped_reason": "skill_not_in_db_v3_proposed"
      }
    ],
    "new_skills_created": 0,
    "role_dimension_saved": 0,
    "skill_dimension_saved": 0,
    "skipped": 1
  },
  "planner_output": null,
  "run_id": "39cf4d23-848c-4261-a968-cb764de2537f"
}

LLM Calls

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

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