← Back to history

Pipeline run

b0c1edb4-ff3d-478c-aa20-e942622f535d

Pipeline LLM cost (USD)
API 1: $0.0089 API 2: $0.0005 API 3: $0.0000 Total: $0.0094

Client output enrichment

v2 Skill cluster · Nature of work · AI index · Tech stack maturity · Evidence · KRA description
role baseline loaded sources · ai_index: jd · nature_of_work: jd · tech_stack_maturity: jd
Nature of work · Data transformation and modeling
Works with clients to gather data warehouse needs, design and productionize ELT/data warehouse automation in WhereScape RED/3D, and migrate legacy RDBMS warehouses to cloud platforms like Snowflake, Azure SQL, and BigQuery using Dimensional/Data Vault models.
""Provide Data Warehouse Automation solutions Develop, schedule & productionize the ELT""
Tech stack maturity
Mainstream Modern
The skill set centers on established SQL/data-warehouse technologies like Snowflake, Hive, and PL/SQL, which indicates a modern but not bleeding-edge or legacy-only stack.
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 (22)
WhereScape RED WhereScape 3D Oracle Teradata Snowflake Azure SQL Hive Amazon Google BigQuery Dimensional Modeling Data Vault ETL ELT Data Warehouse Automation Data Warehousing RDBMS SQL PL/SQL SSIS SSRS Power BI Scheduling Tool
Skill cluster (5 dimension groups, role-scoped)
BI and Visualization Tools
Power BI
Cloud Data Warehouses
Snowflake
Data Modeling and Schema Design
Dimensional Modeling
Programming Languages for Data Work
SQL
Cross-cutting / unaligned
WhereScape RED WhereScape 3D Oracle Teradata Azure SQL Hive Amazon Google BigQuery Data Vault ETL ELT Data Warehouse Automation Data Warehousing RDBMS PL/SQL SSIS SSRS Scheduling Tool
Show KRA description ↓
We are looking for an ELT/Data Integration Consultant. The consultant will join a Professional Services Team who are responsible for developing Data Warehouse Automation solutions & supporting various customers to provide technical expertise on an ELT Automation tool. The role is focused on automation tools WhereScape RED and 3D integrated with various traditional & cloud platforms like Oracle, Teradata, Snowflake, Azure SQL, Hive, Amazon, Google Big query, etc. applied in the areas of Dimensional as well as Data Vault modeling techniques. Requirement gathering & understanding of client Data Warehouse needs Provide Data Warehouse Automation solutions Develop, schedule & productionize the ELT Conduct Proof of Concepts (POC) for clients in assistance with Solutions Architect Migration of Data Warehouse systems from traditional RDBMS platforms to Cloud platforms Must have excellent command over communication (Written and Verbal) Knowledge of Data Warehousing modeling techniques- Kimball- Dimensional modeling or Data Vault 2.0 Should have extensively worked in any of the ETL/ELT tools Good understanding of any Database Architectures/RDBMS Excellent SQL & PL/SQL skills Should have worked on SSIS and SSRS Experience in any scheduling tool Good problem-solving capability and self-explore Knowledge of Power BI

Signals

Skill data-engineer
0.15
Alias
KRA data-engineer
0.57

Post-classification

Centroidupdated · n=311
Alias collision log
New-role queue
New skills captured15
New KRA captured

Captured for admin review

WhereScape RED primary Data Engineer pending
WhereScape 3D primary Data Engineer pending
Oracle primary Data Engineer pending
Teradata primary Data Engineer pending
Azure SQL primary Data Engineer pending
Amazon primary Data Engineer pending
Google BigQuery primary Data Engineer pending
Data Vault primary Data Engineer pending
ETL primary Data Engineer pending
ELT primary Data Engineer pending
Data Warehouse Automation primary Data Engineer pending
Data Warehousing primary Data Engineer pending
SSIS primary Data Engineer pending
SSRS primary Data Engineer pending
Scheduling Tool Data Engineer pending
Status: completed Created: 2026-05-27T15:29:07.236010Z Updated: 2026-06-12T16:22:33.312035Z API 3 duration: 29937 ms
Flow Current 3-step pipeline

1 POST /skills/extract-from-jd

2 POST /skills/extract-details

3 POST /skills/final-role-output

Role Chosen role & resolution

Data Engineer

domain · Data Engineering & Analytics CASE DOMAIN

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

Domain=Data Engineering & Analytics; The JD is centered on ELT/data integration, data warehouse automation, migration, scheduling, and ETL/ELT tooling, which best matches a Data Engineer role.

Matched skills

WhereScape REDWhereScape 3DOracleTeradataSnowflakeAzure SQLHiveAmazonGoogle Big queryKimballData Vault 2.0PL/SQLSSISSSRSPower BI

Matched dimensions

Data Warehouse AutomationELT / Data IntegrationCloud Data Platform MigrationDimensional and Data Vault ModelingDatabase and SQL DevelopmentReporting and Analytics EnablementClient Requirement Gathering and SolutioningProof of Concept Delivery

Matched KRAs

developing Data Warehouse Automation solutionsProvide Data Warehouse Automation solutionsDevelop, schedule & productionize the ELTConduct Proof of Concepts (POC) for clientsMigration of Data Warehouse systems from traditional RDBMS platforms to Cloud platformsRequirement gathering & understanding of client Data Warehouse needssupporting various customers to provide technical expertise

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

We Have Immediate Opening - Data Warehouse Consultant (ETL).


Should have hands-on experience on ETL, SSIS, SSRS 4 + Years of experience mandatory.


Remote Working - Anywhere from India.


Job Description:


Position:   Data Warehouse Consultant (ETL) 


Type:  Fulltime 
  
Year of Experience: 4 - 8 Years 


Joining: Immediate Joining Preferred(Negotiable up to 15 Days).


Headline: 


We are looking for an ELT/Data Integration Consultant. The consultant will join a Professional Services Team who are responsible for developing Data Warehouse Automation solutions & supporting various customers to provide technical expertise on an ELT Automation tool. The role is focused on automation tools WhereScape RED and 3D integrated with various traditional & cloud platforms like Oracle, Teradata, Snowflake, Azure SQL, Hive, Amazon, Google Big query, etc. applied in the areas of Dimensional as well as Data Vault modeling techniques.


Principal Accountabilities: 
  
Requirement gathering & understanding of client Data Warehouse needs Provide Data Warehouse Automation solutions  Develop, schedule & productionize the ELT  Conduct Proof of Concepts (POC) for clients in assistance with Solutions Architect Migration of Data Warehouse systems from traditional RDBMS platforms to Cloud platforms  

Skills Required: 

Must have excellent command over communication (Written and Verbal) Knowledge of Data Warehousing modeling techniques- Kimball- Dimensional modeling or Data Vault 2.0   Should have extensively worked in any of the ETL/ELT tools  Good understanding of any Database Architectures/RDBMS Excellent SQL & PL/SQL skills Should have worked on SSIS and SSRS Experience in any scheduling tool Good problem-solving capability and self-explore Knowledge of Power BI 

Work Location & Culture: 
  
Full-time position Remote Working - Anywhere from India Flexible working hours 

Interested candidates;


Share your Latest CV on: hr@dataentrega.com 
&
Apply on: -https://www.dataentrega.com/careers/

Skills from this JD

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

WhereScape RED 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
TOOL
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
WhereScape 3D 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
TOOL
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Oracle 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
TOOL
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Teradata 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
TOOL
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Snowflake Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Snowflake id=105 · snowflake

Aliases — catalog

  • Snowflake (CANONICAL) primary

Context tags (catalog)

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

Stored enrichment (catalog DB)

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

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

Skill profile (library / DB)

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

Dimensions (API 2 worklist)

  • Cloud Data Warehouses Catalog dimension db id 22

    Library dimension (catalog)

    Roles linked in library: Data Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Cloud Data Warehouses
cloud-data-warehouses
Existing dimension (library) · Role↔dimension saved
Azure SQL 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
PLATFORM
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Hive Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Hive id=2754 · hive

Aliases — catalog

  • Hive (CANONICAL) primary

Context tags (catalog)

Apache Apache Hive Bucketing ETL HQL Hive Metastore Hive SerDe HiveQL MapReduce SQL SQL-on-Hadoop big data bucketing columnar storage data lakes data warehousing integration metadata partitioning schema evolution

Stored enrichment (catalog DB)

Category
Datastore
Sub-category
Local Key Value Store
Vendor
Apache Software Foundation
License
apache_2
Year introduced
2010
Confidence
0.90
Version strategy
NOT_APPLICABLE

Maturity reasoning: Hive appears in Flutter/mobile JDs and package docs, but JD volume is far below SQLite/Realm and it’s mainly used for local key-value storage in Flutter apps.

Skill profile (library / DB)

Skill nature
TOOL
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
3
Sub-category id
2242
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Local Persistence and Offline Behavior Catalog dimension db id 85

    Library dimension (catalog)

    Roles linked in library: Android Developer, Flutter Developer, Hybrid Mobile Developer, Native Mobile Developer, React Native Developer, iOS Developer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Local Persistence and Offline Behavior
local-persistence-and-offline-behavior
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Amazon 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
Cloud Platforms
Sub-category
general
Skill nature
PLATFORM
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Google 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
Skipped — no persistable v3 meta for new skill
skill_not_in_db_v3_proposed
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 saved
Data Vault 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 Warehousing
Sub-category
general
Skill nature
CONCEPT
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
ETL Primary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

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

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

Skill enrichment (orchestrator / LLM)

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

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

Skill enrichment (orchestrator / LLM)

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

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

Skill enrichment (orchestrator / LLM)

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

Derived legacy fields
Category
Data Warehousing
Sub-category
general
Skill nature
CONCEPT
Volatility
STABLE
Typical lifespan
EVERGREEN
Version strategy
UNVERSIONED
RDBMS Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: RDBMS id=1355 · rdbms

Aliases — catalog

  • RDBMS (CANONICAL)

Context tags (catalog)

ACID ER diagrams SQL backup and recovery data integrity data modeling database tuning foreign keys indexes joins normalization query optimization schema design stored procedures transactions views

Stored enrichment (catalog DB)

Category
Datastore
Sub-category
Relational Database Management System
Confidence
0.98
Version strategy
NOT_APPLICABLE

Maturity reasoning: RDBMS is a core requirement in many job descriptions across backend, data, and DBA roles; PostgreSQL, MySQL, and SQL Server remain standard enterprise stacks.

Skill profile (library / DB)

Skill nature
TOOL
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
3
Sub-category id
1023
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • React Frontend Development Catalog dimension db id 96

    Library dimension (catalog)

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
React Frontend Development
d_init_01
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
SQL Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: SQL id=101 · sql

Aliases — catalog

  • SQL (CANONICAL) primary

Context tags (catalog)

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

Stored enrichment (catalog DB)

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

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

Skill profile (library / DB)

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

Dimensions (API 2 worklist)

  • Pega Programming Languages & DSLs Catalog dimension db id 267

    Library dimension (catalog)

    Roles linked in library: Pega Developer

  • Programming Languages & DSLs Catalog dimension db id 475

    Library dimension (catalog)

    Roles linked in library: Engineering Manager

  • Programming Languages for Data Work Catalog dimension db id 21

    Library dimension (catalog)

    Roles linked in library: Data Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Pega Programming Languages & DSLs
pega-programming-languages-dsls
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Programming Languages & DSLs
programming-languages-dsls
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Programming Languages for Data Work
programming-languages-for-data-work
Existing dimension (library) · Role↔dimension saved
PL/SQL Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: PL/SQL id=1567 · pl-sql

Aliases — catalog

  • PL/SQL (CANONICAL)

Context tags (catalog)

Oracle PL/SQL blocks SQL*Plus bulk collect cursors data manipulation data types dynamic SQL exception handling functions packages performance tuning stored procedures transaction control triggers

Stored enrichment (catalog DB)

Category
Language
Sub-category
Procedural Sql Language
Vendor
Oracle Corporation
License
proprietary
Year introduced
1990
Confidence
0.99
Version strategy
NOT_APPLICABLE

Maturity reasoning: PL/SQL appears frequently in Oracle-focused job postings and remains a standard skill for Oracle database development and maintenance; it is not sunset or replaced by a newer successor.

Skill profile (library / DB)

Skill nature
LANGUAGE
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
6
Sub-category id
1173
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • React Frontend Development Catalog dimension db id 96

    Library dimension (catalog)

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
React Frontend Development
d_init_01
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
SSIS 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
TOOL
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
SSRS 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
TOOL
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Power BI Secondary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Power BI id=151 · power-bi

Aliases — catalog

  • Power BI (CANONICAL) primary

Context tags (catalog)

Azure Synapse DAX DirectQuery Import mode M language Power Query RLS SQL Server SSAS dashboard data modeling data warehouse gateway reporting star schema

Stored enrichment (catalog DB)

Category
Platform
Sub-category
Bi Analytics Platform
Vendor
Microsoft
License
proprietary
Year introduced
2015
Confidence
0.96
Version strategy
NOT_APPLICABLE

Maturity reasoning: Power BI appears frequently in BI/data analyst job descriptions and is a standard Microsoft analytics platform in enterprise stacks, with strong vendor support and broad adoption.

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 saved
Scheduling Tool Secondary 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
TOOL
Volatility
FAST
Typical lifespan
SHORT_LIVED
Version strategy
VERSIONED

All API 3 persistence rows

Same grid as the skill-extractor “Persistence items” table: one row per (skill × dimension) work item.

Skill Tag Dimension Skill↔dim Role↔dim Outcome Notes
Snowflake in_db
Cloud Data Warehouses
cloud-data-warehouses
Existing dimension (library) · Role↔dimension saved
Hive in_db
Local Persistence and Offline Behavior
local-persistence-and-offline-behavior
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Google BigQuery new
Cloud Data Warehouses
cloud-data-warehouses
Skipped — no persistable v3 meta for new skill skill_not_in_db_v3_proposed
Dimensional Modeling in_db
Data Modeling and Schema Design
data-modeling-and-schema-design
Existing dimension (library) · Role↔dimension saved
RDBMS in_db
React Frontend Development
d_init_01
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
SQL in_db
Pega Programming Languages & DSLs
pega-programming-languages-dsls
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
SQL in_db
Programming Languages & DSLs
programming-languages-dsls
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
SQL in_db
Programming Languages for Data Work
programming-languages-for-data-work
Existing dimension (library) · Role↔dimension saved
PL/SQL in_db
React Frontend Development
d_init_01
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Power BI in_db
BI and Visualization Tools
bi-and-visualization-tools
Existing dimension (library) · Role↔dimension saved

Library artifacts (this run)

Kind Detail DB id
canonical_skill_proposed WhereScape RED | type=Data Engineering Tools subtype=general nature=TOOL lifespan=MULTI_YEAR
canonical_skill_proposed WhereScape 3D | type=Data Engineering Tools subtype=general nature=TOOL lifespan=MULTI_YEAR
canonical_skill_proposed Oracle | type=Databases subtype=general nature=TOOL lifespan=MULTI_YEAR
canonical_skill_proposed Teradata | type=Databases subtype=general nature=TOOL lifespan=MULTI_YEAR
canonical_skill_proposed Azure SQL | type=Databases subtype=general nature=PLATFORM lifespan=MULTI_YEAR
canonical_skill_proposed Amazon | type=Cloud Platforms subtype=general nature=PLATFORM lifespan=MULTI_YEAR
canonical_skill_proposed Data Vault | type=Data Warehousing subtype=general nature=CONCEPT lifespan=MULTI_YEAR
canonical_skill_proposed ETL | type=Data Engineering Tools subtype=general nature=PRACTICE lifespan=MULTI_YEAR
canonical_skill_proposed ELT | type=Data Engineering Tools subtype=general nature=PRACTICE lifespan=MULTI_YEAR
canonical_skill_proposed Data Warehouse Automation | type=Data Engineering Tools subtype=general nature=PRACTICE lifespan=MULTI_YEAR
canonical_skill_proposed Data Warehousing | type=Data Warehousing subtype=general nature=CONCEPT lifespan=EVERGREEN
canonical_skill_proposed SSIS | type=Data Engineering Tools subtype=general nature=TOOL lifespan=MULTI_YEAR
canonical_skill_proposed SSRS | type=Data Engineering Tools subtype=general nature=TOOL lifespan=MULTI_YEAR
canonical_skill_proposed Scheduling Tool | type=Data Engineering Tools subtype=general nature=TOOL lifespan=SHORT_LIVED
dimension_skill_link_proposed Google BigQuery ↔ Cloud Data Warehouses
role_dimension_link_proposed Data Engineer ↔ Cloud Data Warehouses
nano JD Parser — gpt-4.1-nano click to toggle
RoleData Warehouse Consultant (ETL)
CompanyData Entrega
Experience4 - 8 Years
DomainIT Services & Consulting
Location India (remote)
JD type pass
Show raw JSON
{
  "JD_type": "pass",
  "about_company": null,
  "certifications": [],
  "company_name": "Data Entrega",
  "ctc": null,
  "domain": {
    "primary": {
      "aliases": [
        "ITES",
        "BPO",
        "Tech Consulting"
      ],
      "domain": "IT Services \u0026 Consulting"
    },
    "secondary": null
  },
  "education": [],
  "experience": {
    "max": 8,
    "min": 4,
    "raw": "4 - 8 Years"
  },
  "job_locations": [
    {
      "aliases": [],
      "city": null,
      "country": "India",
      "state": null,
      "work_mode": "remote"
    }
  ],
  "role": "Data Warehouse Consultant (ETL)",
  "role_aliases": [
    "Data Warehouse Consultant",
    "ETL Consultant",
    "Data Integration Consultant"
  ],
  "role_archetype": "Data",
  "roles_and_responsibilities": [
    {
      "bullet_count": 0,
      "heading": "Headline",
      "heading_was_present": true,
      "source_marker": {
        "first_5_words": "We are looking for an",
        "last_5_words": "modeling techniques."
      },
      "text": "We are looking for an ELT/Data Integration Consultant. The consultant will join a Professional Services Team who are responsible for developing Data Warehouse Automation solutions \u0026 supporting various customers to provide technical expertise on an ELT Automation tool. The role is focused on automation tools WhereScape RED and 3D integrated with various traditional \u0026 cloud platforms like Oracle, Teradata, Snowflake, Azure SQL, Hive, Amazon, Google Big query, etc. applied in the areas of Dimensional as well as Data Vault modeling techniques.",
      "word_count": 64
    },
    {
      "bullet_count": 0,
      "heading": "Principal Accountabilities",
      "heading_was_present": true,
      "source_marker": {
        "first_5_words": "Requirement gathering \u0026 understanding",
        "last_5_words": "to Cloud platforms"
      },
      "text": "Requirement gathering \u0026 understanding of client Data Warehouse needs Provide Data Warehouse Automation solutions Develop, schedule \u0026 productionize the ELT Conduct Proof of Concepts (POC) for clients in assistance with Solutions Architect Migration of Data Warehouse systems from traditional RDBMS platforms to Cloud platforms",
      "word_count": 38
    },
    {
      "bullet_count": 0,
      "heading": "Skills Required",
      "heading_was_present": true,
      "source_marker": {
        "first_5_words": "Must have excellent command over",
        "last_5_words": "and self-explore Knowledge of Power BI"
      },
      "text": "Must have excellent command over communication (Written and Verbal) Knowledge of Data Warehousing modeling techniques- Kimball- Dimensional modeling or Data Vault 2.0 Should have extensively worked in any of the ETL/ELT tools Good understanding of any Database Architectures/RDBMS Excellent SQL \u0026 PL/SQL skills Should have worked on SSIS and SSRS Experience in any scheduling tool Good problem-solving capability and self-explore Knowledge of Power BI",
      "word_count": 66
    }
  ],
  "urls": [
    {
      "type": "careers",
      "url": "https://www.dataentrega.com/careers/"
    }
  ]
}
API 1 — extract-from-jd click to toggle
{
  "final_skills": [
    {
      "is_primary": true,
      "skill_name": "WhereScape RED"
    },
    {
      "is_primary": true,
      "skill_name": "WhereScape 3D"
    },
    {
      "is_primary": true,
      "skill_name": "Oracle"
    },
    {
      "is_primary": true,
      "skill_name": "Teradata"
    },
    {
      "is_primary": true,
      "skill_name": "Snowflake"
    },
    {
      "is_primary": true,
      "skill_name": "Azure SQL"
    },
    {
      "is_primary": true,
      "skill_name": "Hive"
    },
    {
      "is_primary": true,
      "skill_name": "Amazon"
    },
    {
      "is_primary": true,
      "skill_name": "Google BigQuery"
    },
    {
      "is_primary": true,
      "skill_name": "Dimensional Modeling"
    },
    {
      "is_primary": true,
      "skill_name": "Data Vault"
    },
    {
      "is_primary": true,
      "skill_name": "ETL"
    },
    {
      "is_primary": true,
      "skill_name": "ELT"
    },
    {
      "is_primary": true,
      "skill_name": "Data Warehouse Automation"
    },
    {
      "is_primary": true,
      "skill_name": "Data Warehousing"
    },
    {
      "is_primary": true,
      "skill_name": "RDBMS"
    },
    {
      "is_primary": true,
      "skill_name": "SQL"
    },
    {
      "is_primary": true,
      "skill_name": "PL/SQL"
    },
    {
      "is_primary": true,
      "skill_name": "SSIS"
    },
    {
      "is_primary": true,
      "skill_name": "SSRS"
    },
    {
      "is_primary": false,
      "skill_name": "Power BI"
    },
    {
      "is_primary": false,
      "skill_name": "Scheduling Tool"
    }
  ],
  "jd_role": {
    "display_name": "Data Warehouse Consultant (ETL)",
    "rationale": null,
    "role_aliases": [
      "Data Warehouse Consultant",
      "ETL Consultant",
      "Data Integration Consultant"
    ],
    "role_archetype": "Data",
    "slug": ""
  },
  "nano_parsed": {
    "JD_type": "pass",
    "about_company": null,
    "certifications": [],
    "company_name": "Data Entrega",
    "ctc": null,
    "domain": {
      "primary": {
        "aliases": [
          "ITES",
          "BPO",
          "Tech Consulting"
        ],
        "domain": "IT Services \u0026 Consulting"
      },
      "secondary": null
    },
    "education": [],
    "experience": {
      "max": 8,
      "min": 4,
      "raw": "4 - 8 Years"
    },
    "job_locations": [
      {
        "aliases": [],
        "city": null,
        "country": "India",
        "state": null,
        "work_mode": "remote"
      }
    ],
    "role": "Data Warehouse Consultant (ETL)",
    "role_aliases": [
      "Data Warehouse Consultant",
      "ETL Consultant",
      "Data Integration Consultant"
    ],
    "role_archetype": "Data",
    "roles_and_responsibilities": [
      {
        "bullet_count": 0,
        "heading": "Headline",
        "heading_was_present": true,
        "source_marker": {
          "first_5_words": "We are looking for an",
          "last_5_words": "modeling techniques."
        },
        "text": "We are looking for an ELT/Data Integration Consultant. The consultant will join a Professional Services Team who are responsible for developing Data Warehouse Automation solutions \u0026 supporting various customers to provide technical expertise on an ELT Automation tool. The role is focused on automation tools WhereScape RED and 3D integrated with various traditional \u0026 cloud platforms like Oracle, Teradata, Snowflake, Azure SQL, Hive, Amazon, Google Big query, etc. applied in the areas of Dimensional as well as Data Vault modeling techniques.",
        "word_count": 64
      },
      {
        "bullet_count": 0,
        "heading": "Principal Accountabilities",
        "heading_was_present": true,
        "source_marker": {
          "first_5_words": "Requirement gathering \u0026 understanding",
          "last_5_words": "to Cloud platforms"
        },
        "text": "Requirement gathering \u0026 understanding of client Data Warehouse needs Provide Data Warehouse Automation solutions Develop, schedule \u0026 productionize the ELT Conduct Proof of Concepts (POC) for clients in assistance with Solutions Architect Migration of Data Warehouse systems from traditional RDBMS platforms to Cloud platforms",
        "word_count": 38
      },
      {
        "bullet_count": 0,
        "heading": "Skills Required",
        "heading_was_present": true,
        "source_marker": {
          "first_5_words": "Must have excellent command over",
          "last_5_words": "and self-explore Knowledge of Power BI"
        },
        "text": "Must have excellent command over communication (Written and Verbal) Knowledge of Data Warehousing modeling techniques- Kimball- Dimensional modeling or Data Vault 2.0 Should have extensively worked in any of the ETL/ELT tools Good understanding of any Database Architectures/RDBMS Excellent SQL \u0026 PL/SQL skills Should have worked on SSIS and SSRS Experience in any scheduling tool Good problem-solving capability and self-explore Knowledge of Power BI",
        "word_count": 66
      }
    ],
    "urls": [
      {
        "type": "careers",
        "url": "https://www.dataentrega.com/careers/"
      }
    ]
  },
  "rejected": false,
  "rejection_reason": null,
  "run_id": "b0c1edb4-ff3d-478c-aa20-e942622f535d",
  "stage3_signals": {
    "alias_found": false,
    "alias_match_roles": [],
    "kra_match_roles": [
      {
        "display_name": "Data Engineer",
        "kra_matches": [
          {
            "kra_text": "Designs dimensional models, star schemas, data vault structures, and curated data mart tables to support BI tools and self-service analytics consumption.",
            "sentence": "Must have excellent command over communication (Written and Verbal) Knowledge of Data Warehousing modeling techniques- Kimball- Dimensional modeling or Data Vault 2.0 Should have extensively worked in any of the ETL/ELT tools Good understanding of any Database Architectures/RDBMS Excellent SQL \u0026 PL/SQL skills Should have worked on SSIS and SSRS Experience in any scheduling tool Good problem-solving capability and self-explore Knowledge of Power BI",
            "similarity": 0.5955
          },
          {
            "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": "The role is focused on automation tools WhereScape RED and 3D integrated with various traditional \u0026 cloud platforms like Oracle, Teradata, Snowflake, Azure SQL, Hive, Amazon, Google Big query, etc. applied in the areas of Dimensional as well as Data Vault modeling techniques.",
            "similarity": 0.5725
          },
          {
            "kra_text": "Works with data analysts, data scientists, and business stakeholders to define data models, ingestion schedules, and data delivery requirements.",
            "sentence": "Requirement gathering \u0026 understanding of client Data Warehouse needs Provide Data Warehouse Automation solutions Develop, schedule \u0026 productionize the ELT Conduct Proof of Concepts (POC) for clients in assistance with Solutions Architect Migration of Data Warehouse systems from traditional RDBMS platforms to Cloud platforms",
            "similarity": 0.5517
          }
        ],
        "matched_count": null,
        "matched_skills": null,
        "role_id": 2,
        "score": 0.5732,
        "slug": "data-engineer",
        "total_count": null
      },
      {
        "display_name": "Cloud Architect",
        "kra_matches": [
          {
            "kra_text": "Defines cloud adoption roadmaps, lift-and-shift vs. refactor migration strategies, and landing zone architectures for workloads moving to AWS, Azure, or GCP.",
            "sentence": "Requirement gathering \u0026 understanding of client Data Warehouse needs Provide Data Warehouse Automation solutions Develop, schedule \u0026 productionize the ELT Conduct Proof of Concepts (POC) for clients in assistance with Solutions Architect Migration of Data Warehouse systems from traditional RDBMS platforms to Cloud platforms",
            "similarity": 0.5091
          },
          {
            "kra_text": "Conducts architecture reviews, approves technical design documents, and guides engineering teams through cloud migration and modernization projects.",
            "sentence": "The role is focused on automation tools WhereScape RED and 3D integrated with various traditional \u0026 cloud platforms like Oracle, Teradata, Snowflake, Azure SQL, Hive, Amazon, Google Big query, etc. applied in the areas of Dimensional as well as Data Vault modeling techniques.",
            "similarity": 0.4421
          },
          {
            "kra_text": "Conducts architecture reviews, approves technical design documents, and guides engineering teams through cloud migration and modernization projects.",
            "sentence": "The consultant will join a Professional Services Team who are responsible for developing Data Warehouse Automation solutions \u0026 supporting various customers to provide technical expertise on an ELT Automation tool.",
            "similarity": 0.4109
          }
        ],
        "matched_count": null,
        "matched_skills": null,
        "role_id": 9,
        "score": 0.454,
        "slug": "cloud-architect",
        "total_count": null
      },
      {
        "display_name": "MLOps Engineer",
        "kra_matches": [
          {
            "kra_text": "Automates ML platform operations including scheduled retraining triggers, pipeline orchestration, evaluation workflows, and alerting configuration.",
            "sentence": "Requirement gathering \u0026 understanding of client Data Warehouse needs Provide Data Warehouse Automation solutions Develop, schedule \u0026 productionize the ELT Conduct Proof of Concepts (POC) for clients in assistance with Solutions Architect Migration of Data Warehouse systems from traditional RDBMS platforms to Cloud platforms",
            "similarity": 0.439
          },
          {
            "kra_text": "Orchestrates model serving deployments to production using Kubernetes, MLflow Model Registry, SageMaker, or Kubeflow Serving infrastructure.",
            "sentence": "The role is focused on automation tools WhereScape RED and 3D integrated with various traditional \u0026 cloud platforms like Oracle, Teradata, Snowflake, Azure SQL, Hive, Amazon, Google Big query, etc. applied in the areas of Dimensional as well as Data Vault modeling techniques.",
            "similarity": 0.4077
          },
          {
            "kra_text": "Automates ML platform operations including scheduled retraining triggers, pipeline orchestration, evaluation workflows, and alerting configuration.",
            "sentence": "The consultant will join a Professional Services Team who are responsible for developing Data Warehouse Automation solutions \u0026 supporting various customers to provide technical expertise on an ELT Automation tool.",
            "similarity": 0.3986
          }
        ],
        "matched_count": null,
        "matched_skills": null,
        "role_id": 16,
        "score": 0.4151,
        "slug": "ml-ops-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": "The role is focused on automation tools WhereScape RED and 3D integrated with various traditional \u0026 cloud platforms like Oracle, Teradata, Snowflake, Azure SQL, Hive, Amazon, Google Big query, etc. applied in the areas of Dimensional as well as Data Vault modeling techniques.",
            "similarity": 0.449
          },
          {
            "kra_text": "Designs and queries relational databases like PostgreSQL and document stores like MongoDB, writing migrations, indexes, and optimized queries.",
            "sentence": "Requirement gathering \u0026 understanding of client Data Warehouse needs Provide Data Warehouse Automation solutions Develop, schedule \u0026 productionize the ELT Conduct Proof of Concepts (POC) for clients in assistance with Solutions Architect Migration of Data Warehouse systems from traditional RDBMS platforms to Cloud platforms",
            "similarity": 0.4156
          },
          {
            "kra_text": "Designs and queries relational databases like PostgreSQL and document stores like MongoDB, writing migrations, indexes, and optimized queries.",
            "sentence": "Must have excellent command over communication (Written and Verbal) Knowledge of Data Warehousing modeling techniques- Kimball- Dimensional modeling or Data Vault 2.0 Should have extensively worked in any of the ETL/ELT tools Good understanding of any Database Architectures/RDBMS Excellent SQL \u0026 PL/SQL skills Should have worked on SSIS and SSRS Experience in any scheduling tool Good problem-solving capability and self-explore Knowledge of Power BI",
            "similarity": 0.3732
          }
        ],
        "matched_count": null,
        "matched_skills": null,
        "role_id": 15,
        "score": 0.4126,
        "slug": "full-stack-engineer",
        "total_count": null
      },
      {
        "display_name": "Svelte Frontend Developer",
        "kra_matches": [
          {
            "kra_text": "backend data integration",
            "sentence": "We are looking for an ELT/Data Integration Consultant.",
            "similarity": 0.4238
          },
          {
            "kra_text": "backend data integration",
            "sentence": "The consultant will join a Professional Services Team who are responsible for developing Data Warehouse Automation solutions \u0026 supporting various customers to provide technical expertise on an ELT Automation tool.",
            "similarity": 0.401
          },
          {
            "kra_text": "backend data integration",
            "sentence": "Requirement gathering \u0026 understanding of client Data Warehouse needs Provide Data Warehouse Automation solutions Develop, schedule \u0026 productionize the ELT Conduct Proof of Concepts (POC) for clients in assistance with Solutions Architect Migration of Data Warehouse systems from traditional RDBMS platforms to Cloud platforms",
            "similarity": 0.4008
          }
        ],
        "matched_count": null,
        "matched_skills": null,
        "role_id": 92,
        "score": 0.4085,
        "slug": "svelte-frontend-developer",
        "total_count": null
      }
    ],
    "skill_match_roles": [
      {
        "display_name": "Data Engineer",
        "kra_matches": null,
        "matched_count": 3,
        "matched_skills": [
          "Dimensional modeling",
          "SQL",
          "Snowflake"
        ],
        "role_id": 2,
        "score": 0.15,
        "slug": "data-engineer",
        "total_count": 20
      },
      {
        "display_name": "Android Developer",
        "kra_matches": null,
        "matched_count": 1,
        "matched_skills": [
          "Hive"
        ],
        "role_id": 4,
        "score": 0.05,
        "slug": "android-engineer",
        "total_count": 20
      },
      {
        "display_name": "iOS Developer",
        "kra_matches": null,
        "matched_count": 1,
        "matched_skills": [
          "Hive"
        ],
        "role_id": 6,
        "score": 0.05,
        "slug": "ios-engineer",
        "total_count": 20
      },
      {
        "display_name": "Hybrid Mobile Developer",
        "kra_matches": null,
        "matched_count": 1,
        "matched_skills": [
          "Hive"
        ],
        "role_id": 11,
        "score": 0.05,
        "slug": "hybrid-mobile-developer",
        "total_count": 20
      },
      {
        "display_name": "Pega Developer",
        "kra_matches": null,
        "matched_count": 1,
        "matched_skills": [
          "SQL"
        ],
        "role_id": 24,
        "score": 0.05,
        "slug": "pega-developer",
        "total_count": 20
      }
    ]
  },
  "stage4_decision": {
    "alias_collision_detected": false,
    "case": "DOMAIN",
    "chosen_role": {
      "display_name": "Data Engineer",
      "kra_matches": null,
      "matched_count": null,
      "matched_skills": null,
      "role_id": 2,
      "score": 0.88,
      "slug": "data-engineer",
      "total_count": null
    },
    "confidence": 0.88,
    "is_new_role": false,
    "llm2_fired": false,
    "llm2_reasoning": null,
    "matched_dimensions": [
      "Data Warehouse Automation",
      "ELT / Data Integration",
      "Cloud Data Platform Migration",
      "Dimensional and Data Vault Modeling",
      "Database and SQL Development",
      "Reporting and Analytics Enablement",
      "Client Requirement Gathering and Solutioning",
      "Proof of Concept Delivery"
    ],
    "matched_kras": [
      "developing Data Warehouse Automation solutions",
      "Provide Data Warehouse Automation solutions",
      "Develop, schedule \u0026 productionize the ELT",
      "Conduct Proof of Concepts (POC) for clients",
      "Migration of Data Warehouse systems from traditional RDBMS platforms to Cloud platforms",
      "Requirement gathering \u0026 understanding of client Data Warehouse needs",
      "supporting various customers to provide technical expertise"
    ],
    "matched_skills": [
      "WhereScape RED",
      "WhereScape 3D",
      "Oracle",
      "Teradata",
      "Snowflake",
      "Azure SQL",
      "Hive",
      "Amazon",
      "Google Big query",
      "Kimball",
      "Data Vault 2.0",
      "PL/SQL",
      "SSIS",
      "SSRS",
      "Power BI"
    ],
    "new_role_display_name": null,
    "new_role_slug": null,
    "queued": false,
    "reasoning": "Domain=Data Engineering \u0026 Analytics; The JD is centered on ELT/data integration, data warehouse automation, migration, scheduling, and ETL/ELT tooling, which best matches a Data Engineer role.",
    "sub_role": null
  },
  "stage5_updates": {
    "centroid_n_after": 311,
    "centroid_updated": true,
    "collision_log_id": null,
    "new_kra_attached": null,
    "new_skills_attached": [
      {
        "is_primary": true,
        "queue_id": 14658,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "WhereScape RED",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 14659,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "WhereScape 3D",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 14660,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "Oracle",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 14661,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "Teradata",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 14662,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "Azure SQL",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 14663,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "Amazon",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 14664,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "Google BigQuery",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 14665,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "Data Vault",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 14666,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "ETL",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 14667,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "ELT",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 14668,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "Data Warehouse Automation",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 14669,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "Data Warehousing",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 14670,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "SSIS",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 14671,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "SSRS",
        "status": "pending"
      },
      {
        "is_primary": false,
        "queue_id": 14672,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "Scheduling Tool",
        "status": "pending"
      }
    ],
    "queue_entry_id": null,
    "v3_pipeline_triggered": false,
    "v3_role_slug": null,
    "v3_run_id": null
  }
}
API 2 — extract-details
{
  "alias_matches": [
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 299,
      "existing_alias_text": "Snowflake",
      "input_term": "Snowflake",
      "matched_canonical": {
        "category_id": 9,
        "display_name": "Snowflake",
        "id": 105,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "PLATFORM",
        "slug": "snowflake",
        "sub_category_id": 113,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 4198,
      "existing_alias_text": "Hive",
      "input_term": "Hive",
      "matched_canonical": {
        "category_id": 3,
        "display_name": "Hive",
        "id": 2754,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "TOOL",
        "slug": "hive",
        "sub_category_id": 2242,
        "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",
      "input_term": "Google 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": "embedding_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",
        "id": 125,
        "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"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 2014,
      "existing_alias_text": "RDBMS",
      "input_term": "RDBMS",
      "matched_canonical": {
        "category_id": 3,
        "display_name": "RDBMS",
        "id": 1355,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "TOOL",
        "slug": "rdbms",
        "sub_category_id": 1023,
        "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": {
        "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"
      },
      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 2513,
      "existing_alias_text": "PL/SQL",
      "input_term": "PL/SQL",
      "matched_canonical": {
        "category_id": 6,
        "display_name": "PL/SQL",
        "id": 1567,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "LANGUAGE",
        "slug": "pl-sql",
        "sub_category_id": 1173,
        "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": 360,
      "existing_alias_text": "Power BI",
      "input_term": "Power BI",
      "matched_canonical": {
        "category_id": 9,
        "display_name": "Power BI",
        "id": 151,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "PLATFORM",
        "slug": "power-bi",
        "sub_category_id": 111,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "matched_via": "alias"
    }
  ],
  "candidate_roles": [
    {
      "display_name": "Data Engineer",
      "id": 2,
      "rationale": null,
      "role_archetype": null,
      "slug": "data-engineer",
      "source": "db"
    },
    {
      "display_name": "Android Developer",
      "id": 4,
      "rationale": null,
      "role_archetype": null,
      "slug": "android-engineer",
      "source": "db"
    },
    {
      "display_name": "Flutter Developer",
      "id": 74,
      "rationale": null,
      "role_archetype": "Engineering",
      "slug": "flutter-developer",
      "source": "db"
    },
    {
      "display_name": "Hybrid Mobile Developer",
      "id": 11,
      "rationale": null,
      "role_archetype": null,
      "slug": "hybrid-mobile-developer",
      "source": "db"
    },
    {
      "display_name": "Native Mobile Developer",
      "id": 75,
      "rationale": null,
      "role_archetype": "Engineering",
      "slug": "native-mobile-developer",
      "source": "db"
    },
    {
      "display_name": "React Native Developer",
      "id": 73,
      "rationale": null,
      "role_archetype": "Engineering",
      "slug": "react-native-developer",
      "source": "db"
    },
    {
      "display_name": "iOS Developer",
      "id": 6,
      "rationale": null,
      "role_archetype": null,
      "slug": "ios-engineer",
      "source": "db"
    },
    {
      "display_name": "Pega Developer",
      "id": 24,
      "rationale": null,
      "role_archetype": null,
      "slug": "pega-developer",
      "source": "db"
    },
    {
      "display_name": "Engineering Manager",
      "id": 121,
      "rationale": null,
      "role_archetype": null,
      "slug": "engineering-manager",
      "source": "db"
    }
  ],
  "chosen_role": {
    "display_name": "Data Engineer",
    "id": 2,
    "rationale": "Domain=Data Engineering \u0026 Analytics; The JD is centered on ELT/data integration, data warehouse automation, migration, scheduling, and ETL/ELT tooling, which best matches a Data Engineer role.",
    "role_archetype": null,
    "slug": "data-engineer",
    "source": "db"
  },
  "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": "Snowflake",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": "Data Engineer",
          "id": 2,
          "rationale": null,
          "role_archetype": null,
          "slug": "data-engineer",
          "source": "db"
        }
      ]
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Local Persistence and Offline Behavior",
        "id": 85,
        "rationale": "On-device storage used for caching, offline support, and durable client state. This cluster is coherent because iOS apps often need to preserve user progress and data when connectivity is limited.",
        "slug": "local-persistence-and-offline-behavior",
        "source": "db"
      },
      "input_skill": "Hive",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": "Android Developer",
          "id": 4,
          "rationale": null,
          "role_archetype": null,
          "slug": "android-engineer",
          "source": "db"
        },
        {
          "display_name": "Flutter Developer",
          "id": 74,
          "rationale": null,
          "role_archetype": "Engineering",
          "slug": "flutter-developer",
          "source": "db"
        },
        {
          "display_name": "Hybrid Mobile Developer",
          "id": 11,
          "rationale": null,
          "role_archetype": null,
          "slug": "hybrid-mobile-developer",
          "source": "db"
        },
        {
          "display_name": "Native Mobile Developer",
          "id": 75,
          "rationale": null,
          "role_archetype": "Engineering",
          "slug": "native-mobile-developer",
          "source": "db"
        },
        {
          "display_name": "React Native Developer",
          "id": 73,
          "rationale": null,
          "role_archetype": "Engineering",
          "slug": "react-native-developer",
          "source": "db"
        },
        {
          "display_name": "iOS Developer",
          "id": 6,
          "rationale": null,
          "role_archetype": null,
          "slug": "ios-engineer",
          "source": "db"
        }
      ]
    },
    {
      "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": "Google BigQuery",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": "Data Engineer",
          "id": 2,
          "rationale": null,
          "role_archetype": null,
          "slug": "data-engineer",
          "source": "db"
        }
      ]
    },
    {
      "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"
        }
      ]
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "React Frontend Development",
        "id": 96,
        "rationale": "Building interactive web user interfaces with React.js, including component composition, state management, hooks, and rendering patterns. React.js belongs here because it is a core library for client-side UI development in modern web applications.",
        "slug": "d_init_01",
        "source": "db"
      },
      "input_skill": "RDBMS",
      "llm_role": null,
      "roles_from_db": []
    },
    {
      "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"
        }
      ]
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "React Frontend Development",
        "id": 96,
        "rationale": "Building interactive web user interfaces with React.js, including component composition, state management, hooks, and rendering patterns. React.js belongs here because it is a core library for client-side UI development in modern web applications.",
        "slug": "d_init_01",
        "source": "db"
      },
      "input_skill": "PL/SQL",
      "llm_role": null,
      "roles_from_db": []
    },
    {
      "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"
      },
      "input_skill": "Power BI",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": "Data Engineer",
          "id": 2,
          "rationale": null,
          "role_archetype": null,
          "slug": "data-engineer",
          "source": "db"
        }
      ]
    }
  ],
  "input_final_skills": [
    "WhereScape RED",
    "WhereScape 3D",
    "Oracle",
    "Teradata",
    "Snowflake",
    "Azure SQL",
    "Hive",
    "Amazon",
    "Google BigQuery",
    "Dimensional Modeling",
    "Data Vault",
    "ETL",
    "ELT",
    "Data Warehouse Automation",
    "Data Warehousing",
    "RDBMS",
    "SQL",
    "PL/SQL",
    "SSIS",
    "SSRS",
    "Power BI",
    "Scheduling Tool"
  ],
  "input_llm_skills": [
    "WhereScape RED",
    "WhereScape 3D",
    "Oracle",
    "Teradata",
    "Snowflake",
    "Azure SQL",
    "Hive",
    "Amazon",
    "Google BigQuery",
    "Dimensional Modeling",
    "Data Vault",
    "ETL",
    "ELT",
    "Data Warehouse Automation",
    "Data Warehousing",
    "RDBMS",
    "SQL",
    "PL/SQL",
    "SSIS",
    "SSRS",
    "Power BI",
    "Scheduling Tool"
  ],
  "new_aliases_persisted": 0,
  "run_id": "b0c1edb4-ff3d-478c-aa20-e942622f535d",
  "skills_detail": [
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "WhereScape RED",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Data Engineering Tools",
          "skill_nature": "TOOL",
          "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": "wherescape-red",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "WhereScape 3D",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Data Engineering Tools",
          "skill_nature": "TOOL",
          "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": "wherescape-3d",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "Oracle",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Databases",
          "skill_nature": "TOOL",
          "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": "oracle",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "Teradata",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Databases",
          "skill_nature": "TOOL",
          "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": "teradata",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [
        {
          "alias_text": "Snowflake",
          "alias_type": "CANONICAL",
          "id": 299,
          "is_primary": true,
          "match_strategy": "CASE_INSENSITIVE"
        }
      ],
      "canonical": {
        "category_id": 9,
        "display_name": "Snowflake",
        "id": 105,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "PLATFORM",
        "slug": "snowflake",
        "sub_category_id": 113,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "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": "Snowflake",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "Data Engineer",
              "id": 2,
              "rationale": null,
              "role_archetype": null,
              "slug": "data-engineer",
              "source": "db"
            }
          ]
        }
      ],
      "input_skill": "Snowflake",
      "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": "Azure SQL",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Databases",
          "skill_nature": "PLATFORM",
          "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": "azure-sql",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [
        {
          "alias_text": "Hive",
          "alias_type": "CANONICAL",
          "id": 4198,
          "is_primary": true,
          "match_strategy": "CASE_INSENSITIVE"
        }
      ],
      "canonical": {
        "category_id": 3,
        "display_name": "Hive",
        "id": 2754,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "TOOL",
        "slug": "hive",
        "sub_category_id": 2242,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "dimensions": [
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Local Persistence and Offline Behavior",
            "id": 85,
            "rationale": "On-device storage used for caching, offline support, and durable client state. This cluster is coherent because iOS apps often need to preserve user progress and data when connectivity is limited.",
            "slug": "local-persistence-and-offline-behavior",
            "source": "db"
          },
          "input_skill": "Hive",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "Android Developer",
              "id": 4,
              "rationale": null,
              "role_archetype": null,
              "slug": "android-engineer",
              "source": "db"
            },
            {
              "display_name": "Flutter Developer",
              "id": 74,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "flutter-developer",
              "source": "db"
            },
            {
              "display_name": "Hybrid Mobile Developer",
              "id": 11,
              "rationale": null,
              "role_archetype": null,
              "slug": "hybrid-mobile-developer",
              "source": "db"
            },
            {
              "display_name": "Native Mobile Developer",
              "id": 75,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "native-mobile-developer",
              "source": "db"
            },
            {
              "display_name": "React Native Developer",
              "id": 73,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "react-native-developer",
              "source": "db"
            },
            {
              "display_name": "iOS Developer",
              "id": 6,
              "rationale": null,
              "role_archetype": null,
              "slug": "ios-engineer",
              "source": "db"
            }
          ]
        }
      ],
      "input_skill": "Hive",
      "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": "Amazon",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Cloud Platforms",
          "skill_nature": "PLATFORM",
          "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": "amazon",
        "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": "Google 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": "Google BigQuery",
      "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": "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
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "Data Vault",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Data Warehousing",
          "skill_nature": "CONCEPT",
          "sub_category": "general",
          "typical_lifespan": "MULTI_YEAR",
          "version_strategy": "UNVERSIONED",
          "volatility": "MEDIUM"
        },
        "enrichment": null,
        "keep_log": [],
        "locked_dimensions": [],
        "merge_log": [],
        "placed": null,
        "relationships": null,
        "skill_id": "data-vault",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "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": [],
        "placed": null,
        "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": "ELT",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Data Engineering Tools",
          "skill_nature": "PRACTICE",
          "sub_category": "general",
          "typical_lifespan": "MULTI_YEAR",
          "version_strategy": "UNVERSIONED",
          "volatility": "MEDIUM"
        },
        "enrichment": null,
        "keep_log": [],
        "locked_dimensions": [],
        "merge_log": [],
        "placed": null,
        "relationships": null,
        "skill_id": "elt",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "Data Warehouse Automation",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Data Engineering Tools",
          "skill_nature": "PRACTICE",
          "sub_category": "general",
          "typical_lifespan": "MULTI_YEAR",
          "version_strategy": "UNVERSIONED",
          "volatility": "MEDIUM"
        },
        "enrichment": null,
        "keep_log": [],
        "locked_dimensions": [],
        "merge_log": [],
        "placed": null,
        "relationships": null,
        "skill_id": "data-warehouse-automation",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "Data Warehousing",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Data Warehousing",
          "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": "data-warehousing",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [
        {
          "alias_text": "RDBMS",
          "alias_type": "CANONICAL",
          "id": 2014,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        }
      ],
      "canonical": {
        "category_id": 3,
        "display_name": "RDBMS",
        "id": 1355,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "TOOL",
        "slug": "rdbms",
        "sub_category_id": 1023,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "dimensions": [
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "React Frontend Development",
            "id": 96,
            "rationale": "Building interactive web user interfaces with React.js, including component composition, state management, hooks, and rendering patterns. React.js belongs here because it is a core library for client-side UI development in modern web applications.",
            "slug": "d_init_01",
            "source": "db"
          },
          "input_skill": "RDBMS",
          "llm_role": null,
          "roles_from_db": []
        }
      ],
      "input_skill": "RDBMS",
      "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": "PL/SQL",
          "alias_type": "CANONICAL",
          "id": 2513,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        }
      ],
      "canonical": {
        "category_id": 6,
        "display_name": "PL/SQL",
        "id": 1567,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "LANGUAGE",
        "slug": "pl-sql",
        "sub_category_id": 1173,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "dimensions": [
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "React Frontend Development",
            "id": 96,
            "rationale": "Building interactive web user interfaces with React.js, including component composition, state management, hooks, and rendering patterns. React.js belongs here because it is a core library for client-side UI development in modern web applications.",
            "slug": "d_init_01",
            "source": "db"
          },
          "input_skill": "PL/SQL",
          "llm_role": null,
          "roles_from_db": []
        }
      ],
      "input_skill": "PL/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": "SSIS",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Data Engineering Tools",
          "skill_nature": "TOOL",
          "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": "ssis",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "SSRS",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Data Engineering Tools",
          "skill_nature": "TOOL",
          "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": "ssrs",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [
        {
          "alias_text": "Power BI",
          "alias_type": "CANONICAL",
          "id": 360,
          "is_primary": true,
          "match_strategy": "CASE_INSENSITIVE"
        }
      ],
      "canonical": {
        "category_id": 9,
        "display_name": "Power BI",
        "id": 151,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "PLATFORM",
        "slug": "power-bi",
        "sub_category_id": 111,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "dimensions": [
        {
          "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"
          },
          "input_skill": "Power BI",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "Data Engineer",
              "id": 2,
              "rationale": null,
              "role_archetype": null,
              "slug": "data-engineer",
              "source": "db"
            }
          ]
        }
      ],
      "input_skill": "Power BI",
      "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": "Scheduling Tool",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Data Engineering Tools",
          "skill_nature": "TOOL",
          "sub_category": "general",
          "typical_lifespan": "SHORT_LIVED",
          "version_strategy": "VERSIONED",
          "volatility": "FAST"
        },
        "enrichment": null,
        "keep_log": [],
        "locked_dimensions": [],
        "merge_log": [],
        "placed": null,
        "relationships": null,
        "skill_id": "scheduling-tool",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    }
  ],
  "unmatched_skills": [
    "WhereScape RED",
    "WhereScape 3D",
    "Oracle",
    "Teradata",
    "Azure SQL",
    "Amazon",
    "Data Vault",
    "ETL",
    "ELT",
    "Data Warehouse Automation",
    "Data Warehousing",
    "SSIS",
    "SSRS",
    "Scheduling Tool"
  ]
}
API 3 — final-role-output
{
  "chosen_role": {
    "display_name": "Data Engineer",
    "id": 2,
    "rationale": "Domain=Data Engineering \u0026 Analytics; The JD is centered on ELT/data integration, data warehouse automation, migration, scheduling, and ETL/ELT tooling, which best matches a Data Engineer role.",
    "role_archetype": null,
    "slug": "data-engineer",
    "source": "db"
  },
  "chosen_role_resolution": "in_db",
  "final_input_skills": [
    {
      "skill": "WhereScape RED",
      "tag": "new"
    },
    {
      "skill": "WhereScape 3D",
      "tag": "new"
    },
    {
      "skill": "Oracle",
      "tag": "new"
    },
    {
      "skill": "Teradata",
      "tag": "new"
    },
    {
      "skill": "Snowflake",
      "tag": "in_db"
    },
    {
      "skill": "Azure SQL",
      "tag": "new"
    },
    {
      "skill": "Hive",
      "tag": "in_db"
    },
    {
      "skill": "Amazon",
      "tag": "new"
    },
    {
      "skill": "Google BigQuery",
      "tag": "in_db"
    },
    {
      "skill": "Dimensional Modeling",
      "tag": "in_db"
    },
    {
      "skill": "Data Vault",
      "tag": "new"
    },
    {
      "skill": "ETL",
      "tag": "new"
    },
    {
      "skill": "ELT",
      "tag": "new"
    },
    {
      "skill": "Data Warehouse Automation",
      "tag": "new"
    },
    {
      "skill": "Data Warehousing",
      "tag": "new"
    },
    {
      "skill": "RDBMS",
      "tag": "in_db"
    },
    {
      "skill": "SQL",
      "tag": "in_db"
    },
    {
      "skill": "PL/SQL",
      "tag": "in_db"
    },
    {
      "skill": "SSIS",
      "tag": "new"
    },
    {
      "skill": "SSRS",
      "tag": "new"
    },
    {
      "skill": "Power BI",
      "tag": "in_db"
    },
    {
      "skill": "Scheduling Tool",
      "tag": "new"
    }
  ],
  "llm_cost_api1_usd": null,
  "llm_cost_api2_usd": null,
  "llm_cost_api3_usd": null,
  "llm_cost_total_usd": null,
  "persistence": {
    "items": [
      {
        "chosen_role_id": 2,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Cloud Data Warehouses",
          "id": 22,
          "rationale": "Managed analytical storage and compute platforms used for curated datasets, reporting, and downstream analytics. These systems are central to data modeling, performance tuning, and cost-aware query design.",
          "slug": "cloud-data-warehouses",
          "source": "db"
        },
        "dimension_id": 22,
        "input_skill": "Snowflake",
        "llm_role": null,
        "matched_chosen_role": true,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension saved",
        "role_dimension_saved": true,
        "roles_from_db": [
          {
            "display_name": "Data Engineer",
            "id": 2,
            "rationale": null,
            "role_archetype": null,
            "slug": "data-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 105,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 2,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Local Persistence and Offline Behavior",
          "id": 85,
          "rationale": "On-device storage used for caching, offline support, and durable client state. This cluster is coherent because iOS apps often need to preserve user progress and data when connectivity is limited.",
          "slug": "local-persistence-and-offline-behavior",
          "source": "db"
        },
        "dimension_id": 85,
        "input_skill": "Hive",
        "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": "Android Developer",
            "id": 4,
            "rationale": null,
            "role_archetype": null,
            "slug": "android-engineer",
            "source": "db"
          },
          {
            "display_name": "Flutter Developer",
            "id": 74,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "flutter-developer",
            "source": "db"
          },
          {
            "display_name": "Hybrid Mobile Developer",
            "id": 11,
            "rationale": null,
            "role_archetype": null,
            "slug": "hybrid-mobile-developer",
            "source": "db"
          },
          {
            "display_name": "Native Mobile Developer",
            "id": 75,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "native-mobile-developer",
            "source": "db"
          },
          {
            "display_name": "React Native Developer",
            "id": 73,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "react-native-developer",
            "source": "db"
          },
          {
            "display_name": "iOS Developer",
            "id": 6,
            "rationale": null,
            "role_archetype": null,
            "slug": "ios-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 2754,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 2,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Cloud Data Warehouses",
          "id": 22,
          "rationale": "Managed analytical storage and compute platforms used for curated datasets, reporting, and downstream analytics. These systems are central to data modeling, performance tuning, and cost-aware query design.",
          "slug": "cloud-data-warehouses",
          "source": "db"
        },
        "dimension_id": 22,
        "input_skill": "Google BigQuery",
        "llm_role": null,
        "matched_chosen_role": true,
        "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"
      },
      {
        "chosen_role_id": 2,
        "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"
        },
        "dimension_id": 26,
        "input_skill": "Dimensional Modeling",
        "llm_role": null,
        "matched_chosen_role": true,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension saved",
        "role_dimension_saved": true,
        "roles_from_db": [
          {
            "display_name": "Data Engineer",
            "id": 2,
            "rationale": null,
            "role_archetype": null,
            "slug": "data-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 125,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 2,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "React Frontend Development",
          "id": 96,
          "rationale": "Building interactive web user interfaces with React.js, including component composition, state management, hooks, and rendering patterns. React.js belongs here because it is a core library for client-side UI development in modern web applications.",
          "slug": "d_init_01",
          "source": "db"
        },
        "dimension_id": 96,
        "input_skill": "RDBMS",
        "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": [],
        "skill_dimension_saved": true,
        "skill_id": 1355,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 2,
        "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": 2,
        "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": 2,
        "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": true,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension saved",
        "role_dimension_saved": true,
        "roles_from_db": [
          {
            "display_name": "Data Engineer",
            "id": 2,
            "rationale": null,
            "role_archetype": null,
            "slug": "data-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 101,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 2,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "React Frontend Development",
          "id": 96,
          "rationale": "Building interactive web user interfaces with React.js, including component composition, state management, hooks, and rendering patterns. React.js belongs here because it is a core library for client-side UI development in modern web applications.",
          "slug": "d_init_01",
          "source": "db"
        },
        "dimension_id": 96,
        "input_skill": "PL/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": [],
        "skill_dimension_saved": true,
        "skill_id": 1567,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 2,
        "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": "Power BI",
        "llm_role": null,
        "matched_chosen_role": true,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension saved",
        "role_dimension_saved": true,
        "roles_from_db": [
          {
            "display_name": "Data Engineer",
            "id": 2,
            "rationale": null,
            "role_archetype": null,
            "slug": "data-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 151,
        "skill_tag": "in_db",
        "skipped_reason": null
      }
    ],
    "new_skills_created": 0,
    "role_dimension_saved": 0,
    "skill_dimension_saved": 0,
    "skipped": 1
  },
  "planner_output": null,
  "run_id": "b0c1edb4-ff3d-478c-aa20-e942622f535d"
}

LLM Calls

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

Loading…