Pipeline run
68a33329-1151-4231-b842-601c13bf45ce
Client output enrichment
v2 Skill cluster · Nature of work · AI index · Tech stack maturity · Evidence · KRA descriptionvocab breakdown (legacy)
Signals
Post-classification
Captured for admin review
1 POST /skills/extract-from-jd
2 POST /skills/extract-details
3 POST /skills/final-role-output
Data Engineer
domain · Data Engineering & Analytics CASE DOMAINslug: data-engineer · id: 2 · source: db
Domain=Data Engineering & Analytics; The JD centers on building and maintaining scalable data pipelines, ETL, Snowflake/Spark processing, Azure Databricks data engineering, and API support, which best matches Data Engineer.
Matched skills
Matched dimensions
Matched KRAs
Resolution:
in_db
— role exists in library; skill↔dim and role↔dim links saved when applicable.
Job description
About the job 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: Pan India E-mail: sujatha.getari@ltimindtree.com I.Balaji@ltimindtree.com Gajula.Ramu@ltimindtree.com Diksha.Chauhan2@ltimindtree.com Shivalila.Yantettinawar@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.
Aliases — catalog
- Apache Spark (CANONICAL)
- apache spark 3 (VERSION)
- spark (VERSION)
- spark 3 (VERSION)
- spark 3.x (VERSION)
- spark3 (VERSION)
Context tags (catalog)
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 |
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)
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) |
Aliases — catalog
- Snowflake (CANONICAL) primary
Context tags (catalog)
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 |
Aliases — catalog
- SQL (CANONICAL) primary
Context tags (catalog)
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 |
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Cloud Platforms
- Sub-category
- Data Engineering Tools
- Skill nature
- PLATFORM
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Cloud Platforms
- Sub-category
- Data Engineering Tools
- Skill nature
- PLATFORM
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
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)
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) |
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Data Engineering Tools
- Sub-category
- general
- Skill nature
- TOOL
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Data Engineering Tools
- Sub-category
- general
- Skill nature
- TOOL
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Cloud Platforms
- Sub-category
- Data Engineering Tools
- Skill nature
- PLATFORM
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Aliases — catalog
- Azure Service Bus (CANONICAL) primary
Context tags (catalog)
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) |
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Cloud Platforms
- Sub-category
- Message Brokers
- Skill nature
- PLATFORM
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Databases
- Sub-category
- general
- Skill nature
- CONCEPT
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Aliases — catalog
- Views (CANONICAL) primary
Context tags (catalog)
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) |
Aliases — catalog
- Indexes (CANONICAL) primary
- indexes (CANONICAL)
Context tags (catalog)
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) |
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Databases
- Sub-category
- general
- Skill nature
- CONCEPT
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Databases
- Sub-category
- general
- Skill nature
- CONCEPT
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Aliases — catalog
- Streams (CANONICAL) primary
Context tags (catalog)
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) |
Aliases — catalog
- Task (CANONICAL) primary
Context tags (catalog)
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
|
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Databases
- Sub-category
- general
- Skill nature
- TOOL
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Aliases — catalog
- APIs (CANONICAL)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Protocol
- Sub-category
- Application Programming Interfaces
- Confidence
- 0.93
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: APIs are a hiring-pipeline staple across backend, mobile, and platform JDs; REST/GraphQL/API design appears in large volumes of job postings and vendor docs, indicating broad adoption.
Skill profile (library / DB)
- Skill nature
- PROTOCOL
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 10
- Sub-category id
- 902
- 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) |
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Data Engineering Tools
- 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 |
|---|---|---|---|---|---|---|
| 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 | |
| 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) | |
| 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 |
| APIs | in_db |
React Frontend Development
d_init_01
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Library artifacts (this run)
| Kind | Detail | DB id |
|---|---|---|
| canonical_skill_proposed | Azure Databricks | type=Cloud Platforms subtype=Data Engineering Tools nature=PLATFORM lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Azure Data Lake | type=Cloud Platforms subtype=Data Engineering Tools nature=PLATFORM lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Pandas | type=Data Engineering Tools subtype=general nature=TOOL lifespan=MULTI_YEAR | |
| canonical_skill_proposed | NumPy | type=Data Engineering Tools subtype=general nature=TOOL lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Azure Data Factory | type=Cloud Platforms subtype=Data Engineering Tools nature=PLATFORM lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Azure Event Hubs | type=Cloud Platforms subtype=Message Brokers nature=PLATFORM lifespan=MULTI_YEAR | |
| 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 | Snowpipes | type=Databases subtype=general nature=TOOL lifespan=MULTI_YEAR | |
| canonical_skill_proposed | ETL | type=Data Engineering Tools subtype=general nature=PRACTICE lifespan=MULTI_YEAR | |
| dimension_skill_link_proposed | Tasks ↔ Concurrency and Async Workflows |
nano JD Parser — gpt-4.1-nano click to toggle
Show raw JSON
{
"JD_type": "pass",
"about_company": null,
"archetype_override_applied": true,
"archetype_override_matched_skills": [
"Python",
"Snowflake",
"Flow",
"Make",
"Databricks",
"APIs",
"Azure",
"Indexes",
"Cloud",
"Flask",
"SQL",
"Streams",
"Location",
"Views",
"channels",
"Azure Service Bus"
],
"certifications": [],
"company_name": "LTIMindtree",
"ctc": null,
"domain": {
"primary": {
"aliases": [],
"domain": "Other"
},
"secondary": null
},
"education": [],
"experience": {
"max": null,
"min": null,
"raw": null
},
"job_locations": [
{
"aliases": [],
"city": null,
"country": "India",
"state": null,
"work_mode": null
}
],
"role": null,
"role_aliases": [],
"role_archetype": "Engineering",
"roles_and_responsibilities": [
{
"bullet_count": 9,
"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": 7,
"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 Data",
"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": 20
}
],
"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": "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": "Azure Data Factory"
},
{
"is_primary": true,
"skill_name": "Azure Service Bus"
},
{
"is_primary": true,
"skill_name": "Azure Event Hubs"
},
{
"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": "Snowpipes"
},
{
"is_primary": true,
"skill_name": "APIs"
},
{
"is_primary": true,
"skill_name": "ETL"
}
],
"jd_role": null,
"nano_parsed": {
"JD_type": "pass",
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"certifications": [],
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"ctc": null,
"domain": {
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"aliases": [],
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},
"secondary": null
},
"education": [],
"experience": {
"max": null,
"min": null,
"raw": null
},
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{
"aliases": [],
"city": null,
"country": "India",
"state": null,
"work_mode": null
}
],
"role": null,
"role_aliases": [],
"role_archetype": "Engineering",
"roles_and_responsibilities": [
{
"bullet_count": 9,
"heading": "Responsibilities",
"heading_was_present": true,
"source_marker": {
"first_5_words": "Develop scalable pipelines to efficiently",
"last_5_words": "reporting pipelines and infrastructure"
},
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"word_count": 134
},
{
"bullet_count": 7,
"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 Data",
"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": 20
}
],
"urls": []
},
"rejected": false,
"rejection_reason": null,
"run_id": "68a33329-1151-4231-b842-601c13bf45ce",
"stage3_signals": {
"alias_found": false,
"alias_match_roles": [],
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{
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{
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"similarity": 0.6614
},
{
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"similarity": 0.6073
},
{
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},
{
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{
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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
},
{
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}
],
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},
{
"display_name": "Flutter Developer",
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{
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"sentence": "Collaborate with Architects and Business stakeholders to understand reporting requirements",
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},
{
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"sentence": "Excellent communication and collaboration skills",
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},
{
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}
],
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},
{
"display_name": "ML Engineer",
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{
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"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": 0.4343
}
],
"matched_count": null,
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"score": 0.4702,
"slug": "ml-engineer",
"total_count": null
},
{
"display_name": "DevOps Engineer",
"kra_matches": [
{
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"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": 0.4222
},
{
"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": 0.4125
}
],
"matched_count": null,
"matched_skills": null,
"role_id": 10,
"score": 0.4613,
"slug": "devops-engineer",
"total_count": null
}
],
"skill_match_roles": [
{
"display_name": "Fullstack Developer",
"kra_matches": null,
"matched_count": 4,
"matched_skills": [
"Azure Service Bus",
"Flask",
"Indexes",
"Python"
],
"role_id": 15,
"score": 0.1818,
"slug": "full-stack-engineer",
"total_count": 22
},
{
"display_name": "Data Engineer",
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"matched_skills": [
"Apache Spark",
"Python",
"SQL",
"Snowflake"
],
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},
{
"display_name": "Backend Developer",
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"matched_skills": [
"Flask",
"Indexes",
"Python"
],
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},
{
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"Flask",
"Indexes",
"Python"
],
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},
{
"display_name": "Node.js Backend Developer",
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"matched_skills": [
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"Flask",
"Indexes"
],
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]
},
"stage4_decision": {
"alias_collision_detected": false,
"case": "DOMAIN",
"chosen_role": {
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"matched_count": null,
"matched_skills": null,
"role_id": 2,
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},
"confidence": 0.98,
"is_new_role": false,
"llm2_fired": false,
"llm2_reasoning": null,
"matched_dimensions": [
"Data Pipeline Engineering",
"ETL / Data Transformation",
"Snowflake Data Development",
"Cloud Data Engineering on Azure",
"API Development for Data Services",
"Reporting Pipeline Maintenance"
],
"matched_kras": [
"Develop scalable pipelines to process transform data",
"Design and develop a scalable and robust framework",
"Perform aggregations on high volume of data",
"Develop Stored Procedures Views Indexes Triggers and Functions",
"Use Snowflake features Streams Tasks Snowpipes",
"Leverage Azure Databricks and Datalake for processing",
"Develop APIs using Pythons Flask framework",
"Maintain and improve existing reporting pipelines"
],
"matched_skills": [
"Spark",
"Python",
"Snowflake",
"SQL",
"Azure Databricks",
"Azure Datalake",
"Azure Data Factory",
"Pandas",
"Numpy",
"Flask",
"Azure Service Bus",
"Azure Event hubs"
],
"new_role_display_name": null,
"new_role_slug": null,
"queued": false,
"reasoning": "Domain=Data Engineering \u0026 Analytics; The JD centers on building and maintaining scalable data pipelines, ETL, Snowflake/Spark processing, Azure Databricks data engineering, and API support, which best matches Data Engineer.",
"sub_role": null
},
"stage5_updates": {
"centroid_n_after": 173,
"centroid_updated": true,
"collision_log_id": null,
"new_kra_attached": null,
"new_skills_attached": [
{
"is_primary": true,
"queue_id": 9046,
"role_display_name": "Data Engineer",
"role_slug": "data-engineer",
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"status": "pending"
},
{
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"role_display_name": "Data Engineer",
"role_slug": "data-engineer",
"skill_name": "Azure Data Lake",
"status": "pending"
},
{
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"role_display_name": "Data Engineer",
"role_slug": "data-engineer",
"skill_name": "Pandas",
"status": "pending"
},
{
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"queue_id": 9049,
"role_display_name": "Data Engineer",
"role_slug": "data-engineer",
"skill_name": "NumPy",
"status": "pending"
},
{
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"role_display_name": "Data Engineer",
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"status": "pending"
},
{
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"role_display_name": "Data Engineer",
"role_slug": "data-engineer",
"skill_name": "Azure Event Hubs",
"status": "pending"
},
{
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"queue_id": 9052,
"role_display_name": "Data Engineer",
"role_slug": "data-engineer",
"skill_name": "Stored Procedures",
"status": "pending"
},
{
"is_primary": true,
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"role_display_name": "Data Engineer",
"role_slug": "data-engineer",
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"status": "pending"
},
{
"is_primary": true,
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"role_display_name": "Data Engineer",
"role_slug": "data-engineer",
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"status": "pending"
},
{
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"role_display_name": "Data Engineer",
"role_slug": "data-engineer",
"skill_name": "Tasks",
"status": "pending"
},
{
"is_primary": true,
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"role_display_name": "Data Engineer",
"role_slug": "data-engineer",
"skill_name": "Snowpipes",
"status": "pending"
},
{
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}
],
"queue_entry_id": null,
"v3_pipeline_triggered": false,
"v3_role_slug": null,
"v3_run_id": null
}
}
API 2 — extract-details
{
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{
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"alias_persisted": false,
"existing_alias_id": 2510,
"existing_alias_text": "spark",
"input_term": "Spark",
"matched_canonical": {
"category_id": 5,
"display_name": "Apache Spark",
"id": 1350,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "FRAMEWORK",
"slug": "apache-spark",
"sub_category_id": 1021,
"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"matched_via": "alias"
},
{
"alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
"alias_persisted": false,
"existing_alias_id": 67,
"existing_alias_text": "Python",
"input_term": "Python",
"matched_canonical": {
"category_id": 6,
"display_name": "Python",
"id": 5,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "LANGUAGE",
"slug": "python",
"sub_category_id": 96,
"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"matched_via": "alias"
},
{
"alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
"alias_persisted": false,
"existing_alias_id": 299,
"existing_alias_text": "Snowflake",
"input_term": "Snowflake",
"matched_canonical": {
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"is_also_category": false,
"is_extractable": true,
"skill_nature": "PLATFORM",
"slug": "snowflake",
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"typical_lifespan": "EVERGREEN",
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},
"matched_via": "alias"
},
{
"alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
"alias_persisted": false,
"existing_alias_id": 271,
"existing_alias_text": "SQL",
"input_term": "SQL",
"matched_canonical": {
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"display_name": "SQL",
"id": 101,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "LANGUAGE",
"slug": "sql",
"sub_category_id": 97,
"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"matched_via": "alias"
},
{
"alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
"alias_persisted": false,
"existing_alias_id": 1980,
"existing_alias_text": "Flask",
"input_term": "Flask",
"matched_canonical": {
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"id": 1344,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "FRAMEWORK",
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"typical_lifespan": "EVERGREEN",
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},
"matched_via": "alias"
},
{
"alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
"alias_persisted": false,
"existing_alias_id": 4841,
"existing_alias_text": "Azure Service Bus",
"input_term": "Azure Service Bus",
"matched_canonical": {
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"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"
},
"matched_via": "alias"
},
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]
},
{
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}
],
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{
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},
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}
]
}
],
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},
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},
{
"alias_text": "indexes",
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"id": 2312,
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}
],
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},
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{
"dimension": {
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},
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}
]
},
{
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},
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},
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}
]
},
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},
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}
]
}
],
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},
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},
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},
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},
{
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"input_skill": "Functions",
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},
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}
],
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},
"dimensions": [
{
"dimension": {
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"rationale": "Core implementation language used to build Flutter app logic, UI composition, and client-side feature code. This is the primary coding surface for shared cross-platform app development.",
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},
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]
}
],
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},
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}
],
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{
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},
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]
}
],
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},
{
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},
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},
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"was_in_llm_skills": true
},
{
"aliases_in_db": [
{
"alias_text": "APIs",
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"id": 1828,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
}
],
"canonical": {
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"is_also_category": false,
"is_extractable": true,
"skill_nature": "PROTOCOL",
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"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.",
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},
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}
],
"input_skill": "APIs",
"matched_via": "alias",
"new_alias_persisted": false,
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"new_skill_meta": null,
"source_tag": "db",
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},
{
"aliases_in_db": [],
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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"
},
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},
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}
],
"unmatched_skills": [
"Azure Databricks",
"Azure Data Lake",
"Pandas",
"NumPy",
"Azure Data Factory",
"Azure Event Hubs",
"Stored Procedures",
"Triggers",
"Functions",
"Snowpipes",
"ETL"
]
}
API 3 — final-role-output
{
"chosen_role": {
"display_name": "Data Engineer",
"id": 2,
"rationale": "Domain=Data Engineering \u0026 Analytics; The JD centers on building and maintaining scalable data pipelines, ETL, Snowflake/Spark processing, Azure Databricks data engineering, and API support, which best matches Data Engineer.",
"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": "Azure Databricks",
"tag": "new"
},
{
"skill": "Azure Data Lake",
"tag": "new"
},
{
"skill": "Flask",
"tag": "in_db"
},
{
"skill": "Pandas",
"tag": "new"
},
{
"skill": "NumPy",
"tag": "new"
},
{
"skill": "Azure Data Factory",
"tag": "new"
},
{
"skill": "Azure Service Bus",
"tag": "in_db"
},
{
"skill": "Azure Event Hubs",
"tag": "new"
},
{
"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"
},
{
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"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 2,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Dart Programming",
"id": 311,
"rationale": "Core implementation language used to build Flutter app logic, UI composition, and client-side feature code. This is the primary coding surface for shared cross-platform app development.",
"slug": "dart-programming",
"source": "db"
},
"dimension_id": 311,
"input_skill": "Streams",
"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": "Flutter Developer",
"id": 74,
"rationale": null,
"role_archetype": "Engineering",
"slug": "flutter-developer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 2913,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 2,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Concurrency and Async Workflows",
"id": 292,
"rationale": "Programming techniques for handling concurrent requests, asynchronous I/O, and coordination of background execution in Python services. This cluster is coherent because backend developers must safely manage throughput and responsiveness.",
"slug": "concurrency-and-async-workflows",
"source": "db"
},
"dimension_id": 292,
"input_skill": "Tasks",
"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": ".NET Backend Developer",
"id": 83,
"rationale": null,
"role_archetype": "Engineering",
"slug": "dotnet-backend-developer",
"source": "db"
},
{
"display_name": "Python Backend Developer",
"id": 80,
"rationale": null,
"role_archetype": "Engineering",
"slug": "python-backend-developer",
"source": "db"
}
],
"skill_dimension_saved": false,
"skill_id": null,
"skill_tag": "new",
"skipped_reason": "skill_not_in_db_v3_proposed"
},
{
"chosen_role_id": 2,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "React Frontend Development",
"id": 96,
"rationale": "Building interactive web user interfaces with React.js, including component composition, state management, hooks, and rendering patterns. React.js belongs here because it is a core library for client-side UI development in modern web applications.",
"slug": "d_init_01",
"source": "db"
},
"dimension_id": 96,
"input_skill": "APIs",
"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": 1192,
"skill_tag": "in_db",
"skipped_reason": null
}
],
"new_skills_created": 0,
"role_dimension_saved": 0,
"skill_dimension_saved": 0,
"skipped": 1
},
"planner_output": null,
"run_id": "68a33329-1151-4231-b842-601c13bf45ce"
}
LLM Calls
Every model call made for this run, in pipeline order. Click a card to see the model's response.