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Pipeline run

7a3b49bf-af6f-4e2e-9932-0d707c431604

Pipeline LLM cost (USD)
API 1: $0.0039 API 2: $0.0004 API 3: $0.0000 Total: $0.0043

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 Spark/Snowflake/Databricks data pipelines that transform high-volume data, generate PDF reports, support downstream APIs, and keep reporting infrastructure running with Python and SQL. Also develops Snowflake objects and Flask APIs, while working with stakeholders to refine reporting needs.
"Develop scalable pipelines to efficiently process transform data using Spark"
Tech stack maturity
Mainstream Modern
The skill set centers on widely adopted, contemporary data engineering technologies like Spark, Snowflake, Python, SQL, streams, and views, which align with a mainstream modern stack rather than bleeding-edge or legacy systems.
AI index (0 = no AI use, 5 = totally AI-dependent · v2.1)
0.20 / 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): AI
Evidence — skills matched in JD (21)
Spark Python Snowflake SQL Stored Procedures Views Indexes Triggers Functions Streams Tasks Snowpipe Azure Databricks Azure Data Lake Flask Pandas NumPy ETL Azure Data Factory Azure Service Bus Azure Event Hubs
Skill cluster (7 dimension groups, role-scoped)
Programming Languages for Data Work
Python SQL
Asynchronous Messaging and Event Streaming
Azure Service Bus
Cloud Data Warehouses
Snowflake
ETL and ELT Tooling
Spark
Relational Database Design
Indexes
Web Application Frameworks
Flask
Cross-cutting / unaligned
Stored Procedures Views Triggers Functions Streams Tasks Snowpipe Azure Databricks Azure Data Lake Pandas NumPy ETL Azure Data Factory Azure Event Hubs
Show KRA description ↓
Develop scalable pipelines to efficiently process transform data using Spark Design and develop a scalable and robust framework for generating PDF reports using Python Spark Utilize Snowflake Spark SQL to perform aggregations on high volume of data Develop Stored Procedures Views Indexes Triggers and Functions in Snowflake Database to maintain data and share with downstream applications in form of APIs Should use Snowflake features Streams Tasks Snowpipes etc wherever needed in the development flow Leverage Azure Databricks and Datalake for data processing and storage Develop APIs using Pythons Flask framework to support front end applications Collaborate with Architects and Business stakeholders to understand reporting requirements Maintain and improve existing reporting pipelines and infrastructure Proven experience as a Data Engineer with a strong understanding of data pipelines and ETL processes Proficiency in Python with experience in data manipulation libraries such as Pandas and Numpy Experience with SQL Snowflake Spark for data querying and aggregations Familiarity with Azure cloud services such as Data Factory Databricks and Datalake Experience developing APIs using frameworks like Flask is a plus Excellent communication and collaboration skills Ability to work independently and manage multiple tasks effectively Python, SQL, Spark, Azure Data Factory, Azure Datalake, Azure Databricks Azure Service Bus and Azure Event hubs

Signals

Skill data-engineer
0.21
Alias data-engineer
1.00
KRA data-engineer
0.62

Post-classification

Centroidupdated · n=196
Alias collision log
New-role queue
New skills captured12
New KRA captured

Captured for admin review

Stored Procedures primary Data Engineer pending
Triggers primary Data Engineer pending
Functions primary Data Engineer pending
Tasks primary Data Engineer pending
Snowpipe primary Data Engineer pending
Azure Databricks primary Data Engineer pending
Azure Data Lake primary Data Engineer pending
Pandas primary Data Engineer pending
NumPy primary Data Engineer pending
ETL primary Data Engineer pending
Azure Data Factory primary Data Engineer pending
Azure Event Hubs Data Engineer pending
Status: completed Created: 2026-05-27T14:39:36.860051Z Updated: 2026-06-12T17:28:32.986535Z API 3 duration: 30390 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 data-engineer 0.21 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
1
Skipped

Job description

Are you looking for a new career challenge? With LTIMindtree, are you ready to embark on a data-driven career? Working for global leading manufacturing client for providing an engaging product experience through best-in-class PIM implementation and building rich, relevant, and trusted product information across channels and digital touchpoints so their end customers can make an informed purchase decision – will surely be a fulfilling experience.


Location: Coimbatore


Email: SUJATHA.GETARI@ltimindtree.com
Gajula.Ramu@ltimindtree.com
Shivalila.Yantettinawar@ltimindtree.com
Diksha.Chauhan2@ltimindtree.com
I.Balaji@ltimindtree.com




Responsibilities
Develop scalable pipelines to efficiently process transform data using Spark
Design and develop a scalable and robust framework for generating PDF reports using Python Spark
Utilize Snowflake Spark SQL to perform aggregations on high volume of data
Develop Stored Procedures Views Indexes Triggers and Functions in Snowflake Database to maintain data and share with downstream applications in form of APIs
Should use Snowflake features Streams Tasks Snowpipes etc wherever needed in the development flow
Leverage Azure Databricks and Datalake for data processing and storage
Develop APIs using Pythons Flask framework to support front end applications
Collaborate with Architects and Business stakeholders to understand reporting requirements
Maintain and improve existing reporting pipelines and infrastructure
Qualifications
Proven experience as a Data Engineer with a strong understanding of data pipelines and ETL processes
Proficiency in Python with experience in data manipulation libraries such as Pandas and Numpy
Experience with SQL Snowflake Spark for data querying and aggregations
Familiarity with Azure cloud services such as Data Factory Databricks and Datalake
Experience developing APIs using frameworks like Flask is a plus
Excellent communication and collaboration skills
Ability to work independently and manage multiple tasks effectively


Mandatory Skills: Python, SQL, Spark, Azure Data Factory, Azure Datalake, Azure Databricks
Azure Service Bus and Azure Event hubs


Why join us?
• Work in industry leading implementations for Tier-1 clients
• Accelerated career growth and global exposure
• Collaborative, inclusive work environment rooted in innovation
• Exposure to best-in-class automation framework
• Innovation first culture: We embrace automation, AI insights and clean data


Know someone who fits this perfectly? Tag them – let’s connect the right talent with right opportunity


DM or email to know more
Let’s build something great together

Skills from this JD

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

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
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)
Snowflake Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Snowflake id=105 · snowflake

Aliases — catalog

  • Snowflake (CANONICAL) primary

Context tags (catalog)

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

Stored enrichment (catalog DB)

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

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

Skill profile (library / DB)

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

Dimensions (API 2 worklist)

  • Cloud Data Warehouses Catalog dimension db id 22

    Library dimension (catalog)

    Roles linked in library: Data Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Cloud Data Warehouses
cloud-data-warehouses
Existing dimension (library) · Role↔dimension saved
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
Stored Procedures Primary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

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

Derived legacy fields
Category
Databases
Sub-category
general
Skill nature
CONCEPT
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Views Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Views id=3116 · views

Aliases — catalog

  • Views (CANONICAL) primary

Context tags (catalog)

AJAX CRUD operations MVC RESTful API UI components client-side rendering component lifecycle data binding data visualization dynamic content event handling query builder server-side rendering state management template rendering

Stored enrichment (catalog DB)

Category
Framework
Sub-category
Query Builder Framework
Vendor
null
License
unknown
Confidence
0.90
Version strategy
NOT_APPLICABLE

Maturity reasoning: “Views” as a query-builder framework has low JD volume and is largely overshadowed by ORM/query tools like Django ORM, SQLAlchemy, and Knex in current postings and docs.

Skill profile (library / DB)

Skill nature
FRAMEWORK
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
5
Sub-category id
2424
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Views and Content Querying Catalog dimension db id 347

    Library dimension (catalog)

    Roles linked in library: Drupal Dev

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Views and Content Querying
views-and-content-querying
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Indexes Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Indexes id=1428 · indexes

Aliases — catalog

  • Indexes (CANONICAL) primary
  • indexes (CANONICAL)

Context tags (catalog)

B-tree MySQL NoSQL PostgreSQL SQL SQL Server SQL indexing bitmap index clustered index composite index data retrieval database optimization database performance full-text index full-text search hash index index fragmentation index maintenance index scan index seek indexing strategy non-clustered index query optimization query performance scan vs seek unique index

Stored enrichment (catalog DB)

Category
Concept
Sub-category
Database Indexing
Confidence
0.88
Version strategy
NOT_APPLICABLE

Maturity reasoning: Database indexes are a standard topic in SQL/NoSQL job descriptions and core interview screens; they’re broadly used across PostgreSQL, MySQL, and MongoDB for query performance tuning.

Skill profile (library / DB)

Skill nature
CONCEPT
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
2
Sub-category id
2477
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Relational Data Modeling Catalog dimension db id 216

    Library dimension (catalog)

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

  • 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

  • Relational Database Usage Catalog dimension db id 371

    Library dimension (catalog)

    Roles linked in library: Go Backend Developer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Relational Data Modeling
relational-data-modeling
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Relational Database Design
relational-database-design
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Relational Database Usage
relational-database-usage
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Triggers Primary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

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

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

Skill enrichment (orchestrator / LLM)

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

Derived legacy fields
Category
Databases
Sub-category
general
Skill nature
CONCEPT
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Streams Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Streams id=2913 · streams

Aliases — catalog

  • Streams (CANONICAL) primary

Context tags (catalog)

asynchronous backpressure cold streams data flow event sourcing event-driven hot streams message brokers observables operators publish-subscribe reactive reactive programming stream processing stream transformations

Stored enrichment (catalog DB)

Category
Concept
Sub-category
Reactive Stream Concept
Confidence
0.86
Version strategy
NOT_APPLICABLE

Maturity reasoning: Reactive streams are widely used in JDs for Java, RxJS, and Kafka ecosystems; major vendors and frameworks still document them as a standard async/data-flow pattern rather than a niche tool.

Skill profile (library / DB)

Skill nature
CONCEPT
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
2
Sub-category id
2226
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Dart Programming Catalog dimension db id 311

    Library dimension (catalog)

    Roles linked in library: Flutter Developer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Dart Programming
dart-programming
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Tasks Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Task id=3264 · task

Aliases — catalog

  • Task (CANONICAL) primary

Context tags (catalog)

background tasks concurrent tasks task allocation task automation task completion task dependencies task execution task lifecycle task management task monitoring task orchestration task prioritization task queue task scheduling task tracking

Stored enrichment (catalog DB)

Category
Runtime
Sub-category
Task Runtime
Confidence
0.62
Version strategy
NOT_APPLICABLE

Maturity reasoning: "Task" as a runtime skill has low JD volume and is usually a generic term, not a named platform; market signals point to niche usage rather than broad hiring demand.

Skill profile (library / DB)

Skill nature
RUNTIME
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
67
Sub-category id
2566
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Concurrency and Async Workflows Catalog dimension db id 292

    Library dimension (catalog)

    Roles linked in library: .NET Backend Developer, Python Backend Developer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Concurrency and Async Workflows
concurrency-and-async-workflows
Skipped — no persistable v3 meta for new skill
skill_not_in_db_v3_proposed
Snowpipe 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
Azure Databricks 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
Azure Data Lake 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
Flask Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Flask id=1344 · flask

Aliases — catalog

  • Flask (CANONICAL) primary
  • flask 2 (VERSION)
  • flask 2.x (VERSION)
  • flask 3 (VERSION)
  • flask 3.x (VERSION)
  • flask2 (VERSION)
  • flask3 (VERSION)
  • flask>=3 (VERSION)

Context tags (catalog)

API Blueprints Flask-Migrate Flask-RESTful Flask-SQLAlchemy Flask-WTF JSON Jinja2 RESTful RESTful APIs SQLAlchemy Werkzeug debugging deployment gunicorn middleware routing session management template rendering unit testing virtual environments virtualenv

Stored enrichment (catalog DB)

Category
Framework
Sub-category
Web Framework
Vendor
Pallets Projects
License
bsd
Year introduced
2010
Confidence
0.99
Version strategy
SEPARATE_ENTITY
Version tag
3.x

Maturity reasoning: Flask appears in many Python web developer job postings and remains a common lightweight framework in hiring pipelines, though often alongside Django/FastAPI rather than as a niche tool.

Skill profile (library / DB)

Skill nature
FRAMEWORK
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
5
Sub-category id
35
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • React Frontend Development Catalog dimension db id 96

    Library dimension (catalog)

  • Web Application Frameworks Catalog dimension db id 2

    Library dimension (catalog)

    Roles linked in library: Backend Developer, Fullstack Developer, Fullstack Developer, Java Backend Developer, Node.js Backend Developer, PHP Backend Developer, Python Backend Developer

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)
Web Application Frameworks
web-application-frameworks
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Pandas 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
STABLE
Typical lifespan
EVERGREEN
Version strategy
UNVERSIONED
NumPy 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
STABLE
Typical lifespan
EVERGREEN
Version strategy
UNVERSIONED
ETL Primary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

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

Derived legacy fields
Category
Data Engineering Tools
Sub-category
general
Skill nature
PRACTICE
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
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
Azure Service Bus Secondary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Azure Service Bus id=3292 · azure-service-bus

Aliases — catalog

  • Azure Service Bus (CANONICAL) primary

Context tags (catalog)

Azure Functions asynchronous communication broker cloud messaging dead-letter queue event-driven integration message message broker message routing messaging patterns publish-subscribe queue reliability scalability service bus explorer service orchestration subscription topic

Stored enrichment (catalog DB)

Category
Service
Sub-category
Messaging Service
Vendor
Microsoft
License
other_open
Year introduced
2010
Confidence
0.98
Version strategy
NOT_APPLICABLE

Maturity reasoning: Commonly listed in cloud/backend JDs for enterprise messaging and event-driven systems; Microsoft actively supports it as a core Azure service, with broad adoption alongside Kafka/RabbitMQ in production stacks.

Skill profile (library / DB)

Skill nature
CLOUD_SERVICE
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
11
Sub-category id
119
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Asynchronous Messaging and Event Streaming Catalog dimension db id 297

    Library dimension (catalog)

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

  • Cloud Platforms & Managed Services Catalog dimension db id 221

    Library dimension (catalog)

    Roles linked in library: Fullstack Developer, Go Backend Developer, Node.js Backend Developer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Asynchronous Messaging and Event Streaming
asynchronous-messaging-and-event-streaming
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Cloud Platforms & Managed Services
cloud-platforms-managed-services
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Azure Event Hubs 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
Cloud Platforms
Sub-category
general
Skill nature
PLATFORM
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
Spark in_db
ETL and ELT Tooling
etl-and-elt-tooling
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)
Snowflake in_db
Cloud Data Warehouses
cloud-data-warehouses
Existing dimension (library) · Role↔dimension saved
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
Views in_db
Views and Content Querying
views-and-content-querying
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Indexes in_db
Relational Data Modeling
relational-data-modeling
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Indexes in_db
Relational Database Design
relational-database-design
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Indexes in_db
Relational Database Usage
relational-database-usage
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Streams in_db
Dart Programming
dart-programming
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Tasks new
Concurrency and Async Workflows
concurrency-and-async-workflows
Skipped — no persistable v3 meta for new skill skill_not_in_db_v3_proposed
Flask in_db
React Frontend Development
d_init_01
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Flask in_db
Web Application Frameworks
web-application-frameworks
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Azure Service Bus in_db
Asynchronous Messaging and Event Streaming
asynchronous-messaging-and-event-streaming
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Azure Service Bus in_db
Cloud Platforms & Managed Services
cloud-platforms-managed-services
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)

Library artifacts (this run)

Kind Detail DB id
canonical_skill_proposed Stored Procedures | type=Databases subtype=general nature=CONCEPT lifespan=MULTI_YEAR
canonical_skill_proposed Triggers | type=Databases subtype=general nature=CONCEPT lifespan=MULTI_YEAR
canonical_skill_proposed Functions | type=Databases subtype=general nature=CONCEPT lifespan=MULTI_YEAR
canonical_skill_proposed Snowpipe | type=Data Engineering Tools subtype=general nature=TOOL lifespan=MULTI_YEAR
canonical_skill_proposed Azure Databricks | type=Cloud Platforms subtype=general nature=PLATFORM lifespan=MULTI_YEAR
canonical_skill_proposed Azure Data Lake | type=Cloud Platforms subtype=general nature=PLATFORM lifespan=MULTI_YEAR
canonical_skill_proposed Pandas | type=Data Engineering Tools subtype=general nature=TOOL lifespan=EVERGREEN
canonical_skill_proposed NumPy | type=Data Engineering Tools subtype=general nature=TOOL lifespan=EVERGREEN
canonical_skill_proposed ETL | type=Data Engineering Tools subtype=general nature=PRACTICE lifespan=MULTI_YEAR
canonical_skill_proposed Azure Data Factory | type=Cloud Platforms subtype=general nature=PLATFORM lifespan=MULTI_YEAR
canonical_skill_proposed Azure Event Hubs | type=Cloud Platforms subtype=general nature=PLATFORM lifespan=MULTI_YEAR
dimension_skill_link_proposed Tasks ↔ Concurrency and Async Workflows
nano JD Parser — gpt-4.1-nano click to toggle
RoleData Engineer
CompanyLTIMindtree
DomainManufacturing
Location Coimbatore, India
JD type pass
Show raw JSON
{
  "JD_type": "pass",
  "about_company": null,
  "certifications": [],
  "company_name": "LTIMindtree",
  "ctc": null,
  "domain": {
    "primary": {
      "aliases": [
        "Manufacturing Industry",
        "Industrial Manufacturing"
      ],
      "domain": "Manufacturing"
    },
    "secondary": null
  },
  "education": [],
  "experience": {
    "max": null,
    "min": null,
    "raw": null
  },
  "job_locations": [
    {
      "aliases": [
        "Coimbatore",
        "Kovai"
      ],
      "city": "Coimbatore",
      "country": "India",
      "state": null,
      "work_mode": null
    }
  ],
  "role": "Data Engineer",
  "role_aliases": [
    "Data Engineer",
    "Data Developer",
    "ETL Developer"
  ],
  "role_archetype": "Data",
  "roles_and_responsibilities": [
    {
      "bullet_count": 0,
      "heading": "Responsibilities",
      "heading_was_present": true,
      "source_marker": {
        "first_5_words": "Develop scalable pipelines to efficiently",
        "last_5_words": "reporting pipelines and infrastructure"
      },
      "text": "Develop scalable pipelines to efficiently process transform data using Spark\nDesign and develop a scalable and robust framework for generating PDF reports using Python Spark\nUtilize Snowflake Spark SQL to perform aggregations on high volume of data\nDevelop Stored Procedures Views Indexes Triggers and Functions in Snowflake Database to maintain data and share with downstream applications in form of APIs\nShould use Snowflake features Streams Tasks Snowpipes etc wherever needed in the development flow\nLeverage Azure Databricks and Datalake for data processing and storage\nDevelop APIs using Pythons Flask framework to support front end applications\nCollaborate with Architects and Business stakeholders to understand reporting requirements\nMaintain and improve existing reporting pipelines and infrastructure",
      "word_count": 134
    },
    {
      "bullet_count": 0,
      "heading": "Qualifications",
      "heading_was_present": true,
      "source_marker": {
        "first_5_words": "Proven experience as a Data",
        "last_5_words": "manage multiple tasks effectively"
      },
      "text": "Proven experience as a Data Engineer with a strong understanding of data pipelines and ETL processes\nProficiency in Python with experience in data manipulation libraries such as Pandas and Numpy\nExperience with SQL Snowflake Spark for data querying and aggregations\nFamiliarity with Azure cloud services such as Data Factory Databricks and Datalake\nExperience developing APIs using frameworks like Flask is a plus\nExcellent communication and collaboration skills\nAbility to work independently and manage multiple tasks effectively",
      "word_count": 83
    },
    {
      "bullet_count": 2,
      "heading": "Mandatory Skills",
      "heading_was_present": true,
      "source_marker": {
        "first_5_words": "Python, SQL, Spark, Azure",
        "last_5_words": "Service Bus and Azure Event"
      },
      "text": "Python, SQL, Spark, Azure Data Factory, Azure Datalake, Azure Databricks\nAzure Service Bus and Azure Event hubs",
      "word_count": 22
    }
  ],
  "urls": []
}
API 1 — extract-from-jd click to toggle
{
  "final_skills": [
    {
      "is_primary": true,
      "skill_name": "Spark"
    },
    {
      "is_primary": true,
      "skill_name": "Python"
    },
    {
      "is_primary": true,
      "skill_name": "Snowflake"
    },
    {
      "is_primary": true,
      "skill_name": "SQL"
    },
    {
      "is_primary": true,
      "skill_name": "Stored Procedures"
    },
    {
      "is_primary": true,
      "skill_name": "Views"
    },
    {
      "is_primary": true,
      "skill_name": "Indexes"
    },
    {
      "is_primary": true,
      "skill_name": "Triggers"
    },
    {
      "is_primary": true,
      "skill_name": "Functions"
    },
    {
      "is_primary": true,
      "skill_name": "Streams"
    },
    {
      "is_primary": true,
      "skill_name": "Tasks"
    },
    {
      "is_primary": true,
      "skill_name": "Snowpipe"
    },
    {
      "is_primary": true,
      "skill_name": "Azure Databricks"
    },
    {
      "is_primary": true,
      "skill_name": "Azure Data Lake"
    },
    {
      "is_primary": true,
      "skill_name": "Flask"
    },
    {
      "is_primary": true,
      "skill_name": "Pandas"
    },
    {
      "is_primary": true,
      "skill_name": "NumPy"
    },
    {
      "is_primary": true,
      "skill_name": "ETL"
    },
    {
      "is_primary": true,
      "skill_name": "Azure Data Factory"
    },
    {
      "is_primary": false,
      "skill_name": "Azure Service Bus"
    },
    {
      "is_primary": false,
      "skill_name": "Azure Event Hubs"
    }
  ],
  "jd_role": {
    "display_name": "Data Engineer",
    "rationale": null,
    "role_aliases": [
      "Data Engineer",
      "Data Developer",
      "ETL Developer"
    ],
    "role_archetype": "Data",
    "slug": ""
  },
  "nano_parsed": {
    "JD_type": "pass",
    "about_company": null,
    "certifications": [],
    "company_name": "LTIMindtree",
    "ctc": null,
    "domain": {
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        ],
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      },
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    },
    "education": [],
    "experience": {
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      "min": null,
      "raw": null
    },
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          "Kovai"
        ],
        "city": "Coimbatore",
        "country": "India",
        "state": null,
        "work_mode": null
      }
    ],
    "role": "Data Engineer",
    "role_aliases": [
      "Data Engineer",
      "Data Developer",
      "ETL Developer"
    ],
    "role_archetype": "Data",
    "roles_and_responsibilities": [
      {
        "bullet_count": 0,
        "heading": "Responsibilities",
        "heading_was_present": true,
        "source_marker": {
          "first_5_words": "Develop scalable pipelines to efficiently",
          "last_5_words": "reporting pipelines and infrastructure"
        },
        "text": "Develop scalable pipelines to efficiently process transform data using Spark\nDesign and develop a scalable and robust framework for generating PDF reports using Python Spark\nUtilize Snowflake Spark SQL to perform aggregations on high volume of data\nDevelop Stored Procedures Views Indexes Triggers and Functions in Snowflake Database to maintain data and share with downstream applications in form of APIs\nShould use Snowflake features Streams Tasks Snowpipes etc wherever needed in the development flow\nLeverage Azure Databricks and Datalake for data processing and storage\nDevelop APIs using Pythons Flask framework to support front end applications\nCollaborate with Architects and Business stakeholders to understand reporting requirements\nMaintain and improve existing reporting pipelines and infrastructure",
        "word_count": 134
      },
      {
        "bullet_count": 0,
        "heading": "Qualifications",
        "heading_was_present": true,
        "source_marker": {
          "first_5_words": "Proven experience as a Data",
          "last_5_words": "manage multiple tasks effectively"
        },
        "text": "Proven experience as a Data Engineer with a strong understanding of data pipelines and ETL processes\nProficiency in Python with experience in data manipulation libraries such as Pandas and Numpy\nExperience with SQL Snowflake Spark for data querying and aggregations\nFamiliarity with Azure cloud services such as Data Factory Databricks and Datalake\nExperience developing APIs using frameworks like Flask is a plus\nExcellent communication and collaboration skills\nAbility to work independently and manage multiple tasks effectively",
        "word_count": 83
      },
      {
        "bullet_count": 2,
        "heading": "Mandatory Skills",
        "heading_was_present": true,
        "source_marker": {
          "first_5_words": "Python, SQL, Spark, Azure",
          "last_5_words": "Service Bus and Azure Event"
        },
        "text": "Python, SQL, Spark, Azure Data Factory, Azure Datalake, Azure Databricks\nAzure Service Bus and Azure Event hubs",
        "word_count": 22
      }
    ],
    "urls": []
  },
  "rejected": false,
  "rejection_reason": null,
  "run_id": "7a3b49bf-af6f-4e2e-9932-0d707c431604",
  "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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      }
    ],
    "kra_match_roles": [
      {
        "display_name": "Data Engineer",
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            "similarity": 0.6614
          },
          {
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            "sentence": "Utilize Snowflake Spark SQL to perform aggregations on high volume of data",
            "similarity": 0.6073
          },
          {
            "kra_text": "Develops batch and real-time streaming data pipelines using Apache Spark, Apache Kafka, Apache Flink, or Airflow for data movement and processing at scale.",
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      {
        "display_name": "Fullstack Developer",
        "kra_matches": [
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            "similarity": 0.4774
          },
          {
            "kra_text": "Works closely with product managers and UX designers to translate requirements and wireframes into working software features through iterative development.",
            "sentence": "Collaborate with Architects and Business stakeholders to understand reporting requirements",
            "similarity": 0.4744
          },
          {
            "kra_text": "Delivers features through CI/CD pipelines using automated tests, staged rollouts, feature flags, and incremental deployments.",
            "sentence": "Maintain and improve existing reporting pipelines and infrastructure",
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          }
        ],
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        "matched_skills": null,
        "role_id": 15,
        "score": 0.4717,
        "slug": "full-stack-engineer",
        "total_count": null
      },
      {
        "display_name": "Flutter Developer",
        "kra_matches": [
          {
            "kra_text": "collaborate with design, product, and backend teams",
            "sentence": "Collaborate with Architects and Business stakeholders to understand reporting requirements",
            "similarity": 0.5265
          },
          {
            "kra_text": "collaborate with design, product, and backend teams",
            "sentence": "Excellent communication and collaboration skills",
            "similarity": 0.4702
          },
          {
            "kra_text": "integrate external APIs and data sources",
            "sentence": "Develop APIs using Pythons Flask framework to support front end applications",
            "similarity": 0.4139
          }
        ],
        "matched_count": null,
        "matched_skills": null,
        "role_id": 74,
        "score": 0.4702,
        "slug": "flutter-developer",
        "total_count": null
      },
      {
        "display_name": "ML Engineer",
        "kra_matches": [
          {
            "kra_text": "Prepares, cleans, and transforms training datasets, manages feature stores, and builds feature engineering pipelines for model training.",
            "sentence": "Develop scalable pipelines to efficiently process transform data using Spark",
            "similarity": 0.4992
          },
          {
            "kra_text": "Translates product requirements into machine learning system specifications including feature definitions, model architecture choices, and success metric definitions.",
            "sentence": "Collaborate with Architects and Business stakeholders to understand reporting requirements",
            "similarity": 0.477
          },
          {
            "kra_text": "Prepares, cleans, and transforms training datasets, manages feature stores, and builds feature engineering pipelines for model training.",
            "sentence": "Proven experience as a Data Engineer with a strong understanding of data pipelines and ETL processes",
            "similarity": 0.4344
          }
        ],
        "matched_count": null,
        "matched_skills": null,
        "role_id": 3,
        "score": 0.4702,
        "slug": "ml-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": "Maintain and improve existing reporting pipelines and infrastructure",
            "similarity": 0.5494
          },
          {
            "kra_text": "Collaborates with development teams to improve build processes, reduce deployment friction, containerize applications, and adopt DevOps best practices.",
            "sentence": "Collaborate with Architects and Business stakeholders to understand reporting requirements",
            "similarity": 0.4221
          },
          {
            "kra_text": "Collaborates with development teams to improve build processes, reduce deployment friction, containerize applications, and adopt DevOps best practices.",
            "sentence": "Excellent communication and collaboration skills",
            "similarity": 0.4124
          }
        ],
        "matched_count": null,
        "matched_skills": null,
        "role_id": 10,
        "score": 0.4613,
        "slug": "devops-engineer",
        "total_count": null
      }
    ],
    "skill_match_roles": [
      {
        "display_name": "Data Engineer",
        "kra_matches": null,
        "matched_count": 4,
        "matched_skills": [
          "Apache Spark",
          "Python",
          "SQL",
          "Snowflake"
        ],
        "role_id": 2,
        "score": 0.2105,
        "slug": "data-engineer",
        "total_count": 19
      },
      {
        "display_name": "Fullstack Developer",
        "kra_matches": null,
        "matched_count": 3,
        "matched_skills": [
          "Flask",
          "Indexes",
          "Python"
        ],
        "role_id": 15,
        "score": 0.1579,
        "slug": "full-stack-engineer",
        "total_count": 19
      },
      {
        "display_name": "Fullstack Developer",
        "kra_matches": null,
        "matched_count": 3,
        "matched_skills": [
          "Flask",
          "Indexes",
          "Python"
        ],
        "role_id": 435,
        "score": 0.1579,
        "slug": "fullstack-developer",
        "total_count": 19
      },
      {
        "display_name": "Backend Developer",
        "kra_matches": null,
        "matched_count": 3,
        "matched_skills": [
          "Flask",
          "Indexes",
          "Python"
        ],
        "role_id": 1,
        "score": 0.1579,
        "slug": "backend-engineer",
        "total_count": 19
      },
      {
        "display_name": "Python Backend Developer",
        "kra_matches": null,
        "matched_count": 3,
        "matched_skills": [
          "Flask",
          "Indexes",
          "Python"
        ],
        "role_id": 80,
        "score": 0.1579,
        "slug": "python-backend-developer",
        "total_count": 19
      }
    ]
  },
  "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 data-engineer 0.21 does not contradict",
    "sub_role": null
  },
  "stage5_updates": {
    "centroid_n_after": 196,
    "centroid_updated": true,
    "collision_log_id": null,
    "new_kra_attached": null,
    "new_skills_attached": [
      {
        "is_primary": true,
        "queue_id": 10240,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "Stored Procedures",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 10241,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "Triggers",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 10242,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "Functions",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 10243,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "Tasks",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 10244,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "Snowpipe",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 10245,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "Azure Databricks",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 10246,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "Azure Data Lake",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 10247,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "Pandas",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 10248,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "NumPy",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 10249,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "ETL",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 10250,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "Azure Data Factory",
        "status": "pending"
      },
      {
        "is_primary": false,
        "queue_id": 10251,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "Azure Event Hubs",
        "status": "pending"
      }
    ],
    "queue_entry_id": null,
    "v3_pipeline_triggered": false,
    "v3_role_slug": null,
    "v3_run_id": null
  }
}
API 2 — extract-details
{
  "alias_matches": [
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
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      "alias_persisted": false,
      "existing_alias_id": 67,
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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": 271,
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      "matched_canonical": {
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      "matched_via": "alias"
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      "alias_persisted": false,
      "existing_alias_id": 4368,
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        "typical_lifespan": "EVERGREEN",
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      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "TODO: REMOVE AFTER TESTING \u2014 alias DB write disabled",
      "alias_persisted": false,
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      "alias_persisted": false,
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      "matched_via": "alias"
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    {
      "display_name": "Java Backend Developer",
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      "rationale": null,
      "role_archetype": "Engineering",
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      "roles_from_db": [
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          "display_name": "Cyber Security Engineer",
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    },
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      "dimension": {
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        "slug": "programming-languages-for-data-work",
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      "input_skill": "Python",
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    },
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      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Programming Languages for ML Systems",
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        "slug": "programming-languages-for-ml-systems",
        "source": "db"
      },
      "input_skill": "Python",
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      "roles_from_db": [
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    },
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        "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",
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        "slug": "views-and-content-querying",
        "source": "db"
      },
      "input_skill": "Views",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": "Drupal Dev",
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          "rationale": null,
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        "slug": "relational-data-modeling",
        "source": "db"
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        "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",
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      "input_skill": "Indexes",
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      "roles_from_db": [
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          "display_name": ".NET Backend Developer",
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          "display_name": "Kotlin Backend Developer",
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          "role_archetype": "Engineering",
          "slug": "kotlin-server-backend-developer",
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        {
          "display_name": "Node.js Backend Developer",
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          "rationale": null,
          "role_archetype": "Engineering",
          "slug": "node-backend-developer",
          "source": "db"
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        {
          "display_name": "Python Backend Developer",
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          "rationale": null,
          "role_archetype": "Engineering",
          "slug": "python-backend-developer",
          "source": "db"
        },
        {
          "display_name": "Ruby Backend Developer",
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          "rationale": null,
          "role_archetype": "Engineering",
          "slug": "ruby-backend-developer",
          "source": "db"
        },
        {
          "display_name": "Scala Backend Developer",
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      "dimensions": [],
      "input_skill": "ETL",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Data Engineering Tools",
          "skill_nature": "PRACTICE",
          "sub_category": "general",
          "typical_lifespan": "MULTI_YEAR",
          "version_strategy": "UNVERSIONED",
          "volatility": "MEDIUM"
        },
        "enrichment": null,
        "keep_log": [],
        "locked_dimensions": [],
        "merge_log": [],
        "placed": null,
        "relationships": null,
        "skill_id": "etl",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "Azure Data Factory",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Cloud Platforms",
          "skill_nature": "PLATFORM",
          "sub_category": "general",
          "typical_lifespan": "MULTI_YEAR",
          "version_strategy": "UNVERSIONED",
          "volatility": "MEDIUM"
        },
        "enrichment": null,
        "keep_log": [],
        "locked_dimensions": [],
        "merge_log": [],
        "placed": null,
        "relationships": null,
        "skill_id": "azure-data-factory",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [
        {
          "alias_text": "Azure Service Bus",
          "alias_type": "CANONICAL",
          "id": 4841,
          "is_primary": true,
          "match_strategy": "CASE_INSENSITIVE"
        }
      ],
      "canonical": {
        "category_id": 11,
        "display_name": "Azure Service Bus",
        "id": 3292,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "CLOUD_SERVICE",
        "slug": "azure-service-bus",
        "sub_category_id": 119,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "dimensions": [
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Asynchronous Messaging and Event Streaming",
            "id": 297,
            "rationale": "Asynchronous communication patterns and broker technologies used to decouple backend services and move work off the request path. Includes queues, pub/sub, event streams, consumer groups, dead-letter queues, and delivery semantics across systems such as Kafka, RabbitMQ, NATS, SQS/SNS, Pulsar, and ActiveMQ.",
            "slug": "asynchronous-messaging-and-event-streaming",
            "source": "db"
          },
          "input_skill": "Azure Service Bus",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": ".NET Backend Developer",
              "id": 83,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "dotnet-backend-developer",
              "source": "db"
            },
            {
              "display_name": "Go Backend Developer",
              "id": 81,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "go-backend-developer",
              "source": "db"
            },
            {
              "display_name": "Kotlin Backend Developer",
              "id": 84,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "kotlin-server-backend-developer",
              "source": "db"
            },
            {
              "display_name": "Node.js Backend Developer",
              "id": 82,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "node-backend-developer",
              "source": "db"
            },
            {
              "display_name": "Scala Backend Developer",
              "id": 87,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "scala-backend-developer",
              "source": "db"
            }
          ]
        },
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Cloud Platforms \u0026 Managed Services",
            "id": 221,
            "rationale": "Operates and integrates vendor-specific cloud compute, storage, and hosting services.",
            "slug": "cloud-platforms-managed-services",
            "source": "db"
          },
          "input_skill": "Azure Service Bus",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "Fullstack Developer",
              "id": 15,
              "rationale": null,
              "role_archetype": null,
              "slug": "full-stack-engineer",
              "source": "db"
            },
            {
              "display_name": "Go Backend Developer",
              "id": 81,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "go-backend-developer",
              "source": "db"
            },
            {
              "display_name": "Node.js Backend Developer",
              "id": 82,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "node-backend-developer",
              "source": "db"
            }
          ]
        }
      ],
      "input_skill": "Azure Service Bus",
      "matched_via": "alias",
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": null,
      "source_tag": "db",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "Azure Event Hubs",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Cloud Platforms",
          "skill_nature": "PLATFORM",
          "sub_category": "general",
          "typical_lifespan": "MULTI_YEAR",
          "version_strategy": "UNVERSIONED",
          "volatility": "MEDIUM"
        },
        "enrichment": null,
        "keep_log": [],
        "locked_dimensions": [],
        "merge_log": [],
        "placed": null,
        "relationships": null,
        "skill_id": "azure-event-hubs",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    }
  ],
  "unmatched_skills": [
    "Stored Procedures",
    "Triggers",
    "Functions",
    "Snowpipe",
    "Azure Databricks",
    "Azure Data Lake",
    "Pandas",
    "NumPy",
    "ETL",
    "Azure Data Factory",
    "Azure Event Hubs"
  ]
}
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 data-engineer 0.21 does not contradict",
    "role_archetype": null,
    "slug": "data-engineer",
    "source": "db"
  },
  "chosen_role_resolution": "in_db",
  "final_input_skills": [
    {
      "skill": "Spark",
      "tag": "in_db"
    },
    {
      "skill": "Python",
      "tag": "in_db"
    },
    {
      "skill": "Snowflake",
      "tag": "in_db"
    },
    {
      "skill": "SQL",
      "tag": "in_db"
    },
    {
      "skill": "Stored Procedures",
      "tag": "new"
    },
    {
      "skill": "Views",
      "tag": "in_db"
    },
    {
      "skill": "Indexes",
      "tag": "in_db"
    },
    {
      "skill": "Triggers",
      "tag": "new"
    },
    {
      "skill": "Functions",
      "tag": "new"
    },
    {
      "skill": "Streams",
      "tag": "in_db"
    },
    {
      "skill": "Tasks",
      "tag": "in_db"
    },
    {
      "skill": "Snowpipe",
      "tag": "new"
    },
    {
      "skill": "Azure Databricks",
      "tag": "new"
    },
    {
      "skill": "Azure Data Lake",
      "tag": "new"
    },
    {
      "skill": "Flask",
      "tag": "in_db"
    },
    {
      "skill": "Pandas",
      "tag": "new"
    },
    {
      "skill": "NumPy",
      "tag": "new"
    },
    {
      "skill": "ETL",
      "tag": "new"
    },
    {
      "skill": "Azure Data Factory",
      "tag": "new"
    },
    {
      "skill": "Azure Service Bus",
      "tag": "in_db"
    },
    {
      "skill": "Azure Event Hubs",
      "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": "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": "Spark",
        "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": 1350,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 2,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Cloud Security Scripting \u0026 DSL Languages",
          "id": 248,
          "rationale": "Proficiency in programming and domain-specific languages used to automate and script cloud security controls.",
          "slug": "cloud-security-scripting-dsl-languages",
          "source": "db"
        },
        "dimension_id": 248,
        "input_skill": "Python",
        "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": "Cloud Security Engineer",
            "id": 23,
            "rationale": null,
            "role_archetype": null,
            "slug": "cloud-security-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 5,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 2,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Programming Languages",
          "id": 1,
          "rationale": "Primary implementation languages used to build client and server feature code. Full stack engineers need enough fluency to move across layers and implement product behavior end to end.",
          "slug": "programming-languages",
          "source": "db"
        },
        "dimension_id": 1,
        "input_skill": "Python",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "Backend Developer",
            "id": 1,
            "rationale": null,
            "role_archetype": "A Backend Engineer designs, builds, and maintains the server-side logic and data handling that power applications and services. They focus on implementing reliable business functionality, integrating with other systems, and ensuring the backend is scalable, maintainable, and observable.",
            "slug": "backend-engineer",
            "source": "db"
          },
          {
            "display_name": "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"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 5,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 2,
        "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"
        },
        "dimension_id": 59,
        "input_skill": "Python",
        "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": "Cyber Security Engineer",
            "id": 5,
            "rationale": null,
            "role_archetype": null,
            "slug": "cybersecurity-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 5,
        "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": "Python",
        "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": 5,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 2,
        "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"
        },
        "dimension_id": 39,
        "input_skill": "Python",
        "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": 5,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 2,
        "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"
        },
        "dimension_id": 97,
        "input_skill": "Python",
        "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": "AR/VR Engineer",
            "id": 8,
            "rationale": null,
            "role_archetype": null,
            "slug": "ar-vr-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 5,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 2,
        "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"
        },
        "dimension_id": 290,
        "input_skill": "Python",
        "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": "Python Backend Developer",
            "id": 80,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "python-backend-developer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 5,
        "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": "Snowflake",
        "llm_role": null,
        "matched_chosen_role": true,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension saved",
        "role_dimension_saved": true,
        "roles_from_db": [
          {
            "display_name": "Data Engineer",
            "id": 2,
            "rationale": null,
            "role_archetype": null,
            "slug": "data-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 105,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 2,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "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 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": "Views and Content Querying",
          "id": 347,
          "rationale": "Building listings, feeds, and filtered content displays using Drupal\u0027s query and presentation tools. This cluster is coherent because many Drupal features are delivered through reusable content queries rather than custom code.",
          "slug": "views-and-content-querying",
          "source": "db"
        },
        "dimension_id": 347,
        "input_skill": "Views",
        "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": "Drupal Dev",
            "id": 228,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "drupal-dev",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 3116,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 2,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Relational Data Modeling",
          "id": 216,
          "rationale": "Modeling and tuning relational persistence for backend features. PHP backend developers need this to shape schemas, indexes, transactions, and query-aware data structures that support application behavior.",
          "slug": "relational-data-modeling",
          "source": "db"
        },
        "dimension_id": 216,
        "input_skill": "Indexes",
        "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": "Fullstack Developer",
            "id": 15,
            "rationale": null,
            "role_archetype": null,
            "slug": "full-stack-engineer",
            "source": "db"
          },
          {
            "display_name": "Fullstack Developer",
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        "dimension_id": 292,
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        "outcome_line": "Skipped \u2014 no persistable v3 meta for new skill",
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        "roles_from_db": [
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        "skill_dimension_saved": true,
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        ],
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}

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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