Pipeline run
ba3c40e4-41c7-4c21-9f0b-9479914520a7
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
CASE Aslug: data-engineer · id: 2 · source: db
Exact alias hit on data-engineer (1.0) — no other alias at this confidence; skill_top python-backend-developer 0.15 does not contradict
Resolution:
in_db
— role exists in library; skill↔dim and role↔dim links saved when applicable.
Job description
About Greystar: As the Global Leader in Rental Housing, Greystar provides end-to-end property management services for residential housing, apartment homes, furnished corporate housing, and mixed-use properties incorporating retail space. About the role: We are seeking a Senior Data Engineer skilled in Databricks, Python, Scala, Azure Synapse and Azure Data Factory to join our team of data engineers within Greystar Information Technology. This team serves Greystar by ingesting data from multiple sources, making it available to internal stakeholders, and by interfacing with and exchanging data between a variety of internal and external systems. You will be responsible for building and enhancing our Enterprise Data Platform (EDP) which is built within the Azure cloud and utilizes modern processes and technologies such as Databricks, Synapse, Azure Data Factory (ADF), ADLS Gen2 Data Lake, Azure DevOps and CI/CD pipelines. You will develop, deploy and troubleshoot complex data ingestion pipelines and processes. Your curious mind and attention to detail will be an asset, as will your extensive knowledge and experience in the data engineering space. JOB DESCRIPTION How you will make in impact: • Design, develop, optimize, and maintain data architecture and pipelines that adhere to ETL principles and business goals • Collaborate with data engineers, data consumers, and other team members to come up with simple, functional, and elegant solutions that balance the data needs across the organization • Solve complex data problems to deliver insights that helps the organization achieve its goals • Create data products that will be used throughout the organization • Advise, consult, mentor and coach other data and analytic professionals on data standards and practices • Foster a culture of sharing, re-use, design for scale stability, and operational efficiency of data and analytic solutions • Develop and deliver documentation on data engineering capabilities, standards, and processes; participate in coaching, mentoring, design reviews and code reviews • Partner with business analysts and solutions architects to develop technical architectures for strategic enterprise projects and initiatives. • Deliver awesome code Technical Qualifications: • 7+ years relevant and progressive data engineering experience • In-depth technical expertise with Azure Databricks (PySpark and Python), Microsoft Azure architecture, including Synapse, ADF (Azure Data Factory) pipelines • Extensive hands-on experience with data pipelines, handling various source and target locations such as APIs, Data Lakes, file-based formats, SQL, and No-SQL databases • Experience in engineering practices such as development, code refactoring, and leveraging design patterns, CI/CD, and building highly scalable data applications and processes • Experience developing batch ETL pipelines; streaming data pipelines are a plus • Knowledge of advanced data engineering concepts such as dimensional modeling, ETL, data governance, data warehousing involving structured and unstructured data • Thorough knowledge of Synapse and SQL Server including T-SQL and stored procedures • Experience performing root cause analysis on internal and external data and processes to answer specific business questions and identify opportunities for improvement. • Advanced working SQL knowledge and experience working with relational databases, query authoring (SQL) as well as working familiarity with a variety of databases. • Knowledge and understanding of Boomi is a plus Additional Qualifications and Experience: • Excellent problem-solving skills and experience • Effective communication skills • Strong collaboration skills • "Self-starter" attitude and the ability to make decisions with minimal guidance from others • Innovative and passionate about your work and the work of your teammates • Ability to comprehend and analyze operational systems and ask appropriate questions to determine how to improve, migrate or modify the solution to meet business needs • Experience with data ingestion and engineering, specifically involving large data volumes • Knowledge of CI/CD release pipelines is a plus • Knowledge of the Agile development process • Experience working with and supporting cross-functional teams in a dynamic environment Education: • Bachelor’s degree in computer science, information technology, business management information systems, or equivalent experience. Interested candidates can directly apply through the below link. https://tlv.sh/bCDV
Skills from this JD
Each row merges API 1 extraction, API 2 library match / v3 orchestration (dimensions + locked dims), and API 3 persistence tags.
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Cloud Platforms
- Sub-category
- general
- Skill nature
- PLATFORM
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
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
|
— | — |
Skipped — no persistable v3 meta for new skill
skill_not_in_db_v3_proposed
|
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 & DSLs Catalog dimension db id 475
Library dimension (catalog)
Roles linked in library: Engineering Manager
-
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 & DSLs
programming-languages-dsls
|
✓ | — | 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
- Microsoft Azure (CANONICAL) primary
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Platform
- Sub-category
- Cloud Platform
- Vendor
- Microsoft
- License
- other_open
- Year introduced
- 2010
- Confidence
- 0.99
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Azure appears in large volumes of cloud/DevOps job descriptions and is a core hyperscaler alongside AWS/GCP; Microsoft’s continued product investment and broad enterprise adoption signal mainstream demand.
Skill profile (library / DB)
- Skill nature
- PLATFORM
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 9
- Sub-category id
- 46
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
Cloud & Hosting Providers Catalog dimension db id 414
Library dimension (catalog)
Roles linked in library: PHP Backend Developer
-
Cloud Platforms Catalog dimension db id 20
Library dimension (catalog)
Roles linked in library: .NET Backend Developer, Backend Developer, Cyber Security Engineer, Data Engineer, DevOps Engineer, Fullstack Developer, Go Backend Developer, Java Backend Developer, Kotlin Backend Developer, ML Engineer, MLOps Engineer, Node.js Backend Developer, Python Backend Developer, Scala Backend Developer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Cloud & Hosting Providers
cloud-hosting-providers
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Cloud Platforms
cloud-platforms
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved |
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Cloud Platforms
- Sub-category
- general
- 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
- general
- 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
- Data Engineering Tools
- Sub-category
- general
- Skill nature
- PRACTICE
- 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) |
Aliases — catalog
- Data Lakes (CANONICAL)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Architecture
- Sub-category
- Data Lake Architecture
- Confidence
- 0.90
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Data lakes are widely listed in cloud/data platform job descriptions and are a standard architecture in AWS, Azure, and GCP ecosystems; they’re a common hiring-pipeline staple rather than a niche pattern.
Skill profile (library / DB)
- Skill nature
- PATTERN
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 1
- Sub-category id
- 1025
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
Cloud Storage and Data Services Catalog dimension db id 144
Library dimension (catalog)
Roles linked in library: Cloud Architect
-
React Frontend Development Catalog dimension db id 96
Library dimension (catalog)
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Cloud Storage and Data Services
cloud-storage-and-data-services
|
— | — |
Skipped — no persistable v3 meta for new skill
skill_not_in_db_v3_proposed
|
|
React Frontend Development
d_init_01
|
— | — |
Skipped — no persistable v3 meta for new skill
skill_not_in_db_v3_proposed
|
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 & DSLs Catalog dimension db id 475
Library dimension (catalog)
Roles linked in library: Engineering Manager
-
Programming Languages for Data Work Catalog dimension db id 21
Library dimension (catalog)
Roles linked in library: Data Engineer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Pega Programming Languages & DSLs
pega-programming-languages-dsls
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Programming Languages & DSLs
programming-languages-dsls
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Programming Languages for Data Work
programming-languages-for-data-work
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved |
Aliases — catalog
- NoSQL (CANONICAL)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Concept
- Sub-category
- Database Paradigm
- Confidence
- 0.93
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: NoSQL is broadly listed in job descriptions across backend/data roles, with MongoDB, DynamoDB, and Cassandra appearing as common market signals; it remains a hiring-pipeline staple rather than a niche or sunset tech.
Skill profile (library / DB)
- Skill nature
- CONCEPT
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 2
- Sub-category id
- 1019
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
NoSQL Databases Catalog dimension db id 19
Library dimension (catalog)
Roles linked in library: Backend Developer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
NoSQL Databases
nosql-databases
|
✓ | — | 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
- Software Development
- Sub-category
- general
- Skill nature
- PRACTICE
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Aliases — catalog
- design patterns (CANONICAL)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Concept
- Sub-category
- Software Design Patterns
- Confidence
- 0.94
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Design patterns are a standard interview/JD topic across backend and frontend roles; job postings commonly mention them alongside OOP and system design, and they remain core in books, courses, and code reviews.
Skill profile (library / DB)
- Skill nature
- CONCEPT
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 2
- Sub-category id
- 1247
- 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) |
Aliases — catalog
- CI/CD (CANONICAL)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Methodology
- Sub-category
- Ci Cd Process
- Confidence
- 0.93
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: CI/CD appears in a large share of software engineering JDs and is a standard requirement across DevOps, platform, and backend roles; major vendors like GitHub, GitLab, and AWS all center product roadmaps on CI/CD pipelines.
Skill profile (library / DB)
- Skill nature
- METHODOLOGY
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 8
- Sub-category id
- 900
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
CI/CD Pipeline Platforms Catalog dimension db id 150
Library dimension (catalog)
Roles linked in library: DevOps Engineer
-
CI/CD for Machine Learning Catalog dimension db id 56
Library dimension (catalog)
Roles linked in library: ML Engineer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
CI/CD Pipeline Platforms
ci-cd-pipeline-platforms
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
CI/CD for Machine Learning
ci-cd-for-machine-learning
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
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
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
Aliases — catalog
- Dimensional modeling (CANONICAL) primary
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Concept
- Sub-category
- Data Modeling Concept
- Confidence
- 0.97
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Common in analytics/data-warehouse JDs and BI roles; star/snowflake schema terms appear frequently in job postings and vendor docs for Snowflake/BigQuery/Redshift.
Skill profile (library / DB)
- Skill nature
- CONCEPT
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 2
- Sub-category id
- 10
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
Data Modeling and Schema Design Catalog dimension db id 26
Library dimension (catalog)
Roles linked in library: Data Engineer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Data Modeling and Schema Design
data-modeling-and-schema-design
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved |
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Data Management
- 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
- Data Engineering Tools
- 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
- Data Management
- Sub-category
- general
- Skill nature
- CONCEPT
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Version strategy
- UNVERSIONED
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Data Management
- Sub-category
- general
- Skill nature
- CONCEPT
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Version strategy
- UNVERSIONED
Aliases — catalog
- SQL Server (CANONICAL) primary
- SQL Server 2000 (VERSION)
- SQL Server 2005 (VERSION)
- SQL Server 2008 (VERSION)
- SQL Server 2012 (VERSION)
- SQL Server 2014 (VERSION)
- SQL Server 2016 (VERSION)
- SQL Server 2017 (VERSION)
- SQL Server 2019 (VERSION)
- SQL Server 2022 (VERSION)
- SQL Server 6.5 (VERSION)
- SQL Server 7.0 (VERSION)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Datastore
- Sub-category
- Relational Database
- Vendor
- Microsoft
- License
- proprietary
- Year introduced
- 1989
- Confidence
- 0.99
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: SQL Server appears in many enterprise job descriptions and remains a major Microsoft-supported RDBMS with active Azure SQL/SQL Server demand; it is a common hiring-pipeline staple, not a sunset technology.
Skill profile (library / DB)
- Skill nature
- TOOL
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 3
- Sub-category id
- 29
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
Relational Database Design Catalog dimension db id 4
Library dimension (catalog)
Roles linked in library: .NET Backend Developer, Backend Developer, Kotlin Backend Developer, Node.js Backend Developer, Python Backend Developer, Ruby Backend Developer, Scala Backend Developer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Relational Database Design
relational-database-design
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Programming Languages
- Sub-category
- general
- Skill nature
- LANGUAGE
- 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
- Database Concepts
- Sub-category
- general
- Skill nature
- CONCEPT
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Aliases — catalog
- root cause analysis (CANONICAL) primary
- root-cause analysis (CANONICAL)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Methodology
- Sub-category
- Root Cause Analysis Methodology
- Confidence
- 0.96
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Root cause analysis is a standard incident/postmortem skill in SRE, ITIL, and quality roles; it appears broadly in job descriptions for operations, QA, and manufacturing, not as a niche tool.
Skill profile (library / DB)
- Skill nature
- METHODOLOGY
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 8
- Sub-category id
- 3301
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
Observability and Diagnostics Catalog dimension db id 287
Library dimension (catalog)
Roles linked in library: Go Backend Developer, Java Backend Developer, Python Backend Developer
-
Site Troubleshooting and Debugging Catalog dimension db id 353
Library dimension (catalog)
Roles linked in library: Drupal Dev
-
Sitecore Troubleshooting and Maintenance Catalog dimension db id 447
Library dimension (catalog)
Roles linked in library: Sitecore Dev
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Observability and Diagnostics
observability-and-diagnostics
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Site Troubleshooting and Debugging
site-troubleshooting-and-debugging
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Sitecore Troubleshooting and Maintenance
sitecore-troubleshooting-and-maintenance
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Aliases — catalog
- Relational Databases (CANONICAL)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Domain
- Sub-category
- Relational Database Management
- Confidence
- 0.90
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Relational databases remain a hiring staple across most backend/data JDs, with PostgreSQL, MySQL, and SQL Server appearing routinely; cloud vendors also center managed RDBMS offerings, signaling broad adoption.
Skill profile (library / DB)
- Skill nature
- CONCEPT
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 37
- Sub-category id
- 1018
- 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
- Integration 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
- PRACTICE
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Aliases — catalog
- Agile (CANONICAL) primary
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Methodology
- Sub-category
- Agile
- Confidence
- 0.99
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Agile appears in a large share of software job descriptions and is a standard hiring-pipeline requirement; Scrum/Kanban are commonly listed alongside it, showing broad market adoption.
Skill profile (library / DB)
- Skill nature
- METHODOLOGY
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 8
- Sub-category id
- 3594
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
React Frontend Development Catalog dimension db id 96
Library dimension (catalog)
-
Software Concepts, Patterns & Practices Catalog dimension db id 478
Library dimension (catalog)
Roles linked in library: Engineering Manager
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) |
|
Software Concepts, Patterns & Practices
software-concepts-patterns-practices
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
All API 3 persistence rows
Same grid as the skill-extractor “Persistence items” table: one row per (skill × dimension) work item.
| Skill | Tag | Dimension | Skill↔dim | Role↔dim | Outcome | Notes |
|---|---|---|---|---|---|---|
| PySpark | new |
ETL and ELT Tooling
etl-and-elt-tooling
|
— | — | Skipped — no persistable v3 meta for new skill | skill_not_in_db_v3_proposed |
| 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 & DSLs
programming-languages-dsls
|
✓ | — | 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) | |
| Microsoft Azure | in_db |
Cloud & Hosting Providers
cloud-hosting-providers
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Microsoft Azure | in_db |
Cloud Platforms
cloud-platforms
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved | |
| APIs | in_db |
React Frontend Development
d_init_01
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Data Lake | new |
Cloud Storage and Data Services
cloud-storage-and-data-services
|
— | — | Skipped — no persistable v3 meta for new skill | skill_not_in_db_v3_proposed |
| Data Lake | new |
React Frontend Development
d_init_01
|
— | — | Skipped — no persistable v3 meta for new skill | skill_not_in_db_v3_proposed |
| SQL | in_db |
Pega Programming Languages & DSLs
pega-programming-languages-dsls
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| SQL | in_db |
Programming Languages & DSLs
programming-languages-dsls
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| SQL | in_db |
Programming Languages for Data Work
programming-languages-for-data-work
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved | |
| NoSQL | in_db |
NoSQL Databases
nosql-databases
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Design Patterns | in_db |
React Frontend Development
d_init_01
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| CI/CD | in_db |
CI/CD Pipeline Platforms
ci-cd-pipeline-platforms
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| CI/CD | in_db |
CI/CD for Machine Learning
ci-cd-for-machine-learning
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Dimensional Modeling | in_db |
Data Modeling and Schema Design
data-modeling-and-schema-design
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved | |
| SQL Server | in_db |
Relational Database Design
relational-database-design
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Root Cause Analysis | in_db |
Observability and Diagnostics
observability-and-diagnostics
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Root Cause Analysis | in_db |
Site Troubleshooting and Debugging
site-troubleshooting-and-debugging
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Root Cause Analysis | in_db |
Sitecore Troubleshooting and Maintenance
sitecore-troubleshooting-and-maintenance
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Relational Databases | in_db |
React Frontend Development
d_init_01
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Agile | in_db |
React Frontend Development
d_init_01
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Agile | in_db |
Software Concepts, Patterns & Practices
software-concepts-patterns-practices
|
✓ | — | 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=general nature=PLATFORM lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Synapse | type=Cloud Platforms subtype=general nature=PLATFORM lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Azure Data Factory | type=Cloud Platforms subtype=general nature=PLATFORM lifespan=MULTI_YEAR | |
| canonical_skill_proposed | ETL | type=Data Engineering Tools subtype=general nature=PRACTICE lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Code Refactoring | type=Software Development subtype=general nature=PRACTICE lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Batch ETL | type=Data Engineering Tools subtype=general nature=PRACTICE lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Streaming Data Pipelines | type=Data Engineering Tools subtype=general nature=PRACTICE lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Data Governance | type=Data Management subtype=general nature=CONCEPT lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Data Warehousing | type=Data Engineering Tools subtype=general nature=CONCEPT lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Structured Data | type=Data Management subtype=general nature=CONCEPT lifespan=EVERGREEN | |
| canonical_skill_proposed | Unstructured Data | type=Data Management subtype=general nature=CONCEPT lifespan=EVERGREEN | |
| canonical_skill_proposed | T-SQL | type=Programming Languages subtype=general nature=LANGUAGE lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Stored Procedures | type=Database Concepts subtype=general nature=CONCEPT lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Boomi | type=Integration Tools subtype=general nature=TOOL lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Data Ingestion | type=Data Engineering Tools subtype=general nature=PRACTICE lifespan=MULTI_YEAR | |
| dimension_skill_link_proposed | PySpark ↔ ETL and ELT Tooling | |
| role_dimension_link_proposed | Data Engineer ↔ ETL and ELT Tooling | |
| dimension_skill_link_proposed | Data Lake ↔ Cloud Storage and Data Services | |
| dimension_skill_link_proposed | Data Lake ↔ React Frontend Development |
nano JD Parser — gpt-4.1-nano click to toggle
Show raw JSON
{
"JD_type": "pass",
"about_company": {
"source_marker": {
"first_5_words": "As the Global Leader in",
"last_5_words": "retail space."
},
"text": "As the Global Leader in Rental Housing, Greystar provides end-to-end property management services for residential housing, apartment homes, furnished corporate housing, and mixed-use properties incorporating retail space.",
"word_count": 32
},
"certifications": [],
"company_name": "Greystar",
"ctc": null,
"domain": {
"primary": {
"aliases": [
"Information Technology",
"Tech Services"
],
"domain": "IT Services \u0026 Consulting"
},
"secondary": null
},
"education": [
{
"level": "Bachelor\u0027s",
"qualification": "Bachelor\u0027s - Computer Science / Information Technology / Business Management Information Systems (or equivalent)",
"raw": "Bachelor\u2019s degree in computer science, information technology, business management information systems, or equivalent experience.",
"requirement": "required"
}
],
"experience": {
"max": null,
"min": 7,
"raw": "7+ years relevant and progressive data engineering experience"
},
"job_locations": [],
"role": "Senior Data Engineer",
"role_aliases": [
"Data Engineer",
"Senior Data Engineer",
"Data Engineering Specialist"
],
"role_archetype": "Data",
"roles_and_responsibilities": [
{
"bullet_count": 9,
"heading": "How you will make in impact",
"heading_was_present": true,
"source_marker": {
"first_5_words": "\u2022 Design, develop, optimize, and",
"last_5_words": "Deliver awesome code"
},
"text": "\u2022 Design, develop, optimize, and maintain data architecture and pipelines that adhere to ETL principles and business goals\n\u2022 Collaborate with data engineers, data consumers, and other team members to come up with simple, functional, and elegant solutions that balance the data needs across the organization\n\u2022 Solve complex data problems to deliver insights that helps the organization achieve its goals\n\u2022 Create data products that will be used throughout the organization\n\u2022 Advise, consult, mentor and coach other data and analytic professionals on data standards and practices\n\u2022 Foster a culture of sharing, re-use, design for scale stability, and operational efficiency of data and analytic solutions\n\u2022 Develop and deliver documentation on data engineering capabilities, standards, and processes; participate in coaching, mentoring, design reviews and code reviews\n\u2022 Partner with business analysts and solutions architects to develop technical architectures for strategic enterprise projects and initiatives.\n\u2022 Deliver awesome code",
"word_count": 139
},
{
"bullet_count": 10,
"heading": "Technical Qualifications",
"heading_was_present": true,
"source_marker": {
"first_5_words": "\u2022 7+ years relevant and progressive",
"last_5_words": "is a plus"
},
"text": "\u2022 7+ years relevant and progressive data engineering experience\n\u2022 In-depth technical expertise with Azure Databricks (PySpark and Python), Microsoft Azure architecture, including Synapse, ADF (Azure Data Factory) pipelines\n\u2022 Extensive hands-on experience with data pipelines, handling various source and target locations such as APIs, Data Lakes, file-based formats, SQL, and No-SQL databases\n\u2022 Experience in engineering practices such as development, code refactoring, and leveraging design patterns, CI/CD, and building highly scalable data applications and processes\n\u2022 Experience developing batch ETL pipelines; streaming data pipelines are a plus\n\u2022 Knowledge of advanced data engineering concepts such as dimensional modeling, ETL, data governance, data warehousing involving structured and unstructured data\n\u2022 Thorough knowledge of Synapse and SQL Server including T-SQL and stored procedures\n\u2022 Experience performing root cause analysis on internal and external data and processes to answer specific business questions and identify opportunities for improvement.\n\u2022 Advanced working SQL knowledge and experience working with relational databases, query authoring (SQL) as well as working familiarity with a variety of databases.\n\u2022 Knowledge and understanding of Boomi is a plus",
"word_count": 202
},
{
"bullet_count": 10,
"heading": "Additional Qualifications and Experience",
"heading_was_present": true,
"source_marker": {
"first_5_words": "\u2022 Excellent problem-solving skills and",
"last_5_words": "in a dynamic environment"
},
"text": "\u2022 Excellent problem-solving skills and experience\n\u2022 Effective communication skills\n\u2022 Strong collaboration skills\n\u2022 \"Self-starter\" attitude and the ability to make decisions with minimal guidance from others\n\u2022 Innovative and passionate about your work and the work of your teammates\n\u2022 Ability to comprehend and analyze operational systems and ask appropriate questions to determine how to improve, migrate or modify the solution to meet business needs\n\u2022 Experience with data ingestion and engineering, specifically involving large data volumes\n\u2022 Knowledge of CI/CD release pipelines is a plus\n\u2022 Knowledge of the Agile development process\n\u2022 Experience working with and supporting cross-functional teams in a dynamic environment",
"word_count": 104
}
],
"urls": [
{
"type": "careers",
"url": "https://tlv.sh/bCDV"
}
]
}
API 1 — extract-from-jd click to toggle
{
"final_skills": [
{
"is_primary": true,
"skill_name": "Azure Databricks"
},
{
"is_primary": true,
"skill_name": "PySpark"
},
{
"is_primary": true,
"skill_name": "Python"
},
{
"is_primary": true,
"skill_name": "Microsoft Azure"
},
{
"is_primary": true,
"skill_name": "Synapse"
},
{
"is_primary": true,
"skill_name": "Azure Data Factory"
},
{
"is_primary": true,
"skill_name": "ETL"
},
{
"is_primary": true,
"skill_name": "APIs"
},
{
"is_primary": true,
"skill_name": "Data Lake"
},
{
"is_primary": true,
"skill_name": "SQL"
},
{
"is_primary": true,
"skill_name": "NoSQL"
},
{
"is_primary": true,
"skill_name": "Code Refactoring"
},
{
"is_primary": true,
"skill_name": "Design Patterns"
},
{
"is_primary": true,
"skill_name": "CI/CD"
},
{
"is_primary": true,
"skill_name": "Batch ETL"
},
{
"is_primary": false,
"skill_name": "Streaming Data Pipelines"
},
{
"is_primary": true,
"skill_name": "Dimensional Modeling"
},
{
"is_primary": true,
"skill_name": "Data Governance"
},
{
"is_primary": true,
"skill_name": "Data Warehousing"
},
{
"is_primary": true,
"skill_name": "Structured Data"
},
{
"is_primary": true,
"skill_name": "Unstructured Data"
},
{
"is_primary": true,
"skill_name": "SQL Server"
},
{
"is_primary": true,
"skill_name": "T-SQL"
},
{
"is_primary": true,
"skill_name": "Stored Procedures"
},
{
"is_primary": true,
"skill_name": "Root Cause Analysis"
},
{
"is_primary": true,
"skill_name": "Relational Databases"
},
{
"is_primary": false,
"skill_name": "Boomi"
},
{
"is_primary": true,
"skill_name": "Data Ingestion"
},
{
"is_primary": false,
"skill_name": "Agile"
}
],
"jd_role": {
"display_name": "Senior Data Engineer",
"rationale": null,
"role_aliases": [
"Data Engineer",
"Senior Data Engineer",
"Data Engineering Specialist"
],
"role_archetype": "Data",
"slug": ""
},
"nano_parsed": {
"JD_type": "pass",
"about_company": {
"source_marker": {
"first_5_words": "As the Global Leader in",
"last_5_words": "retail space."
},
"text": "As the Global Leader in Rental Housing, Greystar provides end-to-end property management services for residential housing, apartment homes, furnished corporate housing, and mixed-use properties incorporating retail space.",
"word_count": 32
},
"certifications": [],
"company_name": "Greystar",
"ctc": null,
"domain": {
"primary": {
"aliases": [
"Information Technology",
"Tech Services"
],
"domain": "IT Services \u0026 Consulting"
},
"secondary": null
},
"education": [
{
"level": "Bachelor\u0027s",
"qualification": "Bachelor\u0027s - Computer Science / Information Technology / Business Management Information Systems (or equivalent)",
"raw": "Bachelor\u2019s degree in computer science, information technology, business management information systems, or equivalent experience.",
"requirement": "required"
}
],
"experience": {
"max": null,
"min": 7,
"raw": "7+ years relevant and progressive data engineering experience"
},
"job_locations": [],
"role": "Senior Data Engineer",
"role_aliases": [
"Data Engineer",
"Senior Data Engineer",
"Data Engineering Specialist"
],
"role_archetype": "Data",
"roles_and_responsibilities": [
{
"bullet_count": 9,
"heading": "How you will make in impact",
"heading_was_present": true,
"source_marker": {
"first_5_words": "\u2022 Design, develop, optimize, and",
"last_5_words": "Deliver awesome code"
},
"text": "\u2022 Design, develop, optimize, and maintain data architecture and pipelines that adhere to ETL principles and business goals\n\u2022 Collaborate with data engineers, data consumers, and other team members to come up with simple, functional, and elegant solutions that balance the data needs across the organization\n\u2022 Solve complex data problems to deliver insights that helps the organization achieve its goals\n\u2022 Create data products that will be used throughout the organization\n\u2022 Advise, consult, mentor and coach other data and analytic professionals on data standards and practices\n\u2022 Foster a culture of sharing, re-use, design for scale stability, and operational efficiency of data and analytic solutions\n\u2022 Develop and deliver documentation on data engineering capabilities, standards, and processes; participate in coaching, mentoring, design reviews and code reviews\n\u2022 Partner with business analysts and solutions architects to develop technical architectures for strategic enterprise projects and initiatives.\n\u2022 Deliver awesome code",
"word_count": 139
},
{
"bullet_count": 10,
"heading": "Technical Qualifications",
"heading_was_present": true,
"source_marker": {
"first_5_words": "\u2022 7+ years relevant and progressive",
"last_5_words": "is a plus"
},
"text": "\u2022 7+ years relevant and progressive data engineering experience\n\u2022 In-depth technical expertise with Azure Databricks (PySpark and Python), Microsoft Azure architecture, including Synapse, ADF (Azure Data Factory) pipelines\n\u2022 Extensive hands-on experience with data pipelines, handling various source and target locations such as APIs, Data Lakes, file-based formats, SQL, and No-SQL databases\n\u2022 Experience in engineering practices such as development, code refactoring, and leveraging design patterns, CI/CD, and building highly scalable data applications and processes\n\u2022 Experience developing batch ETL pipelines; streaming data pipelines are a plus\n\u2022 Knowledge of advanced data engineering concepts such as dimensional modeling, ETL, data governance, data warehousing involving structured and unstructured data\n\u2022 Thorough knowledge of Synapse and SQL Server including T-SQL and stored procedures\n\u2022 Experience performing root cause analysis on internal and external data and processes to answer specific business questions and identify opportunities for improvement.\n\u2022 Advanced working SQL knowledge and experience working with relational databases, query authoring (SQL) as well as working familiarity with a variety of databases.\n\u2022 Knowledge and understanding of Boomi is a plus",
"word_count": 202
},
{
"bullet_count": 10,
"heading": "Additional Qualifications and Experience",
"heading_was_present": true,
"source_marker": {
"first_5_words": "\u2022 Excellent problem-solving skills and",
"last_5_words": "in a dynamic environment"
},
"text": "\u2022 Excellent problem-solving skills and experience\n\u2022 Effective communication skills\n\u2022 Strong collaboration skills\n\u2022 \"Self-starter\" attitude and the ability to make decisions with minimal guidance from others\n\u2022 Innovative and passionate about your work and the work of your teammates\n\u2022 Ability to comprehend and analyze operational systems and ask appropriate questions to determine how to improve, migrate or modify the solution to meet business needs\n\u2022 Experience with data ingestion and engineering, specifically involving large data volumes\n\u2022 Knowledge of CI/CD release pipelines is a plus\n\u2022 Knowledge of the Agile development process\n\u2022 Experience working with and supporting cross-functional teams in a dynamic environment",
"word_count": 104
}
],
"urls": [
{
"type": "careers",
"url": "https://tlv.sh/bCDV"
}
]
},
"rejected": false,
"rejection_reason": null,
"run_id": "ba3c40e4-41c7-4c21-9f0b-9479914520a7",
"stage3_signals": {
"alias_found": true,
"alias_match_roles": [
{
"display_name": "Data Engineer",
"kra_matches": null,
"matched_count": null,
"matched_skills": null,
"role_id": 2,
"score": 1.0,
"slug": "data-engineer",
"total_count": null
}
],
"kra_match_roles": [
{
"display_name": "Data Engineer",
"kra_matches": [
{
"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.",
"sentence": "Experience developing batch ETL pipelines; streaming data pipelines are a plus",
"similarity": 0.6497
},
{
"kra_text": "Works with data analysts, data scientists, and business stakeholders to define data models, ingestion schedules, and data delivery requirements.",
"sentence": "Collaborate with data engineers, data consumers, and other team members to come up with simple, functional, and elegant solutions that balance the data needs across the organization",
"similarity": 0.6317
},
{
"kra_text": "Works with data analysts, data scientists, and business stakeholders to define data models, ingestion schedules, and data delivery requirements.",
"sentence": "Design, develop, optimize, and maintain data architecture and pipelines that adhere to ETL principles and business goals",
"similarity": 0.6267
}
],
"matched_count": null,
"matched_skills": null,
"role_id": 2,
"score": 0.636,
"slug": "data-engineer",
"total_count": null
},
{
"display_name": "Cloud Architect",
"kra_matches": [
{
"kra_text": "Conducts architecture reviews, approves technical design documents, and guides engineering teams through cloud migration and modernization projects.",
"sentence": "Partner with business analysts and solutions architects to develop technical architectures for strategic enterprise projects and initiatives.",
"similarity": 0.581
},
{
"kra_text": "Conducts architecture reviews, approves technical design documents, and guides engineering teams through cloud migration and modernization projects.",
"sentence": "Develop and deliver documentation on data engineering capabilities, standards, and processes; participate in coaching, mentoring, design reviews and code reviews",
"similarity": 0.5482
},
{
"kra_text": "Conducts architecture reviews, approves technical design documents, and guides engineering teams through cloud migration and modernization projects.",
"sentence": "Design, develop, optimize, and maintain data architecture and pipelines that adhere to ETL principles and business goals",
"similarity": 0.4788
}
],
"matched_count": null,
"matched_skills": null,
"role_id": 9,
"score": 0.536,
"slug": "cloud-architect",
"total_count": null
},
{
"display_name": "Flutter Developer",
"kra_matches": [
{
"kra_text": "collaborate with design, product, and backend teams",
"sentence": "Collaborate with data engineers, data consumers, and other team members to come up with simple, functional, and elegant solutions that balance the data needs across the organization",
"similarity": 0.5775
},
{
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{
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{
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},
{
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{
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{
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"case": "A",
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"is_new_role": false,
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"matched_kras": [],
"matched_skills": [],
"new_role_display_name": null,
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"reasoning": "Exact alias hit on data-engineer (1.0) \u2014 no other alias at this confidence; skill_top python-backend-developer 0.15 does not contradict",
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},
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"centroid_updated": true,
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"new_kra_attached": null,
"new_skills_attached": [
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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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"role_display_name": "Data Engineer",
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},
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},
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},
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},
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}
}
API 2 — extract-details
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"matched_canonical": {
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"is_extractable": true,
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"matched_via": "embedding_alias"
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"matched_via": "alias"
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},
{
"alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
"alias_persisted": false,
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"matched_via": "alias"
},
{
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"alias_persisted": false,
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},
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},
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{
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"input_skill": "Boomi",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": {
"derived": {
"category": "Integration Tools",
"skill_nature": "TOOL",
"sub_category": "general",
"typical_lifespan": "MULTI_YEAR",
"version_strategy": "UNVERSIONED",
"volatility": "MEDIUM"
},
"enrichment": null,
"keep_log": [],
"locked_dimensions": [],
"merge_log": [],
"placed": null,
"relationships": null,
"skill_id": "boomi",
"split_log": [],
"typed": null,
"warnings": []
},
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [],
"input_skill": "Data Ingestion",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": {
"derived": {
"category": "Data Engineering Tools",
"skill_nature": "PRACTICE",
"sub_category": "general",
"typical_lifespan": "MULTI_YEAR",
"version_strategy": "UNVERSIONED",
"volatility": "MEDIUM"
},
"enrichment": null,
"keep_log": [],
"locked_dimensions": [],
"merge_log": [],
"placed": null,
"relationships": null,
"skill_id": "data-ingestion",
"split_log": [],
"typed": null,
"warnings": []
},
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [
{
"alias_text": "Agile",
"alias_type": "CANONICAL",
"id": 868,
"is_primary": true,
"match_strategy": "CASE_INSENSITIVE"
}
],
"canonical": {
"category_id": 8,
"display_name": "Agile",
"id": 520,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "METHODOLOGY",
"slug": "agile",
"sub_category_id": 3594,
"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"dimensions": [
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "React Frontend Development",
"id": 96,
"rationale": "Building interactive web user interfaces with React.js, including component composition, state management, hooks, and rendering patterns. React.js belongs here because it is a core library for client-side UI development in modern web applications.",
"slug": "d_init_01",
"source": "db"
},
"input_skill": "Agile",
"llm_role": null,
"roles_from_db": []
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Software Concepts, Patterns \u0026 Practices",
"id": 478,
"rationale": "Champion foundational software design patterns, development methodologies, and engineering best practices.",
"slug": "software-concepts-patterns-practices",
"source": "db"
},
"input_skill": "Agile",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Engineering Manager",
"id": 121,
"rationale": null,
"role_archetype": null,
"slug": "engineering-manager",
"source": "db"
}
]
}
],
"input_skill": "Agile",
"matched_via": "alias",
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": null,
"source_tag": "db",
"was_in_llm_skills": true
}
],
"unmatched_skills": [
"Azure Databricks",
"Synapse",
"Azure Data Factory",
"ETL",
"Code Refactoring",
"Batch ETL",
"Streaming Data Pipelines",
"Data Governance",
"Data Warehousing",
"Structured Data",
"Unstructured Data",
"T-SQL",
"Stored Procedures",
"Boomi",
"Data Ingestion"
]
}
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 python-backend-developer 0.15 does not contradict",
"role_archetype": null,
"slug": "data-engineer",
"source": "db"
},
"chosen_role_resolution": "in_db",
"final_input_skills": [
{
"skill": "Azure Databricks",
"tag": "new"
},
{
"skill": "PySpark",
"tag": "in_db"
},
{
"skill": "Python",
"tag": "in_db"
},
{
"skill": "Microsoft Azure",
"tag": "in_db"
},
{
"skill": "Synapse",
"tag": "new"
},
{
"skill": "Azure Data Factory",
"tag": "new"
},
{
"skill": "ETL",
"tag": "new"
},
{
"skill": "APIs",
"tag": "in_db"
},
{
"skill": "Data Lake",
"tag": "in_db"
},
{
"skill": "SQL",
"tag": "in_db"
},
{
"skill": "NoSQL",
"tag": "in_db"
},
{
"skill": "Code Refactoring",
"tag": "new"
},
{
"skill": "Design Patterns",
"tag": "in_db"
},
{
"skill": "CI/CD",
"tag": "in_db"
},
{
"skill": "Batch ETL",
"tag": "new"
},
{
"skill": "Streaming Data Pipelines",
"tag": "new"
},
{
"skill": "Dimensional Modeling",
"tag": "in_db"
},
{
"skill": "Data Governance",
"tag": "new"
},
{
"skill": "Data Warehousing",
"tag": "new"
},
{
"skill": "Structured Data",
"tag": "new"
},
{
"skill": "Unstructured Data",
"tag": "new"
},
{
"skill": "SQL Server",
"tag": "in_db"
},
{
"skill": "T-SQL",
"tag": "new"
},
{
"skill": "Stored Procedures",
"tag": "new"
},
{
"skill": "Root Cause Analysis",
"tag": "in_db"
},
{
"skill": "Relational Databases",
"tag": "in_db"
},
{
"skill": "Boomi",
"tag": "new"
},
{
"skill": "Data Ingestion",
"tag": "new"
},
{
"skill": "Agile",
"tag": "in_db"
}
],
"llm_cost_api1_usd": null,
"llm_cost_api2_usd": null,
"llm_cost_api3_usd": null,
"llm_cost_total_usd": null,
"persistence": {
"items": [
{
"chosen_role_id": 2,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "ETL and ELT Tooling",
"id": 24,
"rationale": "Packaged tools for extracting, loading, and transforming data across systems. This dimension covers connector-based ingestion, transformation frameworks, and managed integration products.",
"slug": "etl-and-elt-tooling",
"source": "db"
},
"dimension_id": 24,
"input_skill": "PySpark",
"llm_role": null,
"matched_chosen_role": true,
"outcome_line": "Skipped \u2014 no persistable v3 meta for new skill",
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "Data Engineer",
"id": 2,
"rationale": null,
"role_archetype": null,
"slug": "data-engineer",
"source": "db"
}
],
"skill_dimension_saved": false,
"skill_id": null,
"skill_tag": "new",
"skipped_reason": "skill_not_in_db_v3_proposed"
},
{
"chosen_role_id": 2,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "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 \u0026 DSLs",
"id": 475,
"rationale": "Oversee and guide the selection and effective use of programming and domain\u2010specific languages in software projects.",
"slug": "programming-languages-dsls",
"source": "db"
},
"dimension_id": 475,
"input_skill": "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": "Engineering Manager",
"id": 121,
"rationale": null,
"role_archetype": null,
"slug": "engineering-manager",
"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 \u0026 Hosting Providers",
"id": 414,
"rationale": "Knowledge of major cloud and hosting vendor platforms for deploying and managing PHP applications.",
"slug": "cloud-hosting-providers",
"source": "db"
},
"dimension_id": 414,
"input_skill": "Microsoft Azure",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "PHP Backend Developer",
"id": 86,
"rationale": null,
"role_archetype": "Engineering",
"slug": "php-backend-developer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 97,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 2,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Cloud Platforms",
"id": 20,
"rationale": "Underlying cloud providers that host the managed services or infrastructure used by the role, such as AWS, Azure, and GCP.",
"slug": "cloud-platforms",
"source": "db"
},
"dimension_id": 20,
"input_skill": "Microsoft Azure",
"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": ".NET Backend Developer",
"id": 83,
"rationale": null,
"role_archetype": "Engineering",
"slug": "dotnet-backend-developer",
"source": "db"
},
{
"display_name": "Backend Developer",
"id": 1,
"rationale": null,
"role_archetype": "A Backend Engineer designs, builds, and maintains the server-side logic and data handling that power applications and services. They focus on implementing reliable business functionality, integrating with other systems, and ensuring the backend is scalable, maintainable, and observable.",
"slug": "backend-engineer",
"source": "db"
},
{
"display_name": "Cyber Security Engineer",
"id": 5,
"rationale": null,
"role_archetype": null,
"slug": "cybersecurity-engineer",
"source": "db"
},
{
"display_name": "Data Engineer",
"id": 2,
"rationale": null,
"role_archetype": null,
"slug": "data-engineer",
"source": "db"
},
{
"display_name": "DevOps Engineer",
"id": 10,
"rationale": null,
"role_archetype": null,
"slug": "devops-engineer",
"source": "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": "Java Backend Developer",
"id": 79,
"rationale": null,
"role_archetype": "Engineering",
"slug": "java-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": "ML Engineer",
"id": 3,
"rationale": null,
"role_archetype": null,
"slug": "ml-engineer",
"source": "db"
},
{
"display_name": "MLOps Engineer",
"id": 16,
"rationale": null,
"role_archetype": null,
"slug": "ml-ops-engineer",
"source": "db"
},
{
"display_name": "Node.js Backend Developer",
"id": 82,
"rationale": null,
"role_archetype": "Engineering",
"slug": "node-backend-developer",
"source": "db"
},
{
"display_name": "Python Backend Developer",
"id": 80,
"rationale": null,
"role_archetype": "Engineering",
"slug": "python-backend-developer",
"source": "db"
},
{
"display_name": "Scala Backend Developer",
"id": 87,
"rationale": null,
"role_archetype": "Engineering",
"slug": "scala-backend-developer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 97,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 2,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "React Frontend Development",
"id": 96,
"rationale": "Building interactive web user interfaces with React.js, including component composition, state management, hooks, and rendering patterns. React.js belongs here because it is a core library for client-side UI development in modern web applications.",
"slug": "d_init_01",
"source": "db"
},
"dimension_id": 96,
"input_skill": "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
},
{
"chosen_role_id": 2,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Cloud Storage and Data Services",
"id": 144,
"rationale": "Cloud-native storage and managed data services used to place workloads, choose durability tiers, and define platform boundaries. This is a coherent cluster because architects evaluate storage fit, access patterns, and managed service tradeoffs.",
"slug": "cloud-storage-and-data-services",
"source": "db"
},
"dimension_id": 144,
"input_skill": "Data Lake",
"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": "Cloud Architect",
"id": 9,
"rationale": null,
"role_archetype": null,
"slug": "cloud-architect",
"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": "Data Lake",
"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": [],
"skill_dimension_saved": false,
"skill_id": null,
"skill_tag": "new",
"skipped_reason": "skill_not_in_db_v3_proposed"
},
{
"chosen_role_id": 2,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Pega Programming Languages \u0026 DSLs",
"id": 267,
"rationale": "Programming languages and domain-specific languages used in Pega development.",
"slug": "pega-programming-languages-dsls",
"source": "db"
},
"dimension_id": 267,
"input_skill": "SQL",
"llm_role": null,
"matched_chosen_role": false,
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