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Pipeline run

ba3c40e4-41c7-4c21-9f0b-9479914520a7

Pipeline LLM cost (USD)
API 1: $0.0049 API 2: $0.0006 API 3: $0.0000 Total: $0.0055

Client output enrichment

v2 Skill cluster · Nature of work · AI index · Tech stack maturity · Evidence · KRA description
role baseline loaded sources · ai_index: jd · nature_of_work: jd · tech_stack_maturity: jd
Nature of work · Data pipeline development
Build and maintain Azure-based ETL/data pipelines in Databricks, Synapse, and ADF; design scalable data products and models, troubleshoot data issues with SQL/RCA, and document/coach teams on standards, code reviews, and architecture.
"Design, develop, optimize, and maintain data architecture and pipelines that adhere to ETL principles and business goals"
Tech stack maturity
Mainstream Modern
The stack centers on widely used cloud and data-engineering technologies like Azure, Python, SQL Server, APIs, and CI/CD, which are characteristic of mainstream modern engineering rather than bleeding-edge or legacy-only environments.
AI index (0 = no AI use, 5 = totally AI-dependent · v2.1)
0.00 / 5
· Title match
· Has AI skill
· AI skill (primary)
· AI skill (secondary)
· On AI team
· Builds AI products
vocab breakdown (legacy)
Assistants (×1):
Frameworks (×2):
Models / concepts (×3):
Evidence — skills matched in JD (29)
Azure Databricks PySpark Python Microsoft Azure Synapse Azure Data Factory ETL APIs Data Lake SQL NoSQL Code Refactoring Design Patterns CI/CD Batch ETL Dimensional Modeling Data Governance Data Warehousing Structured Data Unstructured Data SQL Server T-SQL Stored Procedures Root Cause Analysis Relational Databases +4
Skill cluster (5 dimension groups, role-scoped)
Programming Languages for Data Work
Python SQL
Cloud Platforms
Microsoft Azure
Data Modeling and Schema Design
Dimensional Modeling
Site Troubleshooting and Debugging
Root Cause Analysis
Cross-cutting / unaligned
Azure Databricks PySpark Synapse Azure Data Factory ETL APIs Data Lake NoSQL Code Refactoring Design Patterns CI/CD Batch ETL Data Governance Data Warehousing Structured Data Unstructured Data SQL Server T-SQL Stored Procedures Relational Databases Data Ingestion Streaming Data Pipelines Boomi Agile
Show KRA description ↓
• 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 • 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 • 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

Signals

Skill python-backend-developer
0.15
Alias data-engineer
1.00
KRA data-engineer
0.64

Post-classification

Centroidupdated · n=485
Alias collision log
New-role queue
New skills captured17
New KRA captured

Captured for admin review

Azure Databricks primary Data Engineer pending
PySpark primary Data Engineer pending
Synapse primary Data Engineer pending
Azure Data Factory primary Data Engineer pending
ETL primary Data Engineer pending
Data Lake primary Data Engineer pending
Code Refactoring primary Data Engineer pending
Batch ETL primary Data Engineer pending
Streaming Data Pipelines Data Engineer pending
Data Governance primary Data Engineer pending
Data Warehousing primary Data Engineer pending
Structured Data primary Data Engineer pending
Unstructured Data primary Data Engineer pending
T-SQL primary Data Engineer pending
Stored Procedures primary Data Engineer pending
Boomi Data Engineer pending
Data Ingestion primary Data Engineer pending
Status: completed Created: 2026-05-27T16:56:48.352080Z Updated: 2026-05-27T16:58:42.648138Z API 3 duration: 16000 ms
Flow Current 3-step pipeline

1 POST /skills/extract-from-jd

2 POST /skills/extract-details

3 POST /skills/final-role-output

Role Chosen role & resolution

Data Engineer

CASE A

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

Exact alias hit on data-engineer (1.0) — no other alias at this confidence; skill_top python-backend-developer 0.15 does not contradict

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

0
New skills
0
Skill↔dim saved
0
Role↔dim saved
3
Skipped

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.

Azure Databricks Primary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

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

Derived legacy fields
Category
Cloud Platforms
Sub-category
general
Skill nature
PLATFORM
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
PySpark Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Apache Spark id=1350 · apache-spark

Aliases — catalog

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

Context tags (catalog)

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

Stored enrichment (catalog DB)

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

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

Skill profile (library / DB)

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

Dimensions (API 2 worklist)

  • ETL and ELT Tooling Catalog dimension db id 24

    Library dimension (catalog)

    Roles linked in library: Data Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
ETL and ELT Tooling
etl-and-elt-tooling
Skipped — no persistable v3 meta for new skill
skill_not_in_db_v3_proposed
Python Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Python id=5 · python

Aliases — catalog

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

Context tags (catalog)

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

Stored enrichment (catalog DB)

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

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

Skill profile (library / DB)

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

Dimensions (API 2 worklist)

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

    Library dimension (catalog)

    Roles linked in library: Cloud Security Engineer

  • Programming Languages Catalog dimension db id 1

    Library dimension (catalog)

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

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

Aliases — catalog

  • Microsoft Azure (CANONICAL) primary

Context tags (catalog)

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

Stored enrichment (catalog DB)

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

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

Skill profile (library / DB)

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

Dimensions (API 2 worklist)

  • Cloud & Hosting Providers Catalog dimension db id 414

    Library dimension (catalog)

    Roles linked in library: PHP Backend Developer

  • Cloud Platforms Catalog dimension db id 20

    Library dimension (catalog)

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

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Cloud & Hosting Providers
cloud-hosting-providers
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Cloud Platforms
cloud-platforms
Existing dimension (library) · Role↔dimension saved
Synapse Primary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

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

Derived legacy fields
Category
Cloud Platforms
Sub-category
general
Skill nature
PLATFORM
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Azure Data Factory Primary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

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

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

Skill enrichment (orchestrator / LLM)

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

Derived legacy fields
Category
Data Engineering Tools
Sub-category
general
Skill nature
PRACTICE
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
APIs Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: APIs id=1192 · apis

Aliases — catalog

  • APIs (CANONICAL)

Context tags (catalog)

API Gateway Endpoint GraphQL JSON JWT Microservices OAuth Postman REST Rate Limiting SOAP Swagger Throttling Webhooks XML

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)
Data Lake Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Data Lakes id=1358 · data-lakes

Aliases — catalog

  • Data Lakes (CANONICAL)

Context tags (catalog)

AWS Lake Formation Azure Data Lake ETL big data data catalog data governance data ingestion data lakes vs data warehouses data modeling data pipelines data warehousing partitioning real-time analytics schema evolution serverless architecture

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
SQL Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: SQL id=101 · sql

Aliases — catalog

  • SQL (CANONICAL) primary

Context tags (catalog)

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

Stored enrichment (catalog DB)

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

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

Skill profile (library / DB)

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

Dimensions (API 2 worklist)

  • Pega Programming Languages & DSLs Catalog dimension db id 267

    Library dimension (catalog)

    Roles linked in library: Pega Developer

  • Programming Languages & DSLs Catalog dimension db id 475

    Library dimension (catalog)

    Roles linked in library: Engineering Manager

  • Programming Languages for Data Work Catalog dimension db id 21

    Library dimension (catalog)

    Roles linked in library: Data Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Pega Programming Languages & DSLs
pega-programming-languages-dsls
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Programming Languages & DSLs
programming-languages-dsls
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Programming Languages for Data Work
programming-languages-for-data-work
Existing dimension (library) · Role↔dimension saved
NoSQL Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: NoSQL id=1346 · nosql

Aliases — catalog

  • NoSQL (CANONICAL)

Context tags (catalog)

CAP theorem Cassandra DynamoDB MongoDB Redis column-family data modeling document store eventual consistency graph database horizontal scaling key-value store query language schema-less sharding

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)
Code Refactoring Primary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

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

Derived legacy fields
Category
Software Development
Sub-category
general
Skill nature
PRACTICE
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Design Patterns Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: design patterns id=1654 · design-patterns

Aliases — catalog

  • design patterns (CANONICAL)

Context tags (catalog)

Adapter Builder Command Composite Decorator Dependency Injection Facade Factory MVC Observer Prototype SOLID Singleton Strategy Visitor

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)
CI/CD Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: CI/CD id=1190 · ci-cd

Aliases — catalog

  • CI/CD (CANONICAL)

Context tags (catalog)

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

Stored enrichment (catalog DB)

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

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

Skill profile (library / DB)

Skill nature
METHODOLOGY
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
8
Sub-category id
900
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • CI/CD Pipeline Platforms Catalog dimension db id 150

    Library dimension (catalog)

    Roles linked in library: DevOps Engineer

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

    Library dimension (catalog)

    Roles linked in library: ML Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
CI/CD Pipeline Platforms
ci-cd-pipeline-platforms
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
CI/CD for Machine Learning
ci-cd-for-machine-learning
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Batch ETL Primary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

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

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

Skill enrichment (orchestrator / LLM)

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

Derived legacy fields
Category
Data Engineering Tools
Sub-category
general
Skill nature
PRACTICE
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Dimensional Modeling Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Dimensional modeling id=125 · dimensional-modeling

Aliases — catalog

  • Dimensional modeling (CANONICAL) primary

Context tags (catalog)

ETL Kimball OLAP SCD Type 2 business intelligence conformed dimensions data warehouse dimension table drill-down fact table grain slowly changing dimension snowflake schema star schema surrogate key

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
Data Governance Primary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

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

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

Skill enrichment (orchestrator / LLM)

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

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

Skill enrichment (orchestrator / LLM)

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

Derived legacy fields
Category
Data Management
Sub-category
general
Skill nature
CONCEPT
Volatility
STABLE
Typical lifespan
EVERGREEN
Version strategy
UNVERSIONED
Unstructured Data Primary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

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

Derived legacy fields
Category
Data Management
Sub-category
general
Skill nature
CONCEPT
Volatility
STABLE
Typical lifespan
EVERGREEN
Version strategy
UNVERSIONED
SQL Server Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: SQL Server id=18 · sql-server

Aliases — catalog

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

Context tags (catalog)

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

Stored enrichment (catalog DB)

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

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

Skill profile (library / DB)

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

Dimensions (API 2 worklist)

  • Relational Database Design Catalog dimension db id 4

    Library dimension (catalog)

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

API 3 link attempts (this skill)

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

Skill enrichment (orchestrator / LLM)

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

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

Skill enrichment (orchestrator / LLM)

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

Derived legacy fields
Category
Database Concepts
Sub-category
general
Skill nature
CONCEPT
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Root Cause Analysis Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: root cause analysis id=2392 · root-cause-analysis

Aliases — catalog

  • root cause analysis (CANONICAL) primary
  • root-cause analysis (CANONICAL)

Context tags (catalog)

5 Whys Continuous improvement Corrective action Corrective actions Data analysis Defect analysis Defect tracking Failure mode effects analysis Fishbone diagram Kaizen Lean methodology Pareto analysis Preventive action Preventive measures Problem-solving Process mapping Quality control Root cause identification Statistical process control continuous improvement corrective actions data collection defect analysis failure mode effects analysis problem-solving process mapping quality management root cause identification systematic approach trend analysis

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)
Relational Databases Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Relational Databases id=1345 · relational-databases

Aliases — catalog

  • Relational Databases (CANONICAL)

Context tags (catalog)

ACID Data Modeling Database Migration Entity-Relationship Indexes Joins MySQL Normalization Oracle PostgreSQL Query Optimization SQL Schema Design Stored Procedures Transactions

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)
Boomi Secondary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

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

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

Skill enrichment (orchestrator / LLM)

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

Derived legacy fields
Category
Data Engineering Tools
Sub-category
general
Skill nature
PRACTICE
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Agile Secondary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Agile id=520 · agile

Aliases — catalog

  • Agile (CANONICAL) primary

Context tags (catalog)

Kanban SAFe Scrum backlog backlog grooming burndown burndown chart continuous delivery continuous improvement cross-functional daily standup epics incremental development iteration iteration planning lean product backlog product owner retrospective sprint sprint planning stand-up story points user stories velocity

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
RoleSenior Data Engineer
CompanyGreystar
Experience7+ years relevant and progressive data engineering experience
DomainIT Services & Consulting
JD type pass
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",
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}
API 2 — extract-details
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            "rationale": "Designing curated data structures for analytics and downstream consumption. Covers dimensional modeling, normalization tradeoffs, slowly changing dimensions, and schema evolution for durable datasets.",
            "slug": "data-modeling-and-schema-design",
            "source": "db"
          },
          "input_skill": "Dimensional Modeling",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "Data Engineer",
              "id": 2,
              "rationale": null,
              "role_archetype": null,
              "slug": "data-engineer",
              "source": "db"
            }
          ]
        }
      ],
      "input_skill": "Dimensional Modeling",
      "matched_via": "alias",
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": null,
      "source_tag": "db",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "Data Governance",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Data Management",
          "skill_nature": "CONCEPT",
          "sub_category": "general",
          "typical_lifespan": "MULTI_YEAR",
          "version_strategy": "UNVERSIONED",
          "volatility": "MEDIUM"
        },
        "enrichment": null,
        "keep_log": [],
        "locked_dimensions": [],
        "merge_log": [],
        "placed": null,
        "relationships": null,
        "skill_id": "data-governance",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "Data Warehousing",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Data Engineering Tools",
          "skill_nature": "CONCEPT",
          "sub_category": "general",
          "typical_lifespan": "MULTI_YEAR",
          "version_strategy": "UNVERSIONED",
          "volatility": "MEDIUM"
        },
        "enrichment": null,
        "keep_log": [],
        "locked_dimensions": [],
        "merge_log": [],
        "placed": null,
        "relationships": null,
        "skill_id": "data-warehousing",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "Structured Data",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Data Management",
          "skill_nature": "CONCEPT",
          "sub_category": "general",
          "typical_lifespan": "EVERGREEN",
          "version_strategy": "UNVERSIONED",
          "volatility": "STABLE"
        },
        "enrichment": null,
        "keep_log": [],
        "locked_dimensions": [],
        "merge_log": [],
        "placed": null,
        "relationships": null,
        "skill_id": "structured-data",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "Unstructured Data",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Data Management",
          "skill_nature": "CONCEPT",
          "sub_category": "general",
          "typical_lifespan": "EVERGREEN",
          "version_strategy": "UNVERSIONED",
          "volatility": "STABLE"
        },
        "enrichment": null,
        "keep_log": [],
        "locked_dimensions": [],
        "merge_log": [],
        "placed": null,
        "relationships": null,
        "skill_id": "unstructured-data",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [
        {
          "alias_text": "SQL Server",
          "alias_type": "CANONICAL",
          "id": 135,
          "is_primary": true,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "SQL Server 2000",
          "alias_type": "VERSION",
          "id": 138,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "SQL Server 2005",
          "alias_type": "VERSION",
          "id": 139,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "SQL Server 2008",
          "alias_type": "VERSION",
          "id": 140,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "SQL Server 2012",
          "alias_type": "VERSION",
          "id": 141,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "SQL Server 2014",
          "alias_type": "VERSION",
          "id": 142,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "SQL Server 2016",
          "alias_type": "VERSION",
          "id": 143,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "SQL Server 2017",
          "alias_type": "VERSION",
          "id": 144,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "SQL Server 2019",
          "alias_type": "VERSION",
          "id": 145,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "SQL Server 2022",
          "alias_type": "VERSION",
          "id": 146,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "SQL Server 6.5",
          "alias_type": "VERSION",
          "id": 136,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "SQL Server 7.0",
          "alias_type": "VERSION",
          "id": 137,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        }
      ],
      "canonical": {
        "category_id": 3,
        "display_name": "SQL Server",
        "id": 18,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "TOOL",
        "slug": "sql-server",
        "sub_category_id": 29,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "dimensions": [
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Relational Database Design",
            "id": 4,
            "rationale": "Modeling and operating relational persistence for backend services. Includes schema design, normalization, indexing, transactions, and query tuning for operational data stores.",
            "slug": "relational-database-design",
            "source": "db"
          },
          "input_skill": "SQL Server",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": ".NET Backend Developer",
              "id": 83,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "dotnet-backend-developer",
              "source": "db"
            },
            {
              "display_name": "Backend Developer",
              "id": 1,
              "rationale": null,
              "role_archetype": "A Backend Engineer designs, builds, and maintains the server-side logic and data handling that power applications and services. They focus on implementing reliable business functionality, integrating with other systems, and ensuring the backend is scalable, maintainable, and observable.",
              "slug": "backend-engineer",
              "source": "db"
            },
            {
              "display_name": "Kotlin Backend Developer",
              "id": 84,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "kotlin-server-backend-developer",
              "source": "db"
            },
            {
              "display_name": "Node.js Backend Developer",
              "id": 82,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "node-backend-developer",
              "source": "db"
            },
            {
              "display_name": "Python Backend Developer",
              "id": 80,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "python-backend-developer",
              "source": "db"
            },
            {
              "display_name": "Ruby Backend Developer",
              "id": 85,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "ruby-backend-developer",
              "source": "db"
            },
            {
              "display_name": "Scala Backend Developer",
              "id": 87,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "scala-backend-developer",
              "source": "db"
            }
          ]
        }
      ],
      "input_skill": "SQL Server",
      "matched_via": "alias",
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": null,
      "source_tag": "db",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "T-SQL",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Programming Languages",
          "skill_nature": "LANGUAGE",
          "sub_category": "general",
          "typical_lifespan": "MULTI_YEAR",
          "version_strategy": "UNVERSIONED",
          "volatility": "MEDIUM"
        },
        "enrichment": null,
        "keep_log": [],
        "locked_dimensions": [],
        "merge_log": [],
        "placed": null,
        "relationships": null,
        "skill_id": "t-sql",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "Stored Procedures",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Database Concepts",
          "skill_nature": "CONCEPT",
          "sub_category": "general",
          "typical_lifespan": "MULTI_YEAR",
          "version_strategy": "UNVERSIONED",
          "volatility": "MEDIUM"
        },
        "enrichment": null,
        "keep_log": [],
        "locked_dimensions": [],
        "merge_log": [],
        "placed": null,
        "relationships": null,
        "skill_id": "stored-procedures",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [
        {
          "alias_text": "root cause analysis",
          "alias_type": "CANONICAL",
          "id": 3688,
          "is_primary": true,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "root-cause analysis",
          "alias_type": "CANONICAL",
          "id": 4634,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        }
      ],
      "canonical": {
        "category_id": 8,
        "display_name": "root cause analysis",
        "id": 2392,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "METHODOLOGY",
        "slug": "root-cause-analysis",
        "sub_category_id": 3301,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "dimensions": [
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Observability and Diagnostics",
            "id": 287,
            "rationale": "Instrumentation and troubleshooting practices used to understand Java service behavior in production and lower environments. This cluster covers logs, metrics, traces, correlation IDs, and root-cause analysis from service telemetry.",
            "slug": "observability-and-diagnostics",
            "source": "db"
          },
          "input_skill": "Root Cause Analysis",
          "llm_role": null,
          "roles_from_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": "Python Backend Developer",
              "id": 80,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "python-backend-developer",
              "source": "db"
            }
          ]
        },
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Site Troubleshooting and Debugging",
            "id": 353,
            "rationale": "Diagnosing and fixing Drupal site defects across custom code, configuration, and runtime behavior. This is a coherent cluster because Drupal developers are expected to trace issues from symptoms back to modules, templates, or config.",
            "slug": "site-troubleshooting-and-debugging",
            "source": "db"
          },
          "input_skill": "Root Cause Analysis",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "Drupal Dev",
              "id": 228,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "drupal-dev",
              "source": "db"
            }
          ]
        },
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Sitecore Troubleshooting and Maintenance",
            "id": 447,
            "rationale": "Diagnosing defects, regressions, and maintainability issues across Sitecore code, configuration, and content behavior. This is a coherent cluster because the role is expected to stabilize the site experience over time.",
            "slug": "sitecore-troubleshooting-and-maintenance",
            "source": "db"
          },
          "input_skill": "Root Cause Analysis",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "Sitecore Dev",
              "id": 233,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "sitecore-dev",
              "source": "db"
            }
          ]
        }
      ],
      "input_skill": "Root Cause Analysis",
      "matched_via": "alias",
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": null,
      "source_tag": "db",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [
        {
          "alias_text": "Relational Databases",
          "alias_type": "CANONICAL",
          "id": 1988,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        }
      ],
      "canonical": {
        "category_id": 37,
        "display_name": "Relational Databases",
        "id": 1345,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "CONCEPT",
        "slug": "relational-databases",
        "sub_category_id": 1018,
        "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": "Relational Databases",
          "llm_role": null,
          "roles_from_db": []
        }
      ],
      "input_skill": "Relational Databases",
      "matched_via": "alias",
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": null,
      "source_tag": "db",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "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",
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        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
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        "roles_from_db": [
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        },
        "dimension_id": 20,
        "input_skill": "Microsoft Azure",
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            "role_archetype": "Engineering",
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        "outcome_line": "Skipped \u2014 no persistable v3 meta for new skill",
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        "roles_from_db": [
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        "roles_from_db": [
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        "roles_from_db": [
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        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
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        "skill_dimension_saved": true,
        "skill_id": 1654,
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          "rationale": "Systems used to define, run, and maintain automated build and deployment workflows. This cluster is coherent because the role owns delivery automation end to end, including pipeline reliability and promotion logic.",
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        },
        "dimension_id": 150,
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        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
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            "display_name": "DevOps Engineer",
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        ],
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        "skill_id": 1190,
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        "skipped_reason": null
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        },
        "dimension_id": 56,
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        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
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        "roles_from_db": [
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            "display_name": "ML Engineer",
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            "rationale": null,
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        ],
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          "source": "db"
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        "matched_chosen_role": true,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension saved",
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        "roles_from_db": [
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            "display_name": "Data Engineer",
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        ],
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        "skill_id": 125,
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          "rationale": "Modeling and operating relational persistence for backend services. Includes schema design, normalization, indexing, transactions, and query tuning for operational data stores.",
          "slug": "relational-database-design",
          "source": "db"
        },
        "dimension_id": 4,
        "input_skill": "SQL Server",
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        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
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            "display_name": ".NET Backend Developer",
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            "source": "db"
          },
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            "slug": "backend-engineer",
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            "display_name": "Kotlin Backend Developer",
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            "rationale": null,
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            "display_name": "Node.js Backend Developer",
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            "display_name": "Python Backend Developer",
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            "rationale": null,
            "role_archetype": "Engineering",
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            "source": "db"
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            "display_name": "Ruby Backend Developer",
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            "rationale": null,
            "role_archetype": "Engineering",
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            "display_name": "Scala Backend Developer",
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            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "scala-backend-developer",
            "source": "db"
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        ],
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        "skill_id": 18,
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          "display_name": "Observability and Diagnostics",
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          "rationale": "Instrumentation and troubleshooting practices used to understand Java service behavior in production and lower environments. This cluster covers logs, metrics, traces, correlation IDs, and root-cause analysis from service telemetry.",
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        "dimension_id": 287,
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        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
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            "display_name": "Java Backend Developer",
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            "role_archetype": "Engineering",
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        "skill_id": 2392,
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        "dimension_id": 353,
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        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
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            "display_name": "Drupal Dev",
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          "display_name": "Sitecore Troubleshooting and Maintenance",
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        "dimension_id": 447,
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        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
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            "display_name": "Sitecore Dev",
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        "skill_id": 1345,
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        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
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        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
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            "display_name": "Engineering Manager",
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