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Pipeline run

85bc0bd5-dada-4e01-880b-275643703adf

Pipeline LLM cost (USD)
API 1: $0.0098 API 2: $0.0007 API 3: $0.0000 Total: $0.0105

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 maintain Azure/Databricks ETL/ELT pipelines that ingest data from source systems into the data warehouse/data marts, transform it for analytics, and monitor/fix data quality, outages, and performance issues.
""Lead data acquisition efforts to gather data from various structured or semi-structured source systems of record""
Tech stack maturity
Mainstream Modern
The stack centers on widely adopted cloud and data-engineering tools like Azure, Databricks, Spark, Python, Scala, SQL, and CI/CD, which fits a mainstream modern enterprise data platform rather than bleeding-edge or legacy-only environments.
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 (29)
Microsoft Azure Databricks Apache Spark Scala CI/CD ETL ELT Informatica DataStage SSIS PL/SQL T-SQL SQL Python Data Warehouse DataMart Business Intelligence Analytics Data Integration Data Ingestion Data Transformation Data Storage Data Aggregation Data Quality VB.NET +4
Skill cluster (4 dimension groups, role-scoped)
Programming Languages for Data Work
Scala SQL Python
ETL and ELT Tooling
Apache Spark Informatica
Cloud Platforms
Microsoft Azure
Cross-cutting / unaligned
Databricks CI/CD ETL ELT DataStage SSIS PL/SQL T-SQL Data Warehouse DataMart Business Intelligence Analytics Data Integration Data Ingestion Data Transformation Data Storage Data Aggregation Data Quality VB.NET Data Audits Data Pipeline Monitoring Code Versioning Code Deployment
Show KRA description ↓
· Support the full data engineering lifecycle including research, proof of concepts, design, development, testing, deployment, and maintenance of data management solutions · Utilize knowledge of various data management technologies to drive data engineering projects · Lead data acquisition efforts to gather data from various structured or semi-structured source systems of record to hydrate client data warehouse and power analytics across numerous health care domains · Leverage combination of ETL/ELT methodologies to pull complex relational and dimensional data to support loading DataMart’s and reporting aggregates. · Eliminate unwarranted complexity and unneeded interdependencies · Detect data quality issues, identify root causes, implement fixes, and manage data audits to mitigate data challenges · Implement, modify, and maintain data integration efforts that improve data efficiency, reliability, and value · Leverage and facilitate the evolution of best practices for data acquisition, transformation, storage, and aggregation that solve current challenges and reduce the risk of future challenges · Effectively create data transformations that address business requirements and other constraints · Partner with the broader analytics organization to make recommendations for changes to data systems and the architecture of data platforms · Support the implementation of a modern data framework that facilitates business intelligence reporting and advanced analytics · Prepare high level design documents and detailed technical design documents with best practices to enable efficient data ingestion, transformation and data movement. · Leverage DevOps tools to enable code versioning and code deployment. · Leverage data pipeline monitoring tools to detect data integrity issues before they result into user visible outages or data quality issues · Leverage processes and diagnostics tools to troubleshoot, maintain and optimize solutions and respond to customer and production issues • Microsoft Azure, Data bricks, Spark/Scala, CI/CD implementations • Bachelor’s Degree (preferably in information technology, engineering, math, computer science, analytics, engineering or other related field) • Minimum of 5+ years of combined experience in data engineering, ingestion, normalization, transformation, aggregation, structuring, and storage · Minimum of 5+ years of combined experience working with industry standard relational, dimensional or non-relational data storage systems · Minimum of 5+ years of experience in designing ETL/ELT solutions using tools like Informatica, DataStage, SSIS , PL/SQL, T-SQL, etc. · Minimum of 5+ years of experience in managing data assets using SQL, Python, Scala, VB.NET or other similar querying/coding language

Signals

Skill data-engineer
0.25
Alias data-engineer
1.00
KRA data-engineer
0.62

Post-classification

Centroidupdated · n=167
Alias collision log
New-role queue
New skills captured19
New KRA captured

Captured for admin review

ETL primary Data Engineer pending
ELT primary Data Engineer pending
DataStage primary Data Engineer pending
SSIS primary Data Engineer pending
T-SQL primary Data Engineer pending
VB.NET Data Engineer pending
Data Warehouse primary Data Engineer pending
DataMart primary Data Engineer pending
Business Intelligence primary Data Engineer pending
Data Integration primary Data Engineer pending
Data Ingestion primary Data Engineer pending
Data Transformation primary Data Engineer pending
Data Storage primary Data Engineer pending
Data Aggregation primary Data Engineer pending
Data Quality primary Data Engineer pending
Data Audits Data Engineer pending
Data Pipeline Monitoring Data Engineer pending
Code Versioning Data Engineer pending
Code Deployment Data Engineer pending
Status: completed Created: 2026-05-27T14:24:14.108881Z Updated: 2026-05-27T14:27:14.587737Z API 3 duration: 48468 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 centers on end-to-end data engineering, data ingestion/transformation, pipeline reliability, cloud data platform work, and Databricks/Spark implementation, which best matches Data Engineer.

Matched skills

ETL/ELTMicrosoft AzureDatabricksSpark/ScalaCI/CDdata pipeline monitoringDevOps toolsdata integrationdata warehouseDataMartrelational datadimensional data

Matched dimensions

End-to-end data engineering lifecycleData acquisition and ingestionData transformation and aggregationData quality and issue remediationData platform architecturePipeline monitoring and operational supportCloud data engineering

Matched KRAs

Support the full data engineering lifecycleLead data acquisition effortsHydrate client data warehouse and power analyticsSupport loading DataMart’s and reporting aggregatesDetect data quality issues and identify root causesImplement, modify, and maintain data integration effortsPrepare high level and detailed technical design documentsLeverage DevOps tools to enable code versioning and deploymentDetect data integrity issues before outagesTroubleshoot, maintain and optimize solutions

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

Programmers.io is looking candidates for Database Analyst – Engineering Data Integration


Main role – Data Integration


Must have experience in Data warehouse, ETL, SSIS SSRS, Azure data factory, Git Hub and Jenkins, Azure data Bricks, spark with Scala (no Pyspark)


Good knowledge in Data warehousing, ETL Tools, SQL, Scheduling, and Data model)


Plus Informatica & Healthcare


Experience: 5+


Job Description: 
· Support the full data engineering lifecycle including research, proof of concepts, design, development, testing, deployment, and maintenance of data management solutions
· Utilize knowledge of various data management technologies to drive data engineering projects
· Lead data acquisition efforts to gather data from various structured or semi-structured source systems of record to hydrate client data warehouse and power analytics across numerous health care domains
· Leverage combination of ETL/ELT methodologies to pull complex relational and dimensional data to support loading DataMart’s and reporting aggregates.
· Eliminate unwarranted complexity and unneeded interdependencies
· Detect data quality issues, identify root causes, implement fixes, and manage data audits to mitigate data challenges
· Implement, modify, and maintain data integration efforts that improve data efficiency, reliability, and value
· Leverage and facilitate the evolution of best practices for data acquisition, transformation, storage, and aggregation that solve current challenges and reduce the risk of future challenges
· Effectively create data transformations that address business requirements and other constraints
· Partner with the broader analytics organization to make recommendations for changes to data systems and the architecture of data platforms
· Support the implementation of a modern data framework that facilitates business intelligence reporting and advanced analytics
· Prepare high level design documents and detailed technical design documents with best practices to enable efficient data ingestion, transformation and data movement.
· Leverage DevOps tools to enable code versioning and code deployment.
· Leverage data pipeline monitoring tools to detect data integrity issues before they result into user visible outages or data quality issues
· Leverage processes and diagnostics tools to troubleshoot, maintain and optimize solutions and respond to customer and production issues


Requirements:
• Microsoft Azure, Data bricks, Spark/Scala, CI/CD implementations
• Bachelor’s Degree (preferably in information technology, engineering, math, computer science, analytics, engineering or other related field) 
• Minimum of 5+ years of combined experience in data engineering, ingestion, normalization, transformation, aggregation, structuring, and storage
· Minimum of 5+ years of combined experience working with industry standard relational, dimensional or non-relational data storage systems
· Minimum of 5+ years of experience in designing ETL/ELT solutions using tools like Informatica, DataStage, SSIS , PL/SQL, T-SQL, etc. 
· Minimum of 5+ years of experience in managing data assets using SQL, Python, Scala, VB.NET or other similar querying/coding language



Company Profile : 


Please find below the company website for your review.


https://programmers.io/lifeatpio/

Skills from this JD

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

Microsoft Azure Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Microsoft Azure id=97 · microsoft-azure

Aliases — catalog

  • Microsoft Azure (CANONICAL) primary

Context tags (catalog)

AKS ARM templates App Service Azure Active Directory Azure App Service Azure Blob Storage Azure Cosmos DB Azure DevOps Azure Functions Azure Kubernetes Service Azure Logic Apps Azure Monitor Azure Resource Manager Azure SQL Azure Virtual Machines Bicep Cloud Services Entra ID Functions IaaS Infrastructure as Code Key Vault Logic Apps PaaS Resource Group Serverless Computing Service Bus Storage Account Virtual Machines cloud migration microservices serverless

Stored enrichment (catalog DB)

Category
Platform
Sub-category
Cloud Platform
Vendor
Microsoft
License
other_open
Year introduced
2010
Confidence
0.99
Version strategy
NOT_APPLICABLE

Maturity reasoning: Azure appears in large volumes of cloud/DevOps job descriptions and is a core hyperscaler alongside AWS/GCP; Microsoft’s continued product investment and broad enterprise adoption signal mainstream demand.

Skill profile (library / DB)

Skill nature
PLATFORM
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
9
Sub-category id
46
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Cloud & Hosting Providers Catalog dimension db id 414

    Library dimension (catalog)

    Roles linked in library: PHP Backend Developer

  • Cloud Platforms Catalog dimension db id 20

    Library dimension (catalog)

    Roles linked in library: .NET Backend Developer, Backend Developer, Cyber Security Engineer, Data Engineer, DevOps Engineer, Fullstack Developer, Go Backend Developer, Java Backend Developer, Kotlin Backend Developer, ML Engineer, MLOps Engineer, Node.js Backend Developer, Python Backend Developer, Scala Backend Developer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Cloud & Hosting Providers
cloud-hosting-providers
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Cloud Platforms
cloud-platforms
Existing dimension (library) · Role↔dimension saved
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)
Apache Spark 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
Existing dimension (library) · Role↔dimension saved
Scala Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Scala id=102 · scala

Aliases — catalog

  • Scala (CANONICAL) primary

Context tags (catalog)

Akka Apache Kafka Cats Flink JVM Monads Play Framework SBT ScalaTest Shapeless Spark Spark SQL ZIO case class for-comprehension functional programming implicit pattern matching typeclass

Stored enrichment (catalog DB)

Category
Language
Sub-category
Programming Language
Vendor
EPFL
License
apache_2
Year introduced
2004
Confidence
0.99
Version strategy
NOT_APPLICABLE

Maturity reasoning: Scala still appears in many backend/data engineering JDs, especially with Spark and Akka, and remains supported by major JVM ecosystems; it’s not a sunset technology.

Skill profile (library / DB)

Skill nature
LANGUAGE
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
6
Sub-category id
96
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Programming Languages for Data Work Catalog dimension db id 21

    Library dimension (catalog)

    Roles linked in library: Data Engineer

  • Programming Languages for ML Systems Catalog dimension db id 39

    Library dimension (catalog)

    Roles linked in library: ML Engineer, MLOps Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Programming Languages for Data Work
programming-languages-for-data-work
Existing dimension (library) · Role↔dimension saved
Programming Languages for ML Systems
programming-languages-for-ml-systems
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
CI/CD Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: CI/CD id=1190 · ci-cd

Aliases — catalog

  • CI/CD (CANONICAL)

Context tags (catalog)

Ansible CircleCI Docker GitLab CI Jenkins Kubernetes Terraform Travis CI automated testing build automation continuous deployment continuous integration deployment pipelines monitoring version control

Stored enrichment (catalog DB)

Category
Methodology
Sub-category
Ci Cd Process
Confidence
0.93
Version strategy
NOT_APPLICABLE

Maturity reasoning: CI/CD appears in a large share of software engineering JDs and is a standard requirement across DevOps, platform, and backend roles; major vendors like GitHub, GitLab, and AWS all center product roadmaps on CI/CD pipelines.

Skill profile (library / DB)

Skill nature
METHODOLOGY
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
8
Sub-category id
900
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)
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
Informatica Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Informatica id=117 · informatica

Aliases — catalog

  • Informatica (CANONICAL) primary

Context tags (catalog)

CDC Cloud Data Integration ELT ETL IICS PowerCenter data integration data quality data warehousing lookup transformation mapping repository session source qualifier workflow

Stored enrichment (catalog DB)

Category
Platform
Sub-category
Data Integration Platform
Vendor
Informatica
License
proprietary
Year introduced
1993
Confidence
0.90
Version strategy
NOT_APPLICABLE

Maturity reasoning: Informatica appears frequently in enterprise data-integration and ETL job postings, especially alongside cloud migration and MDM roles; it remains a common hiring keyword rather than a sunset technology.

Skill profile (library / DB)

Skill nature
PLATFORM
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
9
Sub-category id
114
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
Existing dimension (library) · Role↔dimension saved
DataStage 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
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
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)
T-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
Programming Languages
Sub-category
general
Skill nature
LANGUAGE
Volatility
STABLE
Typical lifespan
EVERGREEN
Version strategy
UNVERSIONED
SQL Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: SQL id=101 · sql

Aliases — catalog

  • SQL (CANONICAL) primary

Context tags (catalog)

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

Stored enrichment (catalog DB)

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

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

Skill profile (library / DB)

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

Dimensions (API 2 worklist)

  • Pega Programming Languages & DSLs Catalog dimension db id 267

    Library dimension (catalog)

    Roles linked in library: Pega Developer

  • Programming Languages for Data Work Catalog dimension db id 21

    Library dimension (catalog)

    Roles linked in library: Data Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Pega Programming Languages & DSLs
pega-programming-languages-dsls
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Programming Languages for Data Work
programming-languages-for-data-work
Existing dimension (library) · Role↔dimension saved
Python Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Python id=5 · python

Aliases — catalog

  • Python (CANONICAL) primary
  • Python 2 (VERSION)
  • Python 2.x (VERSION)
  • Python 3 (VERSION)
  • Python 3.10 (VERSION)
  • Python 3.11 (VERSION)
  • Python 3.12 (VERSION)
  • Python 3.x (VERSION)
  • py (VERSION)
  • py2 (VERSION)
  • py3 (VERSION)
  • python 3 (VERSION)
  • python 3.x (VERSION)
  • python2 (VERSION)
  • python3 (VERSION)
  • python3.x (VERSION)

Context tags (catalog)

API Django FastAPI Flask Jupyter NumPy PEP 8 Pandas REST SQLAlchemy asyncio pandas pip pytest type hints venv virtualenv

Stored enrichment (catalog DB)

Category
Language
Sub-category
Programming Language
Vendor
PSF
License
mit
Year introduced
1991
Confidence
0.99
Version strategy
SEPARATE_ENTITY
Version tag
3

Maturity reasoning: Python appears in a very high volume of job descriptions across data, backend, automation, and ML roles, and remains a default hiring-pipeline language on major job boards and tech stacks.

Skill profile (library / DB)

Skill nature
LANGUAGE
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
6
Sub-category id
96
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Cloud Security Scripting & DSL Languages Catalog dimension db id 248

    Library dimension (catalog)

    Roles linked in library: Cloud Security Engineer

  • Programming Languages Catalog dimension db id 1

    Library dimension (catalog)

    Roles linked in library: Backend Developer, Fullstack Developer, Fullstack Developer

  • Programming Languages and Scripting Catalog dimension db id 59

    Library dimension (catalog)

    Roles linked in library: Cyber Security Engineer

  • Programming Languages for Data Work Catalog dimension db id 21

    Library dimension (catalog)

    Roles linked in library: Data Engineer

  • Programming Languages for ML Systems Catalog dimension db id 39

    Library dimension (catalog)

    Roles linked in library: ML Engineer, MLOps Engineer

  • Programming Languages for XR Catalog dimension db id 97

    Library dimension (catalog)

    Roles linked in library: AR/VR Engineer

  • Python Programming Catalog dimension db id 290

    Library dimension (catalog)

    Roles linked in library: Python Backend Developer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Cloud Security Scripting & DSL Languages
cloud-security-scripting-dsl-languages
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Programming Languages
programming-languages
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Programming Languages and Scripting
programming-languages-and-scripting
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
Programming Languages for ML Systems
programming-languages-for-ml-systems
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Programming Languages for XR
programming-languages-for-xr
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Python Programming
python-programming
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
VB.NET 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
Programming Languages
Sub-category
general
Skill nature
LANGUAGE
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Data Warehouse Primary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

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

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

Skill enrichment (orchestrator / LLM)

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

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

Skill enrichment (orchestrator / LLM)

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

Derived legacy fields
Category
Data Engineering Tools
Sub-category
general
Skill nature
CONCEPT
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Analytics Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Analytics id=1664 · analytics

Aliases — catalog

  • Analytics (CANONICAL)

Context tags (catalog)

A/B testing ETL KPI Python R SQL business intelligence dashboards data mining data storytelling data visualization data warehousing machine learning predictive modeling statistical analysis

Stored enrichment (catalog DB)

Category
Domain
Sub-category
Analytics
Confidence
0.94
Version strategy
NOT_APPLICABLE

Maturity reasoning: Analytics appears in a large share of data, product, and BI job descriptions, and major vendors (Google Analytics, Adobe Analytics, Power BI) continue to invest heavily in the category.

Skill profile (library / DB)

Skill nature
CONCEPT
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
37
Sub-category id
1257
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)
Data Integration Primary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

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

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

Skill enrichment (orchestrator / LLM)

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

Derived legacy fields
Category
Data Engineering Tools
Sub-category
general
Skill nature
CONCEPT
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Data Transformation Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Data Transform id=1890 · data-transform

Aliases — catalog

  • Data Transform (CANONICAL) primary

Context tags (catalog)

ETL batch processing data aggregation data cleansing data integration data lineage data mapping data modeling data pipeline data quality data visualization data wrangling real-time processing schema evolution transformation logic

Stored enrichment (catalog DB)

Category
Concept
Sub-category
Transformation Concept
Confidence
0.90
Version strategy
NOT_APPLICABLE

Maturity reasoning: Data transformation is a core ETL/ELT concept and appears broadly across job descriptions for analytics, data engineering, and BI roles; it’s a standard pipeline requirement rather than a niche tool.

Skill profile (library / DB)

Skill nature
CONCEPT
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
2
Sub-category id
1445
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Data Pages and Data Modeling Catalog dimension db id 254

    Library dimension (catalog)

    Roles linked in library: Pega Developer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Data Pages and Data Modeling
data-pages-and-data-modeling
Skipped — no persistable v3 meta for new skill
skill_not_in_db_v3_proposed
Data Storage Primary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

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

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

Skill enrichment (orchestrator / LLM)

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

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

Skill enrichment (orchestrator / LLM)

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

Derived legacy fields
Category
Data Engineering Tools
Sub-category
general
Skill nature
CONCEPT
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Data Audits 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
PRACTICE
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Data Pipeline Monitoring 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
PRACTICE
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Code Versioning 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
Practices
Sub-category
general
Skill nature
PRACTICE
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Code Deployment 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
Practices
Sub-category
general
Skill nature
PRACTICE
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED

All API 3 persistence rows

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

Skill Tag Dimension Skill↔dim Role↔dim Outcome Notes
Microsoft Azure in_db
Cloud & Hosting Providers
cloud-hosting-providers
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Microsoft Azure in_db
Cloud Platforms
cloud-platforms
Existing dimension (library) · Role↔dimension saved
Databricks in_db
React Frontend Development
d_init_01
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Apache Spark in_db
ETL and ELT Tooling
etl-and-elt-tooling
Existing dimension (library) · Role↔dimension saved
Scala in_db
Programming Languages for Data Work
programming-languages-for-data-work
Existing dimension (library) · Role↔dimension saved
Scala in_db
Programming Languages for ML Systems
programming-languages-for-ml-systems
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
CI/CD in_db
CI/CD Pipeline Platforms
ci-cd-pipeline-platforms
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
CI/CD in_db
CI/CD for Machine Learning
ci-cd-for-machine-learning
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Informatica in_db
ETL and ELT Tooling
etl-and-elt-tooling
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)
SQL in_db
Pega Programming Languages & DSLs
pega-programming-languages-dsls
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
SQL in_db
Programming Languages for Data Work
programming-languages-for-data-work
Existing dimension (library) · Role↔dimension saved
Python in_db
Cloud Security Scripting & DSL Languages
cloud-security-scripting-dsl-languages
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Python in_db
Programming Languages
programming-languages
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Python in_db
Programming Languages and Scripting
programming-languages-and-scripting
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Python in_db
Programming Languages for Data Work
programming-languages-for-data-work
Existing dimension (library) · Role↔dimension saved
Python in_db
Programming Languages for ML Systems
programming-languages-for-ml-systems
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Python in_db
Programming Languages for XR
programming-languages-for-xr
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Python in_db
Python Programming
python-programming
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Analytics in_db
React Frontend Development
d_init_01
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Data Transformation new
Data Pages and Data Modeling
data-pages-and-data-modeling
Skipped — no persistable v3 meta for new skill skill_not_in_db_v3_proposed

Library artifacts (this run)

Kind Detail DB id
canonical_skill_proposed ETL | type=Data Engineering Tools subtype=general nature=PRACTICE lifespan=MULTI_YEAR
canonical_skill_proposed ELT | type=Data Engineering Tools subtype=general nature=PRACTICE lifespan=MULTI_YEAR
canonical_skill_proposed DataStage | type=Data Engineering Tools subtype=general nature=TOOL lifespan=MULTI_YEAR
canonical_skill_proposed SSIS | type=Data Engineering Tools subtype=general nature=TOOL lifespan=MULTI_YEAR
canonical_skill_proposed T-SQL | type=Programming Languages subtype=general nature=LANGUAGE lifespan=EVERGREEN
canonical_skill_proposed VB.NET | type=Programming Languages subtype=general nature=LANGUAGE lifespan=MULTI_YEAR
canonical_skill_proposed Data Warehouse | type=Data Engineering Tools subtype=general nature=CONCEPT lifespan=MULTI_YEAR
canonical_skill_proposed DataMart | type=Data Engineering Tools subtype=general nature=CONCEPT lifespan=MULTI_YEAR
canonical_skill_proposed Business Intelligence | type=Data Engineering Tools subtype=general nature=CONCEPT lifespan=MULTI_YEAR
canonical_skill_proposed Data Integration | type=Data Engineering Tools subtype=general nature=CONCEPT lifespan=MULTI_YEAR
canonical_skill_proposed Data Ingestion | type=Data Engineering Tools subtype=general nature=CONCEPT lifespan=MULTI_YEAR
canonical_skill_proposed Data Storage | type=Data Engineering Tools subtype=general nature=CONCEPT lifespan=MULTI_YEAR
canonical_skill_proposed Data Aggregation | type=Data Engineering Tools subtype=general nature=CONCEPT lifespan=MULTI_YEAR
canonical_skill_proposed Data Quality | type=Data Engineering Tools subtype=general nature=CONCEPT lifespan=MULTI_YEAR
canonical_skill_proposed Data Audits | type=Data Engineering Tools subtype=general nature=PRACTICE lifespan=MULTI_YEAR
canonical_skill_proposed Data Pipeline Monitoring | type=Data Engineering Tools subtype=general nature=PRACTICE lifespan=MULTI_YEAR
canonical_skill_proposed Code Versioning | type=Practices subtype=general nature=PRACTICE lifespan=MULTI_YEAR
canonical_skill_proposed Code Deployment | type=Practices subtype=general nature=PRACTICE lifespan=MULTI_YEAR
dimension_skill_link_proposed Data Transformation ↔ Data Pages and Data Modeling
nano JD Parser — gpt-4.1-nano click to toggle
RoleDatabase Analyst – Engineering Data Integration
CompanyProgrammers.io
Experience5+
DomainHealthcare
JD type pass
Show raw JSON
{
  "JD_type": "pass",
  "about_company": null,
  "certifications": [],
  "company_name": "Programmers.io",
  "ctc": null,
  "domain": {
    "primary": {
      "aliases": [
        "HealthTech",
        "Clinical Services"
      ],
      "domain": "Healthcare"
    },
    "secondary": null
  },
  "education": [
    {
      "level": "Bachelor\u0027s",
      "qualification": "Bachelor\u0027s - Information Technology / Engineering / Math / Computer Science / Analytics / Engineering (or related)",
      "raw": "Bachelor\u2019s Degree (preferably in information technology, engineering, math, computer science, analytics, engineering or other related field)",
      "requirement": "required"
    }
  ],
  "experience": {
    "max": null,
    "min": 5,
    "raw": "5+"
  },
  "job_locations": [],
  "role": "Database Analyst \u2013 Engineering Data Integration",
  "role_aliases": [
    "Data Analyst",
    "Data Engineer",
    "Database Engineer"
  ],
  "role_archetype": "Data",
  "roles_and_responsibilities": [
    {
      "bullet_count": 13,
      "heading": "Job Description",
      "heading_was_present": true,
      "source_marker": {
        "first_5_words": "\u00b7 Support the full data",
        "last_5_words": "customer and production issues"
      },
      "text": "\u00b7 Support the full data engineering lifecycle including research, proof of concepts, design, development, testing, deployment, and maintenance of data management solutions\n\u00b7 Utilize knowledge of various data management technologies to drive data engineering projects\n\u00b7 Lead data acquisition efforts to gather data from various structured or semi-structured source systems of record to hydrate client data warehouse and power analytics across numerous health care domains\n\u00b7 Leverage combination of ETL/ELT methodologies to pull complex relational and dimensional data to support loading DataMart\u2019s and reporting aggregates.\n\u00b7 Eliminate unwarranted complexity and unneeded interdependencies\n\u00b7 Detect data quality issues, identify root causes, implement fixes, and manage data audits to mitigate data challenges\n\u00b7 Implement, modify, and maintain data integration efforts that improve data efficiency, reliability, and value\n\u00b7 Leverage and facilitate the evolution of best practices for data acquisition, transformation, storage, and aggregation that solve current challenges and reduce the risk of future challenges\n\u00b7 Effectively create data transformations that address business requirements and other constraints\n\u00b7 Partner with the broader analytics organization to make recommendations for changes to data systems and the architecture of data platforms\n\u00b7 Support the implementation of a modern data framework that facilitates business intelligence reporting and advanced analytics\n\u00b7 Prepare high level design documents and detailed technical design documents with best practices to enable efficient data ingestion, transformation and data movement.\n\u00b7 Leverage DevOps tools to enable code versioning and code deployment.\n\u00b7 Leverage data pipeline monitoring tools to detect data integrity issues before they result into user visible outages or data quality issues\n\u00b7 Leverage processes and diagnostics tools to troubleshoot, maintain and optimize solutions and respond to customer and production issues",
      "word_count": 309
    },
    {
      "bullet_count": 5,
      "heading": "Requirements",
      "heading_was_present": true,
      "source_marker": {
        "first_5_words": "\u2022 Microsoft Azure, Data bricks,",
        "last_5_words": "or other similar querying/coding language"
      },
      "text": "\u2022 Microsoft Azure, Data bricks, Spark/Scala, CI/CD implementations\n\u2022 Bachelor\u2019s Degree (preferably in information technology, engineering, math, computer science, analytics, engineering or other related field) \n\u2022 Minimum of 5+ years of combined experience in data engineering, ingestion, normalization, transformation, aggregation, structuring, and storage\n\u00b7 Minimum of 5+ years of combined experience working with industry standard relational, dimensional or non-relational data storage systems\n\u00b7 Minimum of 5+ years of experience in designing ETL/ELT solutions using tools like Informatica, DataStage, SSIS , PL/SQL, T-SQL, etc. \n\u00b7 Minimum of 5+ years of experience in managing data assets using SQL, Python, Scala, VB.NET or other similar querying/coding language",
      "word_count": 104
    }
  ],
  "urls": [
    {
      "type": "website",
      "url": "https://programmers.io/lifeatpio/"
    }
  ]
}
API 1 — extract-from-jd click to toggle
{
  "final_skills": [
    {
      "is_primary": true,
      "skill_name": "Microsoft Azure"
    },
    {
      "is_primary": true,
      "skill_name": "Databricks"
    },
    {
      "is_primary": true,
      "skill_name": "Apache Spark"
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    {
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    },
    {
      "is_primary": true,
      "skill_name": "CI/CD"
    },
    {
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      "skill_name": "ETL"
    },
    {
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      "skill_name": "ELT"
    },
    {
      "is_primary": true,
      "skill_name": "Informatica"
    },
    {
      "is_primary": true,
      "skill_name": "DataStage"
    },
    {
      "is_primary": true,
      "skill_name": "SSIS"
    },
    {
      "is_primary": true,
      "skill_name": "PL/SQL"
    },
    {
      "is_primary": true,
      "skill_name": "T-SQL"
    },
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    },
    {
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    },
    {
      "is_primary": false,
      "skill_name": "VB.NET"
    },
    {
      "is_primary": true,
      "skill_name": "Data Warehouse"
    },
    {
      "is_primary": true,
      "skill_name": "DataMart"
    },
    {
      "is_primary": true,
      "skill_name": "Business Intelligence"
    },
    {
      "is_primary": true,
      "skill_name": "Analytics"
    },
    {
      "is_primary": true,
      "skill_name": "Data Integration"
    },
    {
      "is_primary": true,
      "skill_name": "Data Ingestion"
    },
    {
      "is_primary": true,
      "skill_name": "Data Transformation"
    },
    {
      "is_primary": true,
      "skill_name": "Data Storage"
    },
    {
      "is_primary": true,
      "skill_name": "Data Aggregation"
    },
    {
      "is_primary": true,
      "skill_name": "Data Quality"
    },
    {
      "is_primary": false,
      "skill_name": "Data Audits"
    },
    {
      "is_primary": false,
      "skill_name": "Data Pipeline Monitoring"
    },
    {
      "is_primary": false,
      "skill_name": "Code Versioning"
    },
    {
      "is_primary": false,
      "skill_name": "Code Deployment"
    }
  ],
  "jd_role": {
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    "rationale": null,
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      "Data Analyst",
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    ],
    "role_archetype": "Data",
    "slug": ""
  },
  "nano_parsed": {
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    "about_company": null,
    "certifications": [],
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    "ctc": null,
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    },
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    "job_locations": [],
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        "word_count": 104
      }
    ],
    "urls": [
      {
        "type": "website",
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      }
    ]
  },
  "rejected": false,
  "rejection_reason": null,
  "run_id": "85bc0bd5-dada-4e01-880b-275643703adf",
  "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,
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      },
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        "matched_skills": null,
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        "score": 1.0,
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      }
    ],
    "kra_match_roles": [
      {
        "display_name": "Data Engineer",
        "kra_matches": [
          {
            "kra_text": "Monitors pipeline health, SLA breach alerts, and job failure notifications, and performs root cause analysis for data pipeline incidents.",
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            "similarity": 0.6507
          },
          {
            "kra_text": "Implements data quality validation rules, reconciliation checks, and anomaly detection to ensure data completeness, accuracy, and consistency.",
            "sentence": "\u00b7 Detect data quality issues, identify root causes, implement fixes, and manage data audits to mitigate data challenges",
            "similarity": 0.6172
          },
          {
            "kra_text": "Works with data analysts, data scientists, and business stakeholders to define data models, ingestion schedules, and data delivery requirements.",
            "sentence": "\u00b7 Support the full data engineering lifecycle including research, proof of concepts, design, development, testing, deployment, and maintenance of data management solutions",
            "similarity": 0.6005
          }
        ],
        "matched_count": null,
        "matched_skills": null,
        "role_id": 2,
        "score": 0.6228,
        "slug": "data-engineer",
        "total_count": null
      },
      {
        "display_name": "DevOps Engineer",
        "kra_matches": [
          {
            "kra_text": "Monitors CI/CD pipeline reliability, identifies bottlenecks in delivery workflows, and improves deployment frequency, lead time, and failure recovery rate.",
            "sentence": "\u00b7 Leverage data pipeline monitoring tools to detect data integrity issues before they result into user visible outages or data quality issues",
            "similarity": 0.5858
          },
          {
            "kra_text": "Collaborates with development teams to improve build processes, reduce deployment friction, containerize applications, and adopt DevOps best practices.",
            "sentence": "\u00b7 Leverage DevOps tools to enable code versioning and code deployment.",
            "similarity": 0.5659
          },
          {
            "kra_text": "Monitors CI/CD pipeline reliability, identifies bottlenecks in delivery workflows, and improves deployment frequency, lead time, and failure recovery rate.",
            "sentence": "\u00b7 Leverage processes and diagnostics tools to troubleshoot, maintain and optimize solutions and respond to customer and production issues",
            "similarity": 0.5325
          }
        ],
        "matched_count": null,
        "matched_skills": null,
        "role_id": 10,
        "score": 0.5614,
        "slug": "devops-engineer",
        "total_count": null
      },
      {
        "display_name": "Svelte Frontend Developer",
        "kra_matches": [
          {
            "kra_text": "backend data integration",
            "sentence": "\u00b7 Implement, modify, and maintain data integration efforts that improve data efficiency, reliability, and value",
            "similarity": 0.5511
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          {
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    ],
    "skill_match_roles": [
      {
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        "kra_matches": null,
        "matched_count": 6,
        "matched_skills": [
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          "Informatica",
          "Microsoft Azure",
          "Python",
          "SQL",
          "Scala"
        ],
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      {
        "display_name": "ML Engineer",
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        "matched_skills": [
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          "Microsoft Azure",
          "Python",
          "Scala"
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      {
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          "Scala"
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      {
        "display_name": "Cyber Security Engineer",
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          "Microsoft Azure",
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        ],
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          "Microsoft Azure",
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    ]
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  "stage4_decision": {
    "alias_collision_detected": false,
    "case": "DOMAIN",
    "chosen_role": {
      "display_name": "Data Engineer",
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      "matched_count": null,
      "matched_skills": null,
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    },
    "confidence": 0.97,
    "is_new_role": false,
    "llm2_fired": false,
    "llm2_reasoning": null,
    "matched_dimensions": [
      "End-to-end data engineering lifecycle",
      "Data acquisition and ingestion",
      "Data transformation and aggregation",
      "Data quality and issue remediation",
      "Data platform architecture",
      "Pipeline monitoring and operational support",
      "Cloud data engineering"
    ],
    "matched_kras": [
      "Support the full data engineering lifecycle",
      "Lead data acquisition efforts",
      "Hydrate client data warehouse and power analytics",
      "Support loading DataMart\u2019s and reporting aggregates",
      "Detect data quality issues and identify root causes",
      "Implement, modify, and maintain data integration efforts",
      "Prepare high level and detailed technical design documents",
      "Leverage DevOps tools to enable code versioning and deployment",
      "Detect data integrity issues before outages",
      "Troubleshoot, maintain and optimize solutions"
    ],
    "matched_skills": [
      "ETL/ELT",
      "Microsoft Azure",
      "Databricks",
      "Spark/Scala",
      "CI/CD",
      "data pipeline monitoring",
      "DevOps tools",
      "data integration",
      "data warehouse",
      "DataMart",
      "relational data",
      "dimensional data"
    ],
    "new_role_display_name": null,
    "new_role_slug": null,
    "queued": false,
    "reasoning": "Domain=Data Engineering \u0026 Analytics; The JD centers on end-to-end data engineering, data ingestion/transformation, pipeline reliability, cloud data platform work, and Databricks/Spark implementation, which best matches Data Engineer.",
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    "centroid_updated": true,
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      },
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        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
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        "skill_name": "Data Aggregation",
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        "is_primary": true,
        "queue_id": 8761,
        "role_display_name": "Data Engineer",
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      },
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        "is_primary": false,
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        "skill_name": "Code Versioning",
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        "skill_name": "Code Deployment",
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}
API 2 — extract-details
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        "is_also_category": false,
        "is_extractable": true,
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      "matched_via": "alias"
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    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 1838,
      "existing_alias_text": "Databricks",
      "input_term": "Databricks",
      "matched_canonical": {
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        "is_also_category": false,
        "is_extractable": true,
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      "matched_via": "alias"
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      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 2004,
      "existing_alias_text": "Apache Spark",
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        "is_extractable": true,
        "skill_nature": "FRAMEWORK",
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      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
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      "matched_via": "alias"
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    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 1826,
      "existing_alias_text": "CI/CD",
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        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "METHODOLOGY",
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      "matched_via": "alias"
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    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 311,
      "existing_alias_text": "Informatica",
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      "matched_via": "alias"
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    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 2513,
      "existing_alias_text": "PL/SQL",
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      "matched_via": "alias"
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    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
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      "existing_alias_text": "SQL",
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        "is_extractable": true,
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        "typical_lifespan": "EVERGREEN",
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      "matched_via": "alias"
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      "alias_persisted": false,
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        "is_extractable": true,
        "skill_nature": "LANGUAGE",
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      "matched_via": "alias"
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    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 2634,
      "existing_alias_text": "Analytics",
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      "matched_via": "alias"
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    {
      "alias_persist_skipped_reason": "TODO: REMOVE AFTER TESTING \u2014 alias DB write disabled",
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      "existing_alias_id": 2894,
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      "matched_via": "embedding_alias"
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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": "Fullstack Developer",
              "id": 435,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "fullstack-developer",
              "source": "db"
            },
            {
              "display_name": "Fullstack Developer",
              "id": 15,
              "rationale": null,
              "role_archetype": null,
              "slug": "full-stack-engineer",
              "source": "db"
            }
          ]
        },
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Programming Languages and Scripting",
            "id": 59,
            "rationale": "Languages used to write security automation, analysis scripts, detection logic, and remediation helpers. This is the primary implementation surface for a cybersecurity engineer across tooling and response workflows.",
            "slug": "programming-languages-and-scripting",
            "source": "db"
          },
          "input_skill": "Python",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "Cyber Security Engineer",
              "id": 5,
              "rationale": null,
              "role_archetype": null,
              "slug": "cybersecurity-engineer",
              "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": "Python",
          "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": "Programming Languages for ML Systems",
            "id": 39,
            "rationale": "Languages used to build training code, inference services, evaluation jobs, and ML glue code. This is the primary implementation surface for ML engineers across experimentation and productionization.",
            "slug": "programming-languages-for-ml-systems",
            "source": "db"
          },
          "input_skill": "Python",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "ML Engineer",
              "id": 3,
              "rationale": null,
              "role_archetype": null,
              "slug": "ml-engineer",
              "source": "db"
            },
            {
              "display_name": "MLOps Engineer",
              "id": 16,
              "rationale": null,
              "role_archetype": null,
              "slug": "ml-ops-engineer",
              "source": "db"
            }
          ]
        },
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Programming Languages for XR",
            "id": 97,
            "rationale": "Primary implementation languages used to build immersive client features, interaction logic, and device-specific runtime behavior. This is the core coding surface for AR/VR experiences.",
            "slug": "programming-languages-for-xr",
            "source": "db"
          },
          "input_skill": "Python",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "AR/VR Engineer",
              "id": 8,
              "rationale": null,
              "role_archetype": null,
              "slug": "ar-vr-engineer",
              "source": "db"
            }
          ]
        },
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Python Programming",
            "id": 290,
            "rationale": "Core Python language skills used to implement backend business logic, request handlers, integrations, and service internals. This is the primary coding surface for the role.",
            "slug": "python-programming",
            "source": "db"
          },
          "input_skill": "Python",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "Python Backend Developer",
              "id": 80,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "python-backend-developer",
              "source": "db"
            }
          ]
        }
      ],
      "input_skill": "Python",
      "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": "VB.NET",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Programming Languages",
          "skill_nature": "LANGUAGE",
          "sub_category": "general",
          "typical_lifespan": "MULTI_YEAR",
          "version_strategy": "UNVERSIONED",
          "volatility": "MEDIUM"
        },
        "enrichment": null,
        "keep_log": [],
        "locked_dimensions": [],
        "merge_log": [],
        "placed": null,
        "relationships": null,
        "skill_id": "vb-net",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "Data Warehouse",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Data Engineering Tools",
          "skill_nature": "CONCEPT",
          "sub_category": "general",
          "typical_lifespan": "MULTI_YEAR",
          "version_strategy": "UNVERSIONED",
          "volatility": "MEDIUM"
        },
        "enrichment": null,
        "keep_log": [],
        "locked_dimensions": [],
        "merge_log": [],
        "placed": null,
        "relationships": null,
        "skill_id": "data-warehouse",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "DataMart",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Data Engineering Tools",
          "skill_nature": "CONCEPT",
          "sub_category": "general",
          "typical_lifespan": "MULTI_YEAR",
          "version_strategy": "UNVERSIONED",
          "volatility": "MEDIUM"
        },
        "enrichment": null,
        "keep_log": [],
        "locked_dimensions": [],
        "merge_log": [],
        "placed": null,
        "relationships": null,
        "skill_id": "datamart",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "Business Intelligence",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Data Engineering Tools",
          "skill_nature": "CONCEPT",
          "sub_category": "general",
          "typical_lifespan": "MULTI_YEAR",
          "version_strategy": "UNVERSIONED",
          "volatility": "MEDIUM"
        },
        "enrichment": null,
        "keep_log": [],
        "locked_dimensions": [],
        "merge_log": [],
        "placed": null,
        "relationships": null,
        "skill_id": "business-intelligence",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [
        {
          "alias_text": "Analytics",
          "alias_type": "CANONICAL",
          "id": 2634,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        }
      ],
      "canonical": {
        "category_id": 37,
        "display_name": "Analytics",
        "id": 1664,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "CONCEPT",
        "slug": "analytics",
        "sub_category_id": 1257,
        "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": "Analytics",
          "llm_role": null,
          "roles_from_db": []
        }
      ],
      "input_skill": "Analytics",
      "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 Integration",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Data Engineering Tools",
          "skill_nature": "CONCEPT",
          "sub_category": "general",
          "typical_lifespan": "MULTI_YEAR",
          "version_strategy": "UNVERSIONED",
          "volatility": "MEDIUM"
        },
        "enrichment": null,
        "keep_log": [],
        "locked_dimensions": [],
        "merge_log": [],
        "placed": null,
        "relationships": null,
        "skill_id": "data-integration",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "Data Ingestion",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Data Engineering Tools",
          "skill_nature": "CONCEPT",
          "sub_category": "general",
          "typical_lifespan": "MULTI_YEAR",
          "version_strategy": "UNVERSIONED",
          "volatility": "MEDIUM"
        },
        "enrichment": null,
        "keep_log": [],
        "locked_dimensions": [],
        "merge_log": [],
        "placed": null,
        "relationships": null,
        "skill_id": "data-ingestion",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [
        {
          "alias_text": "Data Transform",
          "alias_type": "CANONICAL",
          "id": 2894,
          "is_primary": true,
          "match_strategy": "CASE_INSENSITIVE"
        }
      ],
      "canonical": {
        "category_id": 2,
        "display_name": "Data Transform",
        "id": 1890,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "CONCEPT",
        "slug": "data-transform",
        "sub_category_id": 1445,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "dimensions": [
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Data Pages and Data Modeling",
            "id": 254,
            "rationale": "Defines how Pega applications source, shape, and expose data for cases and UI components. This includes declarative data access, parameterized data pages, and the data objects used to support process execution.",
            "slug": "data-pages-and-data-modeling",
            "source": "db"
          },
          "input_skill": "Data Transformation",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "Pega Developer",
              "id": 24,
              "rationale": null,
              "role_archetype": null,
              "slug": "pega-developer",
              "source": "db"
            }
          ]
        }
      ],
      "input_skill": "Data Transformation",
      "matched_via": "embedding_alias",
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": null,
      "source_tag": "db",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "Data Storage",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Data Engineering Tools",
          "skill_nature": "CONCEPT",
          "sub_category": "general",
          "typical_lifespan": "MULTI_YEAR",
          "version_strategy": "UNVERSIONED",
          "volatility": "MEDIUM"
        },
        "enrichment": null,
        "keep_log": [],
        "locked_dimensions": [],
        "merge_log": [],
        "placed": null,
        "relationships": null,
        "skill_id": "data-storage",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "Data Aggregation",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Data Engineering Tools",
          "skill_nature": "CONCEPT",
          "sub_category": "general",
          "typical_lifespan": "MULTI_YEAR",
          "version_strategy": "UNVERSIONED",
          "volatility": "MEDIUM"
        },
        "enrichment": null,
        "keep_log": [],
        "locked_dimensions": [],
        "merge_log": [],
        "placed": null,
        "relationships": null,
        "skill_id": "data-aggregation",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "Data Quality",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Data Engineering Tools",
          "skill_nature": "CONCEPT",
          "sub_category": "general",
          "typical_lifespan": "MULTI_YEAR",
          "version_strategy": "UNVERSIONED",
          "volatility": "MEDIUM"
        },
        "enrichment": null,
        "keep_log": [],
        "locked_dimensions": [],
        "merge_log": [],
        "placed": null,
        "relationships": null,
        "skill_id": "data-quality",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "Data Audits",
      "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-audits",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "Data Pipeline Monitoring",
      "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-pipeline-monitoring",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "Code Versioning",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Practices",
          "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": "code-versioning",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "Code Deployment",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Practices",
          "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": "code-deployment",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    }
  ],
  "unmatched_skills": [
    "ETL",
    "ELT",
    "DataStage",
    "SSIS",
    "T-SQL",
    "VB.NET",
    "Data Warehouse",
    "DataMart",
    "Business Intelligence",
    "Data Integration",
    "Data Ingestion",
    "Data Storage",
    "Data Aggregation",
    "Data Quality",
    "Data Audits",
    "Data Pipeline Monitoring",
    "Code Versioning",
    "Code Deployment"
  ]
}
API 3 — final-role-output
{
  "chosen_role": {
    "display_name": "Data Engineer",
    "id": 2,
    "rationale": "Domain=Data Engineering \u0026 Analytics; The JD centers on end-to-end data engineering, data ingestion/transformation, pipeline reliability, cloud data platform work, and Databricks/Spark implementation, which best matches Data Engineer.",
    "role_archetype": null,
    "slug": "data-engineer",
    "source": "db"
  },
  "chosen_role_resolution": "in_db",
  "final_input_skills": [
    {
      "skill": "Microsoft Azure",
      "tag": "in_db"
    },
    {
      "skill": "Databricks",
      "tag": "in_db"
    },
    {
      "skill": "Apache Spark",
      "tag": "in_db"
    },
    {
      "skill": "Scala",
      "tag": "in_db"
    },
    {
      "skill": "CI/CD",
      "tag": "in_db"
    },
    {
      "skill": "ETL",
      "tag": "new"
    },
    {
      "skill": "ELT",
      "tag": "new"
    },
    {
      "skill": "Informatica",
      "tag": "in_db"
    },
    {
      "skill": "DataStage",
      "tag": "new"
    },
    {
      "skill": "SSIS",
      "tag": "new"
    },
    {
      "skill": "PL/SQL",
      "tag": "in_db"
    },
    {
      "skill": "T-SQL",
      "tag": "new"
    },
    {
      "skill": "SQL",
      "tag": "in_db"
    },
    {
      "skill": "Python",
      "tag": "in_db"
    },
    {
      "skill": "VB.NET",
      "tag": "new"
    },
    {
      "skill": "Data Warehouse",
      "tag": "new"
    },
    {
      "skill": "DataMart",
      "tag": "new"
    },
    {
      "skill": "Business Intelligence",
      "tag": "new"
    },
    {
      "skill": "Analytics",
      "tag": "in_db"
    },
    {
      "skill": "Data Integration",
      "tag": "new"
    },
    {
      "skill": "Data Ingestion",
      "tag": "new"
    },
    {
      "skill": "Data Transformation",
      "tag": "in_db"
    },
    {
      "skill": "Data Storage",
      "tag": "new"
    },
    {
      "skill": "Data Aggregation",
      "tag": "new"
    },
    {
      "skill": "Data Quality",
      "tag": "new"
    },
    {
      "skill": "Data Audits",
      "tag": "new"
    },
    {
      "skill": "Data Pipeline Monitoring",
      "tag": "new"
    },
    {
      "skill": "Code Versioning",
      "tag": "new"
    },
    {
      "skill": "Code Deployment",
      "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 \u0026 Hosting Providers",
          "id": 414,
          "rationale": "Knowledge of major cloud and hosting vendor platforms for deploying and managing PHP applications.",
          "slug": "cloud-hosting-providers",
          "source": "db"
        },
        "dimension_id": 414,
        "input_skill": "Microsoft Azure",
        "llm_role": null,
        "matched_chosen_role": false,
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        "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": 1664,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 2,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Data Pages and Data Modeling",
          "id": 254,
          "rationale": "Defines how Pega applications source, shape, and expose data for cases and UI components. This includes declarative data access, parameterized data pages, and the data objects used to support process execution.",
          "slug": "data-pages-and-data-modeling",
          "source": "db"
        },
        "dimension_id": 254,
        "input_skill": "Data Transformation",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Skipped \u2014 no persistable v3 meta for new skill",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "Pega Developer",
            "id": 24,
            "rationale": null,
            "role_archetype": null,
            "slug": "pega-developer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": false,
        "skill_id": null,
        "skill_tag": "new",
        "skipped_reason": "skill_not_in_db_v3_proposed"
      }
    ],
    "new_skills_created": 0,
    "role_dimension_saved": 0,
    "skill_dimension_saved": 0,
    "skipped": 1
  },
  "planner_output": null,
  "run_id": "85bc0bd5-dada-4e01-880b-275643703adf"
}

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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