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Pipeline run

8d2c9843-87a8-46f3-80ee-3dd8ac831612

Pipeline LLM cost (USD)
API 1: $0.0032 API 2: $0.0002 API 3: $0.0000 Total: $0.0033

Client output enrichment

v2 Skill cluster · Nature of work · AI index · Tech stack maturity · Evidence · KRA description
role baseline loaded sources · ai_index: jd · nature_of_work: jd · tech_stack_maturity: jd
Nature of work · Data pipeline development
Build and operate Azure-based data pipelines in PySpark/SQL, handling batch and real-time ingestion, transformation, and operationalization across structured and unstructured data in Synapse, Databricks, ADF, and Delta Lake.
"Experience in ETL/Pipeline Development using tools such as Azure Data bricks and Azure Data Factory with development expertise on batch and real-time data integration"
Tech stack maturity
Mainstream Modern
The stack centers on widely adopted cloud and data-platform tools like Azure DevOps, Databricks, Delta Lake, and GitHub, which indicates a modern but not bleeding-edge engineering environment.
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 (11)
PySpark SQL Azure Synapse Databricks Azure Data Factory MS SQL Delta Lake Spark SQL SQL Server Azure DevOps GitHub
Skill cluster (2 dimension groups, role-scoped)
Programming Languages for Data Work
SQL
Cross-cutting / unaligned
PySpark Azure Synapse Databricks Azure Data Factory MS SQL Delta Lake Spark SQL SQL Server Azure DevOps GitHub
Show KRA description ↓
• Experience in Pyspark, SQL, Cloud data warehouse like Azure Synapse, data bricks • Experience working with structured and unstructured data • Extensive knowledge of Data Warehousing concepts, strategies, methodologies • Experience in ETL/Pipeline Development using tools such as Azure Data bricks and Azure Data Factory with development expertise on batch and real-time data integration • Experience in data ingestion, preparation, integration, and operationalization techniques in optimally addressing the data requirements • Experience in relational data processing technology like MS SQL, Delta Lake, Spark SQL, SQL Server • Experience with Orchestration tools, Azure DevOps, and GitHub • Must be team oriented with strong communication, collaboration, prioritization, and adaptability skill

Signals

Skill devops-engineer
0.18
Alias data-engineer
1.00
KRA java-backend-developer
0.46

Post-classification

Centroidupdated · n=329
Alias collision log
New-role queue
New skills captured5
New KRA captured

Captured for admin review

PySpark primary Data Engineer pending
Azure Synapse primary Data Engineer pending
Azure Data Factory primary Data Engineer pending
MS SQL primary Data Engineer pending
Spark SQL primary Data Engineer pending
Status: completed Created: 2026-05-27T15:37:11.896994Z Updated: 2026-06-12T16:11:03.077529Z API 3 duration: 8046 ms
Flow Current 3-step pipeline

1 POST /skills/extract-from-jd

2 POST /skills/extract-details

3 POST /skills/final-role-output

Role Chosen role & resolution

Data Engineer

CASE A

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

Exact alias hit on data-engineer (1.0) — no other alias at this confidence; skill_top devops-engineer 0.18 does not contradict

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

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

Job description

Job Title : Data bricks

Location : Chennai -3 Days a week (Hybrid)

Duration : FTE

Job Description : Azure data bricks + Pyspark + SQL- hands on exp. In project

Data Bricks Engineer, you will work with multiple teams to deliver solutions on the Cloud using core cloud data warehouse tools. Must be able to analyze data and develop strategies for populating data lakes if required.

Primary Skill : Data bricks, SQL, Azure, Pyspark,

Good to have : ADF, Synapse

Responsibilities

• Experience in Pyspark, SQL, Cloud data warehouse like Azure Synapse, data bricks
• Experience working with structured and unstructured data
• Extensive knowledge of Data Warehousing concepts, strategies, methodologies
• Experience in ETL/Pipeline Development using tools such as Azure Data bricks and Azure Data Factory with development expertise on batch and real-time data integration
• Experience in data ingestion, preparation, integration, and operationalization techniques in optimally addressing the data requirements
• Experience in relational data processing technology like MS SQL, Delta Lake, Spark SQL, SQL Server
• Experience with Orchestration tools, Azure DevOps, and GitHub
• Must be team oriented with strong communication, collaboration, prioritization, and adaptability skill


(ref:hirist.tech)

Skills from this JD

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

PySpark Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Apache Spark id=1350 · apache-spark

Aliases — catalog

  • Apache Spark (CANONICAL)
  • apache spark 3 (VERSION)
  • spark (VERSION)
  • spark 3 (VERSION)
  • spark 3.x (VERSION)
  • spark3 (VERSION)

Context tags (catalog)

Apache Kafka Cluster Manager DAGScheduler Data Lake DataFrame ETL Hadoop MLlib Machine Learning PySpark RDD Scala Spark SQL Spark Streaming SparkSession

Stored enrichment (catalog DB)

Category
Framework
Sub-category
Distributed Data Processing Framework
Vendor
Apache Software Foundation
License
apache_2
Year introduced
2010
Confidence
0.94
Version strategy
SEPARATE_ENTITY
Version tag
3.x

Maturity reasoning: Apache Spark appears in many data engineering JDs and remains a standard for distributed ETL/ELT; its GitHub and vendor ecosystem activity stay strong, with Databricks and cloud platforms still promoting it.

Skill profile (library / DB)

Skill nature
FRAMEWORK
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
5
Sub-category id
1021
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • ETL and ELT Tooling Catalog dimension db id 24

    Library dimension (catalog)

    Roles linked in library: Data Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
ETL and ELT Tooling
etl-and-elt-tooling
Skipped — no persistable v3 meta for new skill
skill_not_in_db_v3_proposed
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
Azure Synapse Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Azure Synapse Analytics id=108 · azure-synapse-analytics

Aliases — catalog

  • Azure Synapse Analytics (CANONICAL) primary

Context tags (catalog)

Apache Spark Azure Data Lake Storage Data Factory Delta Lake PolyBase SQL pools Spark pools Synapse Studio T-SQL dedicated SQL pool linked services notebooks pipelines serverless SQL pool workspace

Stored enrichment (catalog DB)

Category
Service
Sub-category
Analytics Service
Vendor
Microsoft
License
proprietary
Year introduced
2019
Confidence
0.95
Version strategy
NOT_APPLICABLE

Maturity reasoning: Common in cloud data-platform JDs and Microsoft’s Azure analytics stack; often listed alongside Databricks/ADF for warehousing and ETL, indicating broad hiring demand.

Skill profile (library / DB)

Skill nature
CLOUD_SERVICE
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
11
Sub-category id
117
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
Databricks Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Databricks id=1202 · databricks

Aliases — catalog

  • Databricks (CANONICAL)

Context tags (catalog)

Apache Spark Databricks Runtime Delta Lake MLflow SQL Analytics Spark cloud integration collaborative workspace data engineering data lakes data pipelines data visualization job scheduling machine learning notebooks real-time analytics

Stored enrichment (catalog DB)

Category
Platform
Sub-category
Data Analytics Platform
Vendor
Databricks, Inc.
License
other_open
Year introduced
2013
Confidence
0.97
Version strategy
NOT_APPLICABLE

Maturity reasoning: Databricks appears frequently in data engineering and analytics job postings, especially alongside Spark, Delta Lake, and lakehouse stacks; strong vendor adoption and broad enterprise usage signal mainstream demand.

Skill profile (library / DB)

Skill nature
PLATFORM
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
9
Sub-category id
911
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)
Azure Data Factory 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
MS 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
TOOL
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Delta Lake Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Delta Lake id=237 · delta-lake

Aliases — catalog

  • Delta Lake (CANONICAL) primary

Context tags (catalog)

ACID transactions Apache Spark CDC Databricks Delta Engine ETL Lakehouse architecture MERGE INTO OPTIMIZE Parquet Unity Catalog Z-Order batch processing cloud storage data governance data lake data lakehouse data pipeline data reliability partition pruning schema enforcement schema evolution streaming data streaming ingestion time travel

Stored enrichment (catalog DB)

Category
Tool
Sub-category
Table Format Tool
Vendor
Databricks
License
apache_2
Year introduced
2017
Confidence
0.72
Version strategy
NOT_APPLICABLE

Maturity reasoning: Delta Lake appears frequently in data engineering JDs and cloud vendor docs, especially alongside Databricks/Spark for lakehouse stacks; it’s a common hiring-pipeline skill rather than a niche tool.

Skill profile (library / DB)

Skill nature
TOOL
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
13
Sub-category id
1170
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Model and Data Versioning Catalog dimension db id 48

    Library dimension (catalog)

    Roles linked in library: ML Engineer, MLOps Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Model and Data Versioning
model-and-data-versioning
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Spark 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
Data Engineering Tools
Sub-category
general
Skill nature
TOOL
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
SQL Server Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: SQL Server id=18 · sql-server

Aliases — catalog

  • SQL Server (CANONICAL) primary
  • SQL Server 2000 (VERSION)
  • SQL Server 2005 (VERSION)
  • SQL Server 2008 (VERSION)
  • SQL Server 2012 (VERSION)
  • SQL Server 2014 (VERSION)
  • SQL Server 2016 (VERSION)
  • SQL Server 2017 (VERSION)
  • SQL Server 2019 (VERSION)
  • SQL Server 2022 (VERSION)
  • SQL Server 6.5 (VERSION)
  • SQL Server 7.0 (VERSION)

Context tags (catalog)

Always On CLR Integration Clustered Index ETL Execution Plan Linked Servers Query Store Replication SQL Agent SQL Server Agent SQL Server Integration Services SQL Server Management Studio SQL Server Reporting Services SSIS SSMS SSRS Stored Procedures T-SQL TempDB backup and recovery backup and restore clustering data migration data warehousing database design database normalization indexing performance tuning query optimization replication stored procedures transaction log transaction logs

Stored enrichment (catalog DB)

Category
Datastore
Sub-category
Relational Database
Vendor
Microsoft
License
proprietary
Year introduced
1989
Confidence
0.99
Version strategy
NOT_APPLICABLE

Maturity reasoning: SQL Server appears in many enterprise job descriptions and remains a major Microsoft-supported RDBMS with active Azure SQL/SQL Server demand; it is a common hiring-pipeline staple, not a sunset technology.

Skill profile (library / DB)

Skill nature
TOOL
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
3
Sub-category id
29
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Relational Database Design Catalog dimension db id 4

    Library dimension (catalog)

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

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Relational Database Design
relational-database-design
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Azure DevOps Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Azure DevOps id=1214 · azure-devops

Aliases — catalog

  • Azure DevOps (CANONICAL)

Context tags (catalog)

Agile Azure Pipelines Build Agents Continuous Deployment Continuous Integration Docker GitHub Actions Infrastructure as Code Kubernetes Monitoring Release Management Service Hooks Terraform Version Control Work Items

Stored enrichment (catalog DB)

Category
Platform
Sub-category
Devops Platform
Vendor
Microsoft
License
proprietary
Year introduced
2018
Confidence
0.93
Version strategy
NOT_APPLICABLE

Maturity reasoning: Azure DevOps appears in many enterprise job descriptions for CI/CD, boards, and repos, and Microsoft continues active product support and updates; it remains a common hiring-pipeline skill alongside GitHub Actions/Jenkins.

Skill profile (library / DB)

Skill nature
PLATFORM
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
9
Sub-category id
170
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • CI/CD Pipeline Platforms Catalog dimension db id 150

    Library dimension (catalog)

    Roles linked in library: DevOps Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
CI/CD Pipeline Platforms
ci-cd-pipeline-platforms
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
GitHub Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: GitHub id=280 · github

Aliases — catalog

  • GitHub (CANONICAL) primary

Context tags (catalog)

CI/CD Git GitHub Actions GitHub Enterprise GitHub Pages SSH keys actions branch protection branches code review collaboration commit history fork forking issue tracker issues markdown merge conflicts merge request pull request pull requests release tags repositories repository version control webhooks

Stored enrichment (catalog DB)

Category
Platform
Sub-category
Devops Platform
Vendor
GitHub, Inc.
License
other_open
Year introduced
2008
Confidence
0.96
Version strategy
NOT_APPLICABLE

Maturity reasoning: GitHub appears in a very high volume of engineering JDs for source control, code review, and CI/CD; it’s a standard hiring-pipeline skill across teams.

Skill profile (library / DB)

Skill nature
PLATFORM
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
9
Sub-category id
170
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • CI/CD Pipeline Platforms Catalog dimension db id 150

    Library dimension (catalog)

    Roles linked in library: DevOps Engineer

  • CI/CD for Machine Learning Catalog dimension db id 56

    Library dimension (catalog)

    Roles linked in library: ML Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
CI/CD Pipeline Platforms
ci-cd-pipeline-platforms
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
CI/CD for Machine Learning
ci-cd-for-machine-learning
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)

All API 3 persistence rows

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

Skill Tag Dimension Skill↔dim Role↔dim Outcome Notes
PySpark new
ETL and ELT Tooling
etl-and-elt-tooling
Skipped — no persistable v3 meta for new skill skill_not_in_db_v3_proposed
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
Azure Synapse new
Cloud Data Warehouses
cloud-data-warehouses
Skipped — no persistable v3 meta for new skill skill_not_in_db_v3_proposed
Databricks in_db
React Frontend Development
d_init_01
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Delta Lake in_db
Model and Data Versioning
model-and-data-versioning
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
SQL Server in_db
Relational Database Design
relational-database-design
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Azure DevOps in_db
CI/CD Pipeline Platforms
ci-cd-pipeline-platforms
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
GitHub in_db
CI/CD Pipeline Platforms
ci-cd-pipeline-platforms
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
GitHub in_db
CI/CD for Machine Learning
ci-cd-for-machine-learning
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)

Library artifacts (this run)

Kind Detail DB id
canonical_skill_proposed Azure Data Factory | type=Cloud Platforms subtype=general nature=PLATFORM lifespan=MULTI_YEAR
canonical_skill_proposed MS SQL | type=Databases subtype=general nature=TOOL lifespan=MULTI_YEAR
canonical_skill_proposed Spark SQL | type=Data Engineering Tools subtype=general nature=TOOL lifespan=MULTI_YEAR
dimension_skill_link_proposed PySpark ↔ ETL and ELT Tooling
role_dimension_link_proposed Data Engineer ↔ ETL and ELT Tooling
dimension_skill_link_proposed Azure Synapse ↔ Cloud Data Warehouses
role_dimension_link_proposed Data Engineer ↔ Cloud Data Warehouses
nano JD Parser — gpt-4.1-nano click to toggle
RoleData bricks Engineer
DomainIT Services & Consulting
Location Chennai, India (hybrid)
JD type pass
Show raw JSON
{
  "JD_type": "pass",
  "about_company": null,
  "certifications": [],
  "company_name": null,
  "ctc": null,
  "domain": {
    "primary": {
      "aliases": [
        "ITES",
        "BPO"
      ],
      "domain": "IT Services \u0026 Consulting"
    },
    "secondary": null
  },
  "education": [],
  "experience": {
    "max": null,
    "min": null,
    "raw": null
  },
  "job_locations": [
    {
      "aliases": [
        "Chennai, TN"
      ],
      "city": "Chennai",
      "country": "India",
      "state": null,
      "work_mode": "hybrid"
    }
  ],
  "role": "Data bricks Engineer",
  "role_aliases": [
    "Data Engineer",
    "Data Bricks Developer",
    "Azure Data Engineer"
  ],
  "role_archetype": "Data",
  "roles_and_responsibilities": [
    {
      "bullet_count": 8,
      "heading": "Responsibilities",
      "heading_was_present": true,
      "source_marker": {
        "first_5_words": "\u2022 Experience in Pyspark, SQL,",
        "last_5_words": "communication, collaboration, prioritization, and adaptability skill"
      },
      "text": "\u2022 Experience in Pyspark, SQL, Cloud data warehouse like Azure Synapse, data bricks\n\u2022 Experience working with structured and unstructured data\n\u2022 Extensive knowledge of Data Warehousing concepts, strategies, methodologies\n\u2022 Experience in ETL/Pipeline Development using tools such as Azure Data bricks and Azure Data Factory with development expertise on batch and real-time data integration\n\u2022 Experience in data ingestion, preparation, integration, and operationalization techniques in optimally addressing the data requirements\n\u2022 Experience in relational data processing technology like MS SQL, Delta Lake, Spark SQL, SQL Server\n\u2022 Experience with Orchestration tools, Azure DevOps, and GitHub\n\u2022 Must be team oriented with strong communication, collaboration, prioritization, and adaptability skill",
      "word_count": 104
    }
  ],
  "urls": []
}
API 1 — extract-from-jd click to toggle
{
  "final_skills": [
    {
      "is_primary": true,
      "skill_name": "PySpark"
    },
    {
      "is_primary": true,
      "skill_name": "SQL"
    },
    {
      "is_primary": true,
      "skill_name": "Azure Synapse"
    },
    {
      "is_primary": true,
      "skill_name": "Databricks"
    },
    {
      "is_primary": true,
      "skill_name": "Azure Data Factory"
    },
    {
      "is_primary": true,
      "skill_name": "MS SQL"
    },
    {
      "is_primary": true,
      "skill_name": "Delta Lake"
    },
    {
      "is_primary": true,
      "skill_name": "Spark SQL"
    },
    {
      "is_primary": true,
      "skill_name": "SQL Server"
    },
    {
      "is_primary": true,
      "skill_name": "Azure DevOps"
    },
    {
      "is_primary": true,
      "skill_name": "GitHub"
    }
  ],
  "jd_role": {
    "display_name": "Data bricks Engineer",
    "rationale": null,
    "role_aliases": [
      "Data Engineer",
      "Data Bricks Developer",
      "Azure Data Engineer"
    ],
    "role_archetype": "Data",
    "slug": ""
  },
  "nano_parsed": {
    "JD_type": "pass",
    "about_company": null,
    "certifications": [],
    "company_name": null,
    "ctc": null,
    "domain": {
      "primary": {
        "aliases": [
          "ITES",
          "BPO"
        ],
        "domain": "IT Services \u0026 Consulting"
      },
      "secondary": null
    },
    "education": [],
    "experience": {
      "max": null,
      "min": null,
      "raw": null
    },
    "job_locations": [
      {
        "aliases": [
          "Chennai, TN"
        ],
        "city": "Chennai",
        "country": "India",
        "state": null,
        "work_mode": "hybrid"
      }
    ],
    "role": "Data bricks Engineer",
    "role_aliases": [
      "Data Engineer",
      "Data Bricks Developer",
      "Azure Data Engineer"
    ],
    "role_archetype": "Data",
    "roles_and_responsibilities": [
      {
        "bullet_count": 8,
        "heading": "Responsibilities",
        "heading_was_present": true,
        "source_marker": {
          "first_5_words": "\u2022 Experience in Pyspark, SQL,",
          "last_5_words": "communication, collaboration, prioritization, and adaptability skill"
        },
        "text": "\u2022 Experience in Pyspark, SQL, Cloud data warehouse like Azure Synapse, data bricks\n\u2022 Experience working with structured and unstructured data\n\u2022 Extensive knowledge of Data Warehousing concepts, strategies, methodologies\n\u2022 Experience in ETL/Pipeline Development using tools such as Azure Data bricks and Azure Data Factory with development expertise on batch and real-time data integration\n\u2022 Experience in data ingestion, preparation, integration, and operationalization techniques in optimally addressing the data requirements\n\u2022 Experience in relational data processing technology like MS SQL, Delta Lake, Spark SQL, SQL Server\n\u2022 Experience with Orchestration tools, Azure DevOps, and GitHub\n\u2022 Must be team oriented with strong communication, collaboration, prioritization, and adaptability skill",
        "word_count": 104
      }
    ],
    "urls": []
  },
  "rejected": false,
  "rejection_reason": null,
  "run_id": "8d2c9843-87a8-46f3-80ee-3dd8ac831612",
  "stage3_signals": {
    "alias_found": true,
    "alias_match_roles": [
      {
        "display_name": "Data Engineer",
        "kra_matches": null,
        "matched_count": null,
        "matched_skills": null,
        "role_id": 2,
        "score": 1.0,
        "slug": "data-engineer",
        "total_count": null
      }
    ],
    "kra_match_roles": [
      {
        "display_name": "Java Backend Developer",
        "kra_matches": [
          {
            "kra_text": "persistence and data modeling",
            "sentence": "Experience working with structured and unstructured data",
            "similarity": 0.5133
          },
          {
            "kra_text": "persistence and data modeling",
            "sentence": "Extensive knowledge of Data Warehousing concepts, strategies, methodologies",
            "similarity": 0.4707
          },
          {
            "kra_text": "service contract collaboration",
            "sentence": "Must be team oriented with strong communication, collaboration, prioritization, and adaptability skill",
            "similarity": 0.3831
          }
        ],
        "matched_count": null,
        "matched_skills": null,
        "role_id": 79,
        "score": 0.4557,
        "slug": "java-backend-developer",
        "total_count": null
      },
      {
        "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": "Extensive knowledge of Data Warehousing concepts, strategies, methodologies",
            "similarity": 0.5294
          },
          {
            "kra_text": "Works with data analysts, data scientists, and business stakeholders to define data models, ingestion schedules, and data delivery requirements.",
            "sentence": "Experience working with structured and unstructured data",
            "similarity": 0.5126
          },
          {
            "kra_text": "Works with data analysts, data scientists, and business stakeholders to define data models, ingestion schedules, and data delivery requirements.",
            "sentence": "Must be team oriented with strong communication, collaboration, prioritization, and adaptability skill",
            "similarity": 0.3246
          }
        ],
        "matched_count": null,
        "matched_skills": null,
        "role_id": 2,
        "score": 0.4555,
        "slug": "data-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": "Experience working with structured and unstructured data",
            "similarity": 0.4554
          },
          {
            "kra_text": "Works closely with product managers and UX designers to translate requirements and wireframes into working software features through iterative development.",
            "sentence": "Must be team oriented with strong communication, collaboration, prioritization, and adaptability skill",
            "similarity": 0.3811
          },
          {
            "kra_text": "Designs and queries relational databases like PostgreSQL and document stores like MongoDB, writing migrations, indexes, and optimized queries.",
            "sentence": "Extensive knowledge of Data Warehousing concepts, strategies, methodologies",
            "similarity": 0.3743
          }
        ],
        "matched_count": null,
        "matched_skills": null,
        "role_id": 15,
        "score": 0.4036,
        "slug": "full-stack-engineer",
        "total_count": null
      },
      {
        "display_name": "Flutter Developer",
        "kra_matches": [
          {
            "kra_text": "collaborate with design, product, and backend teams",
            "sentence": "Must be team oriented with strong communication, collaboration, prioritization, and adaptability skill",
            "similarity": 0.4761
          },
          {
            "kra_text": "integrate external APIs and data sources",
            "sentence": "Experience working with structured and unstructured data",
            "similarity": 0.3848
          },
          {
            "kra_text": "integrate external APIs and data sources",
            "sentence": "Extensive knowledge of Data Warehousing concepts, strategies, methodologies",
            "similarity": 0.3239
          }
        ],
        "matched_count": null,
        "matched_skills": null,
        "role_id": 74,
        "score": 0.3949,
        "slug": "flutter-developer",
        "total_count": null
      },
      {
        "display_name": "Scala Backend Developer",
        "kra_matches": [
          {
            "kra_text": "application data modeling",
            "sentence": "Experience working with structured and unstructured data",
            "similarity": 0.4601
          },
          {
            "kra_text": "application data modeling",
            "sentence": "Extensive knowledge of Data Warehousing concepts, strategies, methodologies",
            "similarity": 0.4062
          },
          {
            "kra_text": "backend workflow orchestration",
            "sentence": "Must be team oriented with strong communication, collaboration, prioritization, and adaptability skill",
            "similarity": 0.3015
          }
        ],
        "matched_count": null,
        "matched_skills": null,
        "role_id": 87,
        "score": 0.3893,
        "slug": "scala-backend-developer",
        "total_count": null
      }
    ],
    "skill_match_roles": [
      {
        "display_name": "DevOps Engineer",
        "kra_matches": null,
        "matched_count": 2,
        "matched_skills": [
          "Azure DevOps",
          "GitHub"
        ],
        "role_id": 10,
        "score": 0.1818,
        "slug": "devops-engineer",
        "total_count": 11
      },
      {
        "display_name": "ML Engineer",
        "kra_matches": null,
        "matched_count": 2,
        "matched_skills": [
          "Delta Lake",
          "GitHub"
        ],
        "role_id": 3,
        "score": 0.1818,
        "slug": "ml-engineer",
        "total_count": 11
      },
      {
        "display_name": "Data Engineer",
        "kra_matches": null,
        "matched_count": 1,
        "matched_skills": [
          "SQL"
        ],
        "role_id": 2,
        "score": 0.0909,
        "slug": "data-engineer",
        "total_count": 11
      },
      {
        "display_name": "MLOps Engineer",
        "kra_matches": null,
        "matched_count": 1,
        "matched_skills": [
          "Delta Lake"
        ],
        "role_id": 16,
        "score": 0.0909,
        "slug": "ml-ops-engineer",
        "total_count": 11
      },
      {
        "display_name": "Backend Developer",
        "kra_matches": null,
        "matched_count": 1,
        "matched_skills": [
          "SQL Server"
        ],
        "role_id": 1,
        "score": 0.0909,
        "slug": "backend-engineer",
        "total_count": 11
      }
    ]
  },
  "stage4_decision": {
    "alias_collision_detected": false,
    "case": "A",
    "chosen_role": {
      "display_name": "Data Engineer",
      "kra_matches": null,
      "matched_count": null,
      "matched_skills": null,
      "role_id": 2,
      "score": 1.0,
      "slug": "data-engineer",
      "total_count": null
    },
    "confidence": 1.0,
    "is_new_role": false,
    "llm2_fired": false,
    "llm2_reasoning": null,
    "matched_dimensions": [],
    "matched_kras": [],
    "matched_skills": [],
    "new_role_display_name": null,
    "new_role_slug": null,
    "queued": false,
    "reasoning": "Exact alias hit on data-engineer (1.0) \u2014 no other alias at this confidence; skill_top devops-engineer 0.18 does not contradict",
    "sub_role": null
  },
  "stage5_updates": {
    "centroid_n_after": 329,
    "centroid_updated": true,
    "collision_log_id": null,
    "new_kra_attached": null,
    "new_skills_attached": [
      {
        "is_primary": true,
        "queue_id": 15373,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "PySpark",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 15374,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "Azure Synapse",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 15375,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "Azure Data Factory",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 15376,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "MS SQL",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 15377,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "Spark SQL",
        "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": "TODO: REMOVE AFTER TESTING \u2014 alias DB write disabled",
      "alias_persisted": false,
      "existing_alias_id": 2004,
      "existing_alias_text": "Apache Spark",
      "input_term": "PySpark",
      "matched_canonical": {
        "category_id": 5,
        "display_name": "Apache Spark",
        "id": 1350,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "FRAMEWORK",
        "slug": "apache-spark",
        "sub_category_id": 1021,
        "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": 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": "TODO: REMOVE AFTER TESTING \u2014 alias DB write disabled",
      "alias_persisted": false,
      "existing_alias_id": 302,
      "existing_alias_text": "Azure Synapse Analytics",
      "input_term": "Azure Synapse",
      "matched_canonical": {
        "category_id": 11,
        "display_name": "Azure Synapse Analytics",
        "id": 108,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "CLOUD_SERVICE",
        "slug": "azure-synapse-analytics",
        "sub_category_id": 117,
        "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": 1838,
      "existing_alias_text": "Databricks",
      "input_term": "Databricks",
      "matched_canonical": {
        "category_id": 9,
        "display_name": "Databricks",
        "id": 1202,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "PLATFORM",
        "slug": "databricks",
        "sub_category_id": 911,
        "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": 498,
      "existing_alias_text": "Delta Lake",
      "input_term": "Delta Lake",
      "matched_canonical": {
        "category_id": 13,
        "display_name": "Delta Lake",
        "id": 237,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "TOOL",
        "slug": "delta-lake",
        "sub_category_id": 1170,
        "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": 135,
      "existing_alias_text": "SQL Server",
      "input_term": "SQL Server",
      "matched_canonical": {
        "category_id": 3,
        "display_name": "SQL Server",
        "id": 18,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "TOOL",
        "slug": "sql-server",
        "sub_category_id": 29,
        "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": 1850,
      "existing_alias_text": "Azure DevOps",
      "input_term": "Azure DevOps",
      "matched_canonical": {
        "category_id": 9,
        "display_name": "Azure DevOps",
        "id": 1214,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "PLATFORM",
        "slug": "azure-devops",
        "sub_category_id": 170,
        "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": 541,
      "existing_alias_text": "GitHub",
      "input_term": "GitHub",
      "matched_canonical": {
        "category_id": 9,
        "display_name": "GitHub",
        "id": 280,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "PLATFORM",
        "slug": "github",
        "sub_category_id": 170,
        "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": "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"
    },
    {
      "display_name": "ML Engineer",
      "id": 3,
      "rationale": null,
      "role_archetype": null,
      "slug": "ml-engineer",
      "source": "db"
    },
    {
      "display_name": "MLOps Engineer",
      "id": 16,
      "rationale": null,
      "role_archetype": null,
      "slug": "ml-ops-engineer",
      "source": "db"
    },
    {
      "display_name": ".NET Backend Developer",
      "id": 83,
      "rationale": null,
      "role_archetype": "Engineering",
      "slug": "dotnet-backend-developer",
      "source": "db"
    },
    {
      "display_name": "Backend Developer",
      "id": 1,
      "rationale": null,
      "role_archetype": "A Backend Engineer designs, builds, and maintains the server-side logic and data handling that power applications and services. They focus on implementing reliable business functionality, integrating with other systems, and ensuring the backend is scalable, maintainable, and observable.",
      "slug": "backend-engineer",
      "source": "db"
    },
    {
      "display_name": "Kotlin Backend Developer",
      "id": 84,
      "rationale": null,
      "role_archetype": "Engineering",
      "slug": "kotlin-server-backend-developer",
      "source": "db"
    },
    {
      "display_name": "Node.js Backend Developer",
      "id": 82,
      "rationale": null,
      "role_archetype": "Engineering",
      "slug": "node-backend-developer",
      "source": "db"
    },
    {
      "display_name": "Python Backend Developer",
      "id": 80,
      "rationale": null,
      "role_archetype": "Engineering",
      "slug": "python-backend-developer",
      "source": "db"
    },
    {
      "display_name": "Ruby Backend Developer",
      "id": 85,
      "rationale": null,
      "role_archetype": "Engineering",
      "slug": "ruby-backend-developer",
      "source": "db"
    },
    {
      "display_name": "Scala Backend Developer",
      "id": 87,
      "rationale": null,
      "role_archetype": "Engineering",
      "slug": "scala-backend-developer",
      "source": "db"
    },
    {
      "display_name": "DevOps Engineer",
      "id": 10,
      "rationale": null,
      "role_archetype": null,
      "slug": "devops-engineer",
      "source": "db"
    }
  ],
  "chosen_role": {
    "display_name": "Data Engineer",
    "id": 2,
    "rationale": "Exact alias hit on data-engineer (1.0) \u2014 no other alias at this confidence; skill_top devops-engineer 0.18 does not contradict",
    "role_archetype": null,
    "slug": "data-engineer",
    "source": "db"
  },
  "dimensions": [
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "ETL and ELT Tooling",
        "id": 24,
        "rationale": "Packaged tools for extracting, loading, and transforming data across systems. This dimension covers connector-based ingestion, transformation frameworks, and managed integration products.",
        "slug": "etl-and-elt-tooling",
        "source": "db"
      },
      "input_skill": "PySpark",
      "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": "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": "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": "Azure Synapse",
      "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": "Databricks",
      "llm_role": null,
      "roles_from_db": []
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Model and Data Versioning",
        "id": 48,
        "rationale": "Versioning systems for datasets, features, and model artifacts at the storage layer. This enables reproducible training, rollback, lineage of artifacts, and controlled promotion of model assets.",
        "slug": "model-and-data-versioning",
        "source": "db"
      },
      "input_skill": "Delta Lake",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": "ML Engineer",
          "id": 3,
          "rationale": null,
          "role_archetype": null,
          "slug": "ml-engineer",
          "source": "db"
        },
        {
          "display_name": "MLOps Engineer",
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          "rationale": null,
          "role_archetype": null,
          "slug": "ml-ops-engineer",
          "source": "db"
        }
      ]
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Relational Database Design",
        "id": 4,
        "rationale": "Modeling and operating relational persistence for backend services. Includes schema design, normalization, indexing, transactions, and query tuning for operational data stores.",
        "slug": "relational-database-design",
        "source": "db"
      },
      "input_skill": "SQL Server",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": ".NET Backend Developer",
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          "rationale": null,
          "role_archetype": "Engineering",
          "slug": "dotnet-backend-developer",
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        },
        {
          "display_name": "Backend Developer",
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          "role_archetype": "A Backend Engineer designs, builds, and maintains the server-side logic and data handling that power applications and services. They focus on implementing reliable business functionality, integrating with other systems, and ensuring the backend is scalable, maintainable, and observable.",
          "slug": "backend-engineer",
          "source": "db"
        },
        {
          "display_name": "Kotlin Backend Developer",
          "id": 84,
          "rationale": null,
          "role_archetype": "Engineering",
          "slug": "kotlin-server-backend-developer",
          "source": "db"
        },
        {
          "display_name": "Node.js Backend Developer",
          "id": 82,
          "rationale": null,
          "role_archetype": "Engineering",
          "slug": "node-backend-developer",
          "source": "db"
        },
        {
          "display_name": "Python Backend Developer",
          "id": 80,
          "rationale": null,
          "role_archetype": "Engineering",
          "slug": "python-backend-developer",
          "source": "db"
        },
        {
          "display_name": "Ruby Backend Developer",
          "id": 85,
          "rationale": null,
          "role_archetype": "Engineering",
          "slug": "ruby-backend-developer",
          "source": "db"
        },
        {
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            "difficulty_hint": "well_known",
            "display_name": "CI/CD Pipeline Platforms",
            "id": 150,
            "rationale": "Systems used to define, run, and maintain automated build and deployment workflows. This cluster is coherent because the role owns delivery automation end to end, including pipeline reliability and promotion logic.",
            "slug": "ci-cd-pipeline-platforms",
            "source": "db"
          },
          "input_skill": "GitHub",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "DevOps Engineer",
              "id": 10,
              "rationale": null,
              "role_archetype": null,
              "slug": "devops-engineer",
              "source": "db"
            }
          ]
        },
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "CI/CD for Machine Learning",
            "id": 56,
            "rationale": "Tools and platforms for automating ML model integration, testing, and deployment pipelines.",
            "slug": "ci-cd-for-machine-learning",
            "source": "db"
          },
          "input_skill": "GitHub",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "ML Engineer",
              "id": 3,
              "rationale": null,
              "role_archetype": null,
              "slug": "ml-engineer",
              "source": "db"
            }
          ]
        }
      ],
      "input_skill": "GitHub",
      "matched_via": "alias",
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": null,
      "source_tag": "db",
      "was_in_llm_skills": true
    }
  ],
  "unmatched_skills": [
    "Azure Data Factory",
    "MS SQL",
    "Spark SQL"
  ]
}
API 3 — final-role-output
{
  "chosen_role": {
    "display_name": "Data Engineer",
    "id": 2,
    "rationale": "Exact alias hit on data-engineer (1.0) \u2014 no other alias at this confidence; skill_top devops-engineer 0.18 does not contradict",
    "role_archetype": null,
    "slug": "data-engineer",
    "source": "db"
  },
  "chosen_role_resolution": "in_db",
  "final_input_skills": [
    {
      "skill": "PySpark",
      "tag": "in_db"
    },
    {
      "skill": "SQL",
      "tag": "in_db"
    },
    {
      "skill": "Azure Synapse",
      "tag": "in_db"
    },
    {
      "skill": "Databricks",
      "tag": "in_db"
    },
    {
      "skill": "Azure Data Factory",
      "tag": "new"
    },
    {
      "skill": "MS SQL",
      "tag": "new"
    },
    {
      "skill": "Delta Lake",
      "tag": "in_db"
    },
    {
      "skill": "Spark SQL",
      "tag": "new"
    },
    {
      "skill": "SQL Server",
      "tag": "in_db"
    },
    {
      "skill": "Azure DevOps",
      "tag": "in_db"
    },
    {
      "skill": "GitHub",
      "tag": "in_db"
    }
  ],
  "llm_cost_api1_usd": null,
  "llm_cost_api2_usd": null,
  "llm_cost_api3_usd": null,
  "llm_cost_total_usd": null,
  "persistence": {
    "items": [
      {
        "chosen_role_id": 2,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "ETL and ELT Tooling",
          "id": 24,
          "rationale": "Packaged tools for extracting, loading, and transforming data across systems. This dimension covers connector-based ingestion, transformation frameworks, and managed integration products.",
          "slug": "etl-and-elt-tooling",
          "source": "db"
        },
        "dimension_id": 24,
        "input_skill": "PySpark",
        "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": "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": "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": "Azure Synapse",
        "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": "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": "Databricks",
        "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": 1202,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 2,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Model and Data Versioning",
          "id": 48,
          "rationale": "Versioning systems for datasets, features, and model artifacts at the storage layer. This enables reproducible training, rollback, lineage of artifacts, and controlled promotion of model assets.",
          "slug": "model-and-data-versioning",
          "source": "db"
        },
        "dimension_id": 48,
        "input_skill": "Delta Lake",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "ML Engineer",
            "id": 3,
            "rationale": null,
            "role_archetype": null,
            "slug": "ml-engineer",
            "source": "db"
          },
          {
            "display_name": "MLOps Engineer",
            "id": 16,
            "rationale": null,
            "role_archetype": null,
            "slug": "ml-ops-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 237,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 2,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Relational Database Design",
          "id": 4,
          "rationale": "Modeling and operating relational persistence for backend services. Includes schema design, normalization, indexing, transactions, and query tuning for operational data stores.",
          "slug": "relational-database-design",
          "source": "db"
        },
        "dimension_id": 4,
        "input_skill": "SQL Server",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": ".NET Backend Developer",
            "id": 83,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "dotnet-backend-developer",
            "source": "db"
          },
          {
            "display_name": "Backend Developer",
            "id": 1,
            "rationale": null,
            "role_archetype": "A Backend Engineer designs, builds, and maintains the server-side logic and data handling that power applications and services. They focus on implementing reliable business functionality, integrating with other systems, and ensuring the backend is scalable, maintainable, and observable.",
            "slug": "backend-engineer",
            "source": "db"
          },
          {
            "display_name": "Kotlin Backend Developer",
            "id": 84,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "kotlin-server-backend-developer",
            "source": "db"
          },
          {
            "display_name": "Node.js Backend Developer",
            "id": 82,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "node-backend-developer",
            "source": "db"
          },
          {
            "display_name": "Python Backend Developer",
            "id": 80,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "python-backend-developer",
            "source": "db"
          },
          {
            "display_name": "Ruby Backend Developer",
            "id": 85,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "ruby-backend-developer",
            "source": "db"
          },
          {
            "display_name": "Scala Backend Developer",
            "id": 87,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "scala-backend-developer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 18,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 2,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "CI/CD Pipeline Platforms",
          "id": 150,
          "rationale": "Systems used to define, run, and maintain automated build and deployment workflows. This cluster is coherent because the role owns delivery automation end to end, including pipeline reliability and promotion logic.",
          "slug": "ci-cd-pipeline-platforms",
          "source": "db"
        },
        "dimension_id": 150,
        "input_skill": "Azure DevOps",
        "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": "DevOps Engineer",
            "id": 10,
            "rationale": null,
            "role_archetype": null,
            "slug": "devops-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 1214,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 2,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "CI/CD Pipeline Platforms",
          "id": 150,
          "rationale": "Systems used to define, run, and maintain automated build and deployment workflows. This cluster is coherent because the role owns delivery automation end to end, including pipeline reliability and promotion logic.",
          "slug": "ci-cd-pipeline-platforms",
          "source": "db"
        },
        "dimension_id": 150,
        "input_skill": "GitHub",
        "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": "DevOps Engineer",
            "id": 10,
            "rationale": null,
            "role_archetype": null,
            "slug": "devops-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 280,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 2,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "CI/CD for Machine Learning",
          "id": 56,
          "rationale": "Tools and platforms for automating ML model integration, testing, and deployment pipelines.",
          "slug": "ci-cd-for-machine-learning",
          "source": "db"
        },
        "dimension_id": 56,
        "input_skill": "GitHub",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "ML Engineer",
            "id": 3,
            "rationale": null,
            "role_archetype": null,
            "slug": "ml-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 280,
        "skill_tag": "in_db",
        "skipped_reason": null
      }
    ],
    "new_skills_created": 0,
    "role_dimension_saved": 0,
    "skill_dimension_saved": 0,
    "skipped": 2
  },
  "planner_output": null,
  "run_id": "8d2c9843-87a8-46f3-80ee-3dd8ac831612"
}

LLM Calls

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

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