Pipeline run
f7f0a464-b3b2-45ff-b078-70dd481b2d78
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 data-engineer 0.38 does not contradict
Resolution:
in_db
— role exists in library; skill↔dim and role↔dim links saved when applicable.
Job description
Job Description Summary We are looking for a skilled and experienced ETL Lead with strong experience in ETL tools like Informatica on Cloud ( IICS ) and Azure Synapse Analytics to join our collaborative IT team. You will be responsible for designing, implementing, and managing data solutions on the Microsoft Azure platform. Job Description Company Overview Working at GE Aerospace means you are bringing your unique perspective, innovative spirit, drive, and curiosity to a collaborative and diverse team working to advance aerospace for future generations. If you have ideas, we will listen. Join us and see your ideas take flight! Site Overview Established in 2000, the John F. Welch Technology Center (JFWTC) in Bengaluru is our multidisciplinary research and engineering center. Engineers and scientists at JFWTC have contributed to hundreds of aviation patents, pioneering breakthroughs in engine technologies, advanced materials, and additive manufacturing. Role Overview • Design and Implement robust ETL workflows using Informatica to extract, transform and load data from diverse sources. • Design data solutions, including data lakes, data warehouses, and real-time data processing systems, leveraging Azure services like Azure Data Lake Storage, Azure Synapse Analytics • Lead a team of azure data engineers in designing, building, and maintaining scalable and efficient data pipelines on Azure cloud platform, utilizing services like Azure Synapse, Azure Data Factory, Azure Databricks, Azure SQL Database, and others. • Integrate diverse data sources into Azure-based solutions. Ensure smooth and efficient ETL processes, real-time data ingestion, and data transformation. Implement data integration best practices. • Design and implement data models and schemas to support business requirements. Ensure data accuracy, consistency, and reliability. Optimize data structures for performance and scalability. • Implement robust data security measures, including encryption, access control, and data masking. Ensure compliance with data privacy regulations and company policies. • Monitor and optimize data pipelines and queries for performance and efficiency. Implement caching, partitioning, and indexing strategies. Troubleshoot and resolve performance issues. • Establish and enforce data quality standards. Implement data governance practices, metadata management, and data lineage tracking. Ensure data quality through validation and cleansing processes. • Collaborate with business stakeholders to understand data requirements and deliver. • Create and maintain comprehensive technical documentation, including system architecture, design documents, and deployment procedures. Ensure knowledge sharing within the team. • Implement Lean daily management and Lean continuous improvement concepts in Application development and operations. Required Qualifications • Bachelor's or Master's degree in Computer Science, Information Technology, or a related field. • Expertise in ETL tools like Informatica PowerCenter, Informatica Cloud ( IICS ) • Extensive experience in data engineering, with a focus on Azure cloud platform. • Proficiency in Azure services like Azure Data Factory, Azure Databricks, Azure SQL Data Warehouse, and Azure Stream Analytics. • Strong programming skills in languages such as Python, SQL, or Scala for data manipulation and transformation. • Experience with big data technologies like Hadoop, Spark, or Hive is a plus. • Familiarity with data visualization tools like Power BI or Tableau. • Knowledge of data warehousing concepts, data lakes, and real-time data processing. • Excellent problem-solving skills and attention to detail, • Strong communication and leadership skills. • Relevant certifications (e.g., Microsoft Certified: Azure Data Engineer) are a plus. Preferred Qualifications • Effective Communicator: Possess the ability to articulate complex technical concepts in a clear and concise manner, facilitating understanding across a wide range of audiences. • Solution-Oriented Mindset: Demonstrated ability to approach challenges with a solutions-first attitude, proactively identifying and addressing potential obstacles. • Change Advocate: Capable of spearheading and implementing change, showcasing a proactive approach to driving improvements and leading teams towards new initiatives. • Leadership Qualities: Show a strong capacity to lead, influence, and guide teams, fostering a collaborative and productive work environment. • Technical Translator: Excel at translating and breaking down intricate technical topics, ensuring that stakeholders at all levels have a clear understanding of the subject matter. • Analytical Thinker: Possess an analytical and detail-oriented mindset, critically evaluating information derived from multiple sources and drawing meaningful conclusions. • Team Player: Exhibit a strong willingness to collaborate and work cohesively with colleagues, understanding the importance of collective growth and knowledge sharing. • Adaptability: Demonstrate resilience and adaptability in an ever-changing environment, staying updated with the latest technological advancements and best practices in the field of database administration. Desirable Qualification • Humble: respectful, receptive, agile, eager to learn • Transparent: shares critical information, speaks with candor, contributes constructively • Focused: quick learner, strategically prioritizes work, committed • Leadership ability: strong communicator, decision-maker, collaborative • Problem solver: analytical-minded, challenges existing processes, critical thinker At GE Aerospace, we have a relentless dedication to the future of safe and more sustainable flight and believe in our talented people to make it happen. Here, you will have the opportunity to work on really cool things with really smart and collaborative people. Together, we will mobilize a new era of growth in aerospace and defense. Where others stop, we accelerate. www.geaerospace.com Additional Information Relocation Assistance Provided: No • This is a remote position
Skills from this JD
Each row merges API 1 extraction, API 2 library match / v3 orchestration (dimensions + locked dims), and API 3 persistence tags.
Aliases — catalog
- Informatica (CANONICAL) primary
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Platform
- Sub-category
- Data Integration Platform
- Vendor
- Informatica
- License
- proprietary
- Year introduced
- 1993
- Confidence
- 0.90
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Informatica appears frequently in enterprise data-integration and ETL job postings, especially alongside cloud migration and MDM roles; it remains a common hiring keyword rather than a sunset technology.
Skill profile (library / DB)
- Skill nature
- PLATFORM
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 9
- Sub-category id
- 114
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
ETL and ELT Tooling Catalog dimension db id 24
Library dimension (catalog)
Roles linked in library: Data Engineer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
ETL and ELT Tooling
etl-and-elt-tooling
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved |
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Data Engineering Tools
- Sub-category
- general
- Skill nature
- TOOL
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Data Engineering Tools
- Sub-category
- general
- Skill nature
- 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
- PLATFORM
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Aliases — catalog
- Azure (CANONICAL) primary
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Platform
- Sub-category
- Cloud Platform
- Vendor
- Microsoft
- License
- proprietary
- Year introduced
- 2010
- Confidence
- 0.99
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Azure is broadly adopted and frequently appears in cloud/platform job descriptions alongside AWS and GCP; Microsoft’s ongoing enterprise investment and Azure certification demand signal strong hiring-pipeline relevance.
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 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
-
Cloud Platforms & Managed Services Catalog dimension db id 221
Library dimension (catalog)
Roles linked in library: Fullstack Developer, Go Backend Developer, Node.js Backend Developer
-
Cloud Platforms for AI Deployment Catalog dimension db id 211
Library dimension (catalog)
Roles linked in library: AI Engineer
-
Cloud Provider Platforms Catalog dimension db id 131
Library dimension (catalog)
Roles linked in library: Cloud Architect, Cloud Security Engineer
-
Cloud Security Posture Tools Catalog dimension db id 64
Library dimension (catalog)
Roles linked in library: Cloud Security Engineer, Cyber Security Engineer
-
Vendor Product Families Catalog dimension db id 477
Library dimension (catalog)
Roles linked in library: Engineering Manager
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Cloud Platforms
cloud-platforms
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved |
|
Cloud Platforms & Managed Services
cloud-platforms-managed-services
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Cloud Platforms for AI Deployment
cloud-platforms-for-ai-deployment
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Cloud Provider Platforms
cloud-provider-platforms
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Cloud Security Posture Tools
cloud-security-posture-tools
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Vendor Product Families
vendor-product-families
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Aliases — catalog
- Azure Blob Storage (CANONICAL) primary
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Service
- Sub-category
- Object Storage Service
- Vendor
- Microsoft
- License
- proprietary
- Year introduced
- 2008
- Confidence
- 0.98
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Broadly used object storage on Azure; appears frequently in cloud/data engineering JDs and Microsoft positions it as a core storage service, with no sunset or replacement signal.
Skill profile (library / DB)
- Skill nature
- CLOUD_SERVICE
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 11
- Sub-category id
- 120
- 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
-
Cloud Storage and File Formats Catalog dimension db id 35
Library dimension (catalog)
Roles linked in library: Data Engineer
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
|
|
Cloud Storage and File Formats
cloud-storage-and-file-formats
|
— | — |
Skipped — no persistable v3 meta for new skill
skill_not_in_db_v3_proposed
|
Aliases — catalog
- Azure Synapse Analytics (CANONICAL) primary
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Service
- Sub-category
- Analytics Service
- Vendor
- Microsoft
- License
- proprietary
- Year introduced
- 2019
- Confidence
- 0.95
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Common in cloud data-platform JDs and Microsoft’s Azure analytics stack; often listed alongside Databricks/ADF for warehousing and ETL, indicating broad hiring demand.
Skill profile (library / DB)
- Skill nature
- CLOUD_SERVICE
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 11
- Sub-category id
- 117
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
Cloud Data Warehouses Catalog dimension db id 22
Library dimension (catalog)
Roles linked in library: Data Engineer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Cloud Data Warehouses
cloud-data-warehouses
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved |
Aliases — catalog
- Azure Synapse Analytics (CANONICAL) primary
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Service
- Sub-category
- Analytics Service
- Vendor
- Microsoft
- License
- proprietary
- Year introduced
- 2019
- Confidence
- 0.95
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Common in cloud data-platform JDs and Microsoft’s Azure analytics stack; often listed alongside Databricks/ADF for warehousing and ETL, indicating broad hiring demand.
Skill profile (library / DB)
- Skill nature
- CLOUD_SERVICE
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 11
- Sub-category id
- 117
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
Cloud Data Warehouses Catalog dimension db id 22
Library dimension (catalog)
Roles linked in library: Data Engineer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Cloud Data Warehouses
cloud-data-warehouses
|
— | — |
Skipped — no persistable v3 meta for new skill
skill_not_in_db_v3_proposed
|
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Data Engineering Tools
- 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
- PLATFORM
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Databases
- Sub-category
- general
- Skill nature
- PLATFORM
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Databases
- Sub-category
- general
- Skill nature
- 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
- PLATFORM
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
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
- 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
- Scala (CANONICAL) primary
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Language
- Sub-category
- Programming Language
- Vendor
- EPFL
- License
- apache_2
- Year introduced
- 2004
- Confidence
- 0.99
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Scala still appears in many backend/data engineering JDs, especially with Spark and Akka, and remains supported by major JVM ecosystems; it’s not a sunset technology.
Skill profile (library / DB)
- Skill nature
- LANGUAGE
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 6
- Sub-category id
- 96
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
Programming Languages for Data Work Catalog dimension db id 21
Library dimension (catalog)
Roles linked in library: Data Engineer
-
Programming Languages for ML Systems Catalog dimension db id 39
Library dimension (catalog)
Roles linked in library: ML Engineer, MLOps Engineer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Programming Languages for Data Work
programming-languages-for-data-work
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved |
|
Programming Languages for ML Systems
programming-languages-for-ml-systems
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Aliases — catalog
- Hadoop (CANONICAL)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Framework
- Sub-category
- Data Processing Framework
- Vendor
- Apache Software Foundation
- License
- apache_2
- Year introduced
- 2006
- Confidence
- 0.90
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Job postings still mention Hadoop for legacy big-data stacks, but JD volume has fallen as Spark and cloud warehouses replaced MapReduce-era clusters.
Skill profile (library / DB)
- Skill nature
- FRAMEWORK
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 5
- Sub-category id
- 91
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
ETL and ELT Tooling Catalog dimension db id 24
Library dimension (catalog)
Roles linked in library: Data Engineer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
ETL and ELT Tooling
etl-and-elt-tooling
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved |
Aliases — catalog
- Apache Spark (CANONICAL)
- apache spark 3 (VERSION)
- spark (VERSION)
- spark 3 (VERSION)
- spark 3.x (VERSION)
- spark3 (VERSION)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Framework
- Sub-category
- Distributed Data Processing Framework
- Vendor
- Apache Software Foundation
- License
- apache_2
- Year introduced
- 2010
- Confidence
- 0.94
- Version strategy
- SEPARATE_ENTITY
- Version tag
- 3.x
Maturity reasoning: Apache Spark appears in many data engineering JDs and remains a standard for distributed ETL/ELT; its GitHub and vendor ecosystem activity stay strong, with Databricks and cloud platforms still promoting it.
Skill profile (library / DB)
- Skill nature
- FRAMEWORK
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 5
- Sub-category id
- 1021
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
ETL and ELT Tooling Catalog dimension db id 24
Library dimension (catalog)
Roles linked in library: Data Engineer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
ETL and ELT Tooling
etl-and-elt-tooling
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved |
Aliases — catalog
- Hive (CANONICAL) primary
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Datastore
- Sub-category
- Local Key Value Store
- Vendor
- Apache Software Foundation
- License
- apache_2
- Year introduced
- 2010
- Confidence
- 0.90
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Hive appears in Flutter/mobile JDs and package docs, but JD volume is far below SQLite/Realm and it’s mainly used for local key-value storage in Flutter apps.
Skill profile (library / DB)
- Skill nature
- TOOL
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 3
- Sub-category id
- 2242
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
Local Persistence and Offline Behavior Catalog dimension db id 85
Library dimension (catalog)
Roles linked in library: Android Developer, Flutter Developer, Hybrid Mobile Developer, Native Mobile Developer, React Native Developer, iOS Developer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Local Persistence and Offline Behavior
local-persistence-and-offline-behavior
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Aliases — catalog
- Power BI (CANONICAL) primary
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Platform
- Sub-category
- Bi Analytics Platform
- Vendor
- Microsoft
- License
- proprietary
- Year introduced
- 2015
- Confidence
- 0.96
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Power BI appears frequently in BI/data analyst job descriptions and is a standard Microsoft analytics platform in enterprise stacks, with strong vendor support and broad adoption.
Skill profile (library / DB)
- Skill nature
- PLATFORM
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 9
- Sub-category id
- 111
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
BI and Visualization Tools Catalog dimension db id 31
Library dimension (catalog)
Roles linked in library: Data Engineer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
BI and Visualization Tools
bi-and-visualization-tools
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved |
Aliases — catalog
- Tableau (CANONICAL) primary
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Platform
- Sub-category
- Bi Analytics Platform
- Vendor
- Tableau Software
- License
- proprietary
- Year introduced
- 2003
- Confidence
- 0.96
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Tableau appears frequently in BI/data analyst job descriptions and remains a standard enterprise analytics platform with strong vendor support and broad adoption.
Skill profile (library / DB)
- Skill nature
- PLATFORM
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 9
- Sub-category id
- 111
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
BI and Visualization Tools Catalog dimension db id 31
Library dimension (catalog)
Roles linked in library: Data Engineer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
BI and Visualization Tools
bi-and-visualization-tools
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved |
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Practices
- Sub-category
- general
- Skill nature
- PRACTICE
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
All API 3 persistence rows
Same grid as the skill-extractor “Persistence items” table: one row per (skill × dimension) work item.
| Skill | Tag | Dimension | Skill↔dim | Role↔dim | Outcome | Notes |
|---|---|---|---|---|---|---|
| Informatica | in_db |
ETL and ELT Tooling
etl-and-elt-tooling
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved | |
| Azure | in_db |
Cloud Platforms
cloud-platforms
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved | |
| Azure | in_db |
Cloud Platforms & Managed Services
cloud-platforms-managed-services
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Azure | in_db |
Cloud Platforms for AI Deployment
cloud-platforms-for-ai-deployment
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Azure | in_db |
Cloud Provider Platforms
cloud-provider-platforms
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Azure | in_db |
Cloud Security Posture Tools
cloud-security-posture-tools
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Azure | in_db |
Vendor Product Families
vendor-product-families
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Azure Data Lake Storage | 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 |
| Azure Data Lake Storage | new |
Cloud Storage and File Formats
cloud-storage-and-file-formats
|
— | — | Skipped — no persistable v3 meta for new skill | skill_not_in_db_v3_proposed |
| Azure Synapse Analytics | in_db |
Cloud Data Warehouses
cloud-data-warehouses
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved | |
| Azure Synapse | new |
Cloud Data Warehouses
cloud-data-warehouses
|
— | — | 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) | |
| 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 | |
| Scala | in_db |
Programming Languages for Data Work
programming-languages-for-data-work
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved | |
| Scala | in_db |
Programming Languages for ML Systems
programming-languages-for-ml-systems
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Hadoop | in_db |
ETL and ELT Tooling
etl-and-elt-tooling
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved | |
| Spark | in_db |
ETL and ELT Tooling
etl-and-elt-tooling
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved | |
| Hive | in_db |
Local Persistence and Offline Behavior
local-persistence-and-offline-behavior
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Power BI | in_db |
BI and Visualization Tools
bi-and-visualization-tools
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved | |
| Tableau | in_db |
BI and Visualization Tools
bi-and-visualization-tools
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved |
Library artifacts (this run)
| Kind | Detail | DB id |
|---|---|---|
| canonical_skill_proposed | Informatica PowerCenter | type=Data Engineering Tools subtype=general nature=TOOL lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Informatica Cloud | type=Data Engineering Tools subtype=general nature=PLATFORM lifespan=MULTI_YEAR | |
| canonical_skill_proposed | IICS | type=Data Engineering Tools subtype=general nature=PLATFORM lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Azure Data Factory | type=Data Engineering Tools subtype=general nature=PLATFORM lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Azure Databricks | type=Data Engineering Tools subtype=general nature=PLATFORM lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Azure SQL Database | type=Databases subtype=general nature=PLATFORM lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Azure SQL Data Warehouse | type=Databases subtype=general nature=PLATFORM lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Azure Stream Analytics | type=Data Engineering Tools subtype=general nature=PLATFORM lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Lean | type=Practices subtype=general nature=PRACTICE lifespan=MULTI_YEAR | |
| dimension_skill_link_proposed | Azure Data Lake Storage ↔ Cloud Storage and Data Services | |
| dimension_skill_link_proposed | Azure Data Lake Storage ↔ Cloud Storage and File Formats | |
| role_dimension_link_proposed | Data Engineer ↔ Cloud Storage and File Formats | |
| dimension_skill_link_proposed | Azure Synapse ↔ Cloud Data Warehouses | |
| role_dimension_link_proposed | Data Engineer ↔ Cloud Data Warehouses |
nano JD Parser — gpt-4.1-nano click to toggle
Certifications
Show raw JSON
{
"JD_type": "pass",
"about_company": {
"source_marker": {
"first_5_words": "Working at GE Aerospace means",
"last_5_words": "see your ideas take flight!"
},
"text": "Working at GE Aerospace means you are bringing your unique perspective, innovative spirit, drive, and curiosity to a collaborative and diverse team working to advance aerospace for future generations. If you have ideas, we will listen. Join us and see your ideas take flight!",
"word_count": 50
},
"certifications": [
"Microsoft Certified: Azure Data Engineer"
],
"company_name": "GE Aerospace",
"ctc": null,
"domain": {
"primary": {
"aliases": [
"Aerospace",
"Defense"
],
"domain": "Aerospace \u0026 Defense"
},
"secondary": null
},
"education": [
{
"level": "Bachelor\u0027s",
"qualification": "BTECH/BE/BSC/MTECH/ME - Computer Science / Information Technology (or related)",
"raw": "Bachelor\u0027s or Master\u0027s degree in Computer Science, Information Technology, or a related field.",
"requirement": "required"
}
],
"experience": {
"max": null,
"min": null,
"raw": null
},
"job_locations": [
{
"aliases": [],
"city": null,
"country": null,
"state": null,
"work_mode": "remote"
}
],
"role": "ETL Lead",
"role_aliases": [
"ETL Engineer",
"Data Engineer",
"ETL Developer"
],
"role_archetype": "Data",
"roles_and_responsibilities": [
{
"bullet_count": 11,
"heading": "Role Overview",
"heading_was_present": true,
"source_marker": {
"first_5_words": "\u2022 Design and Implement robust ETL",
"last_5_words": "and Lean continuous improvement concepts."
},
"text": "\u2022 Design and Implement robust ETL workflows using Informatica to extract, transform and load data from diverse sources.\n\u2022 Design data solutions, including data lakes, data warehouses, and real-time data processing systems, leveraging Azure services like Azure Data Lake Storage, Azure Synapse Analytics\n\u2022 Lead a team of azure data engineers in designing, building, and maintaining scalable and efficient data pipelines on Azure cloud platform, utilizing services like Azure Synapse, Azure Data Factory, Azure Databricks, Azure SQL Database, and others.\n\u2022 Integrate diverse data sources into Azure-based solutions. Ensure smooth and efficient ETL processes, real-time data ingestion, and data transformation. Implement data integration best practices.\n\u2022 Design and implement data models and schemas to support business requirements. Ensure data accuracy, consistency, and reliability. Optimize data structures for performance and scalability.\n\u2022 Implement robust data security measures, including encryption, access control, and data masking. Ensure compliance with data privacy regulations and company policies.\n\u2022 Monitor and optimize data pipelines and queries for performance and efficiency. Implement caching, partitioning, and indexing strategies. Troubleshoot and resolve performance issues.\n\u2022 Establish and enforce data quality standards. Implement data governance practices, metadata management, and data lineage tracking. Ensure data quality through validation and cleansing processes.\n\u2022 Collaborate with business stakeholders to understand data requirements and deliver.\n\u2022 Create and maintain comprehensive technical documentation, including system architecture, design documents, and deployment procedures. Ensure knowledge sharing within the team.\n\u2022 Implement Lean daily management and Lean continuous improvement concepts in Application development and operations.",
"word_count": 284
},
{
"bullet_count": 11,
"heading": "Required Qualifications",
"heading_was_present": true,
"source_marker": {
"first_5_words": "\u2022 Bachelor\u0027s or Master\u0027s degree",
"last_5_words": "are a plus."
},
"text": "\u2022 Bachelor\u0027s or Master\u0027s degree in Computer Science, Information Technology, or a related field.\n\u2022 Expertise in ETL tools like Informatica PowerCenter, Informatica Cloud ( IICS )\n\u2022 Extensive experience in data engineering, with a focus on Azure cloud platform.\n\u2022 Proficiency in Azure services like Azure Data Factory, Azure Databricks, Azure SQL Data Warehouse, and Azure Stream Analytics.\n\u2022 Strong programming skills in languages such as Python, SQL, or Scala for data manipulation and transformation.\n\u2022 Experience with big data technologies like Hadoop, Spark, or Hive is a plus.\n\u2022 Familiarity with data visualization tools like Power BI or Tableau.\n\u2022 Knowledge of data warehousing concepts, data lakes, and real-time data processing.\n\u2022 Excellent problem-solving skills and attention to detail,\n\u2022 Strong communication and leadership skills.\n\u2022 Relevant certifications (e.g., Microsoft Certified: Azure Data Engineer) are a plus.",
"word_count": 134
},
{
"bullet_count": 8,
"heading": "Preferred Qualifications",
"heading_was_present": true,
"source_marker": {
"first_5_words": "\u2022 Effective Communicator: Possess the",
"last_5_words": "and best practices in the field."
},
"text": "\u2022 Effective Communicator: Possess the ability to articulate complex technical concepts in a clear and concise manner, facilitating understanding across a wide range of audiences.\n\u2022 Solution-Oriented Mindset: Demonstrated ability to approach challenges with a solutions-first attitude, proactively identifying and addressing potential obstacles.\n\u2022 Change Advocate: Capable of spearheading and implementing change, showcasing a proactive approach to driving improvements and leading teams towards new initiatives.\n\u2022 Leadership Qualities: Show a strong capacity to lead, influence, and guide teams, fostering a collaborative and productive work environment.\n\u2022 Technical Translator: Excel at translating and breaking down intricate technical topics, ensuring that stakeholders at all levels have a clear understanding of the subject matter.\n\u2022 Analytical Thinker: Possess an analytical and detail-oriented mindset, critically evaluating information derived from multiple sources and drawing meaningful conclusions.\n\u2022 Team Player: Exhibit a strong willingness to collaborate and work cohesively with colleagues, understanding the importance of collective growth and knowledge sharing.\n\u2022 Adaptability: Demonstrate resilience and adaptability in an ever-changing environment, staying updated with the latest technological advancements and best practices in the field of database administration.",
"word_count": 164
},
{
"bullet_count": 5,
"heading": "Desirable Qualification",
"heading_was_present": true,
"source_marker": {
"first_5_words": "\u2022 Humble: respectful, receptive, agile,",
"last_5_words": "existing processes, critical thinker"
},
"text": "\u2022 Humble: respectful, receptive, agile, eager to learn\n\u2022 Transparent: shares critical information, speaks with candor, contributes constructively\n\u2022 Focused: quick learner, strategically prioritizes work, committed \n\u2022 Leadership ability: strong communicator, decision-maker, collaborative\n\u2022 Problem solver: analytical-minded, challenges existing processes, critical thinker",
"word_count": 34
}
],
"urls": [
{
"type": "website",
"url": "http://www.geaerospace.com"
}
]
}
API 1 — extract-from-jd click to toggle
{
"final_skills": [
{
"is_primary": true,
"skill_name": "Informatica"
},
{
"is_primary": true,
"skill_name": "Informatica PowerCenter"
},
{
"is_primary": true,
"skill_name": "Informatica Cloud"
},
{
"is_primary": true,
"skill_name": "IICS"
},
{
"is_primary": true,
"skill_name": "Azure"
},
{
"is_primary": true,
"skill_name": "Azure Data Lake Storage"
},
{
"is_primary": true,
"skill_name": "Azure Synapse Analytics"
},
{
"is_primary": true,
"skill_name": "Azure Synapse"
},
{
"is_primary": true,
"skill_name": "Azure Data Factory"
},
{
"is_primary": true,
"skill_name": "Azure Databricks"
},
{
"is_primary": true,
"skill_name": "Azure SQL Database"
},
{
"is_primary": true,
"skill_name": "Azure SQL Data Warehouse"
},
{
"is_primary": true,
"skill_name": "Azure Stream Analytics"
},
{
"is_primary": true,
"skill_name": "Python"
},
{
"is_primary": true,
"skill_name": "SQL"
},
{
"is_primary": true,
"skill_name": "Scala"
},
{
"is_primary": false,
"skill_name": "Hadoop"
},
{
"is_primary": false,
"skill_name": "Spark"
},
{
"is_primary": false,
"skill_name": "Hive"
},
{
"is_primary": false,
"skill_name": "Power BI"
},
{
"is_primary": false,
"skill_name": "Tableau"
},
{
"is_primary": false,
"skill_name": "Lean"
}
],
"jd_role": {
"display_name": "ETL Lead",
"rationale": null,
"role_aliases": [
"ETL Engineer",
"Data Engineer",
"ETL Developer"
],
"role_archetype": "Data",
"slug": ""
},
"nano_parsed": {
"JD_type": "pass",
"about_company": {
"source_marker": {
"first_5_words": "Working at GE Aerospace means",
"last_5_words": "see your ideas take flight!"
},
"text": "Working at GE Aerospace means you are bringing your unique perspective, innovative spirit, drive, and curiosity to a collaborative and diverse team working to advance aerospace for future generations. If you have ideas, we will listen. Join us and see your ideas take flight!",
"word_count": 50
},
"certifications": [
"Microsoft Certified: Azure Data Engineer"
],
"company_name": "GE Aerospace",
"ctc": null,
"domain": {
"primary": {
"aliases": [
"Aerospace",
"Defense"
],
"domain": "Aerospace \u0026 Defense"
},
"secondary": null
},
"education": [
{
"level": "Bachelor\u0027s",
"qualification": "BTECH/BE/BSC/MTECH/ME - Computer Science / Information Technology (or related)",
"raw": "Bachelor\u0027s or Master\u0027s degree in Computer Science, Information Technology, or a related field.",
"requirement": "required"
}
],
"experience": {
"max": null,
"min": null,
"raw": null
},
"job_locations": [
{
"aliases": [],
"city": null,
"country": null,
"state": null,
"work_mode": "remote"
}
],
"role": "ETL Lead",
"role_aliases": [
"ETL Engineer",
"Data Engineer",
"ETL Developer"
],
"role_archetype": "Data",
"roles_and_responsibilities": [
{
"bullet_count": 11,
"heading": "Role Overview",
"heading_was_present": true,
"source_marker": {
"first_5_words": "\u2022 Design and Implement robust ETL",
"last_5_words": "and Lean continuous improvement concepts."
},
"text": "\u2022 Design and Implement robust ETL workflows using Informatica to extract, transform and load data from diverse sources.\n\u2022 Design data solutions, including data lakes, data warehouses, and real-time data processing systems, leveraging Azure services like Azure Data Lake Storage, Azure Synapse Analytics\n\u2022 Lead a team of azure data engineers in designing, building, and maintaining scalable and efficient data pipelines on Azure cloud platform, utilizing services like Azure Synapse, Azure Data Factory, Azure Databricks, Azure SQL Database, and others.\n\u2022 Integrate diverse data sources into Azure-based solutions. Ensure smooth and efficient ETL processes, real-time data ingestion, and data transformation. Implement data integration best practices.\n\u2022 Design and implement data models and schemas to support business requirements. Ensure data accuracy, consistency, and reliability. Optimize data structures for performance and scalability.\n\u2022 Implement robust data security measures, including encryption, access control, and data masking. Ensure compliance with data privacy regulations and company policies.\n\u2022 Monitor and optimize data pipelines and queries for performance and efficiency. Implement caching, partitioning, and indexing strategies. Troubleshoot and resolve performance issues.\n\u2022 Establish and enforce data quality standards. Implement data governance practices, metadata management, and data lineage tracking. Ensure data quality through validation and cleansing processes.\n\u2022 Collaborate with business stakeholders to understand data requirements and deliver.\n\u2022 Create and maintain comprehensive technical documentation, including system architecture, design documents, and deployment procedures. Ensure knowledge sharing within the team.\n\u2022 Implement Lean daily management and Lean continuous improvement concepts in Application development and operations.",
"word_count": 284
},
{
"bullet_count": 11,
"heading": "Required Qualifications",
"heading_was_present": true,
"source_marker": {
"first_5_words": "\u2022 Bachelor\u0027s or Master\u0027s degree",
"last_5_words": "are a plus."
},
"text": "\u2022 Bachelor\u0027s or Master\u0027s degree in Computer Science, Information Technology, or a related field.\n\u2022 Expertise in ETL tools like Informatica PowerCenter, Informatica Cloud ( IICS )\n\u2022 Extensive experience in data engineering, with a focus on Azure cloud platform.\n\u2022 Proficiency in Azure services like Azure Data Factory, Azure Databricks, Azure SQL Data Warehouse, and Azure Stream Analytics.\n\u2022 Strong programming skills in languages such as Python, SQL, or Scala for data manipulation and transformation.\n\u2022 Experience with big data technologies like Hadoop, Spark, or Hive is a plus.\n\u2022 Familiarity with data visualization tools like Power BI or Tableau.\n\u2022 Knowledge of data warehousing concepts, data lakes, and real-time data processing.\n\u2022 Excellent problem-solving skills and attention to detail,\n\u2022 Strong communication and leadership skills.\n\u2022 Relevant certifications (e.g., Microsoft Certified: Azure Data Engineer) are a plus.",
"word_count": 134
},
{
"bullet_count": 8,
"heading": "Preferred Qualifications",
"heading_was_present": true,
"source_marker": {
"first_5_words": "\u2022 Effective Communicator: Possess the",
"last_5_words": "and best practices in the field."
},
"text": "\u2022 Effective Communicator: Possess the ability to articulate complex technical concepts in a clear and concise manner, facilitating understanding across a wide range of audiences.\n\u2022 Solution-Oriented Mindset: Demonstrated ability to approach challenges with a solutions-first attitude, proactively identifying and addressing potential obstacles.\n\u2022 Change Advocate: Capable of spearheading and implementing change, showcasing a proactive approach to driving improvements and leading teams towards new initiatives.\n\u2022 Leadership Qualities: Show a strong capacity to lead, influence, and guide teams, fostering a collaborative and productive work environment.\n\u2022 Technical Translator: Excel at translating and breaking down intricate technical topics, ensuring that stakeholders at all levels have a clear understanding of the subject matter.\n\u2022 Analytical Thinker: Possess an analytical and detail-oriented mindset, critically evaluating information derived from multiple sources and drawing meaningful conclusions.\n\u2022 Team Player: Exhibit a strong willingness to collaborate and work cohesively with colleagues, understanding the importance of collective growth and knowledge sharing.\n\u2022 Adaptability: Demonstrate resilience and adaptability in an ever-changing environment, staying updated with the latest technological advancements and best practices in the field of database administration.",
"word_count": 164
},
{
"bullet_count": 5,
"heading": "Desirable Qualification",
"heading_was_present": true,
"source_marker": {
"first_5_words": "\u2022 Humble: respectful, receptive, agile,",
"last_5_words": "existing processes, critical thinker"
},
"text": "\u2022 Humble: respectful, receptive, agile, eager to learn\n\u2022 Transparent: shares critical information, speaks with candor, contributes constructively\n\u2022 Focused: quick learner, strategically prioritizes work, committed \n\u2022 Leadership ability: strong communicator, decision-maker, collaborative\n\u2022 Problem solver: analytical-minded, challenges existing processes, critical thinker",
"word_count": 34
}
],
"urls": [
{
"type": "website",
"url": "http://www.geaerospace.com"
}
]
},
"rejected": false,
"rejection_reason": null,
"run_id": "f7f0a464-b3b2-45ff-b078-70dd481b2d78",
"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": "Works with data analysts, data scientists, and business stakeholders to define data models, ingestion schedules, and data delivery requirements.",
"sentence": "Collaborate with business stakeholders to understand data requirements and deliver.",
"similarity": 0.7079
},
{
"kra_text": "Implements data quality validation rules, reconciliation checks, and anomaly detection to ensure data completeness, accuracy, and consistency.",
"sentence": "Ensure data quality through validation and cleansing processes.",
"similarity": 0.7025
},
{
"kra_text": "Maintains data catalog entries, column-level data lineage, and technical documentation to support data discoverability and governance across the organization.",
"sentence": "Implement data governance practices, metadata management, and data lineage tracking.",
"similarity": 0.7014
}
],
"matched_count": null,
"matched_skills": null,
"role_id": 2,
"score": 0.7039,
"slug": "data-engineer",
"total_count": null
},
{
"display_name": "Svelte Frontend Developer",
"kra_matches": [
{
"kra_text": "backend data integration",
"sentence": "Implement data integration best practices.",
"similarity": 0.6802
},
{
"kra_text": "performance tuning",
"sentence": "Troubleshoot and resolve performance issues.",
"similarity": 0.5865
},
{
"kra_text": "backend data integration",
"sentence": "Integrate diverse data sources into Azure-based solutions.",
"similarity": 0.5516
}
],
"matched_count": null,
"matched_skills": null,
"role_id": 92,
"score": 0.6061,
"slug": "svelte-frontend-developer",
"total_count": null
},
{
"display_name": "Backend Developer",
"kra_matches": [
{
"kra_text": "Identifies and resolves backend performance bottlenecks through query optimization, indexing strategies, connection pooling, and distributed caching with Redis.",
"sentence": "Implement caching, partitioning, and indexing strategies.",
"similarity": 0.6072
},
{
"kra_text": "Identifies and resolves backend performance bottlenecks through query optimization, indexing strategies, connection pooling, and distributed caching with Redis.",
"sentence": "Troubleshoot and resolve performance issues.",
"similarity": 0.5758
},
{
"kra_text": "Identifies and resolves backend performance bottlenecks through query optimization, indexing strategies, connection pooling, and distributed caching with Redis.",
"sentence": "Monitor and optimize data pipelines and queries for performance and efficiency.",
"similarity": 0.5672
}
],
"matched_count": null,
"matched_skills": null,
"role_id": 1,
"score": 0.5834,
"slug": "backend-engineer",
"total_count": null
},
{
"display_name": "Java Backend Developer",
"kra_matches": [
{
"kra_text": "backend performance tuning",
"sentence": "Troubleshoot and resolve performance issues.",
"similarity": 0.6132
},
{
"kra_text": "backend performance tuning",
"sentence": "Optimize data structures for performance and scalability.",
"similarity": 0.5666
},
{
"kra_text": "backend performance tuning",
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"similarity": 0.555
}
],
"matched_count": null,
"matched_skills": null,
"role_id": 79,
"score": 0.5783,
"slug": "java-backend-developer",
"total_count": null
},
{
"display_name": "Flutter Developer",
"kra_matches": [
{
"kra_text": "integrate external APIs and data sources",
"sentence": "Integrate diverse data sources into Azure-based solutions.",
"similarity": 0.5972
},
{
"kra_text": "optimize responsiveness and performance",
"sentence": "Optimize data structures for performance and scalability.",
"similarity": 0.5741
},
{
"kra_text": "integrate external APIs and data sources",
"sentence": "Implement data integration best practices.",
"similarity": 0.5368
}
],
"matched_count": null,
"matched_skills": null,
"role_id": 74,
"score": 0.5694,
"slug": "flutter-developer",
"total_count": null
}
],
"skill_match_roles": [
{
"display_name": "Data Engineer",
"kra_matches": null,
"matched_count": 6,
"matched_skills": [
"Azure",
"Azure Synapse Analytics",
"Informatica",
"Python",
"SQL",
"Scala"
],
"role_id": 2,
"score": 0.375,
"slug": "data-engineer",
"total_count": 16
},
{
"display_name": "ML Engineer",
"kra_matches": null,
"matched_count": 3,
"matched_skills": [
"Azure",
"Python",
"Scala"
],
"role_id": 3,
"score": 0.1875,
"slug": "ml-engineer",
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},
{
"display_name": "Engineering Manager",
"kra_matches": null,
"matched_count": 3,
"matched_skills": [
"Azure",
"Python",
"SQL"
],
"role_id": 121,
"score": 0.1875,
"slug": "engineering-manager",
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},
{
"display_name": "MLOps Engineer",
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"matched_count": 3,
"matched_skills": [
"Azure",
"Python",
"Scala"
],
"role_id": 16,
"score": 0.1875,
"slug": "ml-ops-engineer",
"total_count": 16
},
{
"display_name": "Cyber Security Engineer",
"kra_matches": null,
"matched_count": 2,
"matched_skills": [
"Azure",
"Python"
],
"role_id": 5,
"score": 0.125,
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"total_count": 16
}
]
},
"stage4_decision": {
"alias_collision_detected": false,
"case": "A",
"chosen_role": {
"display_name": "Data Engineer",
"kra_matches": null,
"matched_count": null,
"matched_skills": null,
"role_id": 2,
"score": 1.0,
"slug": "data-engineer",
"total_count": null
},
"confidence": 1.0,
"is_new_role": false,
"llm2_fired": false,
"llm2_reasoning": null,
"matched_dimensions": [],
"matched_kras": [],
"matched_skills": [],
"new_role_display_name": null,
"new_role_slug": null,
"queued": false,
"reasoning": "Exact alias hit on data-engineer (1.0) \u2014 no other alias at this confidence; skill_top data-engineer 0.38 does not contradict",
"sub_role": null
},
"stage5_updates": {
"centroid_n_after": 499,
"centroid_updated": true,
"collision_log_id": null,
"new_kra_attached": null,
"new_skills_attached": [
{
"is_primary": true,
"queue_id": 23239,
"role_display_name": "Data Engineer",
"role_slug": "data-engineer",
"skill_name": "Informatica PowerCenter",
"status": "pending"
},
{
"is_primary": true,
"queue_id": 23240,
"role_display_name": "Data Engineer",
"role_slug": "data-engineer",
"skill_name": "Informatica Cloud",
"status": "pending"
},
{
"is_primary": true,
"queue_id": 23241,
"role_display_name": "Data Engineer",
"role_slug": "data-engineer",
"skill_name": "IICS",
"status": "pending"
},
{
"is_primary": true,
"queue_id": 23242,
"role_display_name": "Data Engineer",
"role_slug": "data-engineer",
"skill_name": "Azure Data Lake Storage",
"status": "pending"
},
{
"is_primary": true,
"queue_id": 23243,
"role_display_name": "Data Engineer",
"role_slug": "data-engineer",
"skill_name": "Azure Synapse",
"status": "pending"
},
{
"is_primary": true,
"queue_id": 23244,
"role_display_name": "Data Engineer",
"role_slug": "data-engineer",
"skill_name": "Azure Data Factory",
"status": "pending"
},
{
"is_primary": true,
"queue_id": 23245,
"role_display_name": "Data Engineer",
"role_slug": "data-engineer",
"skill_name": "Azure Databricks",
"status": "pending"
},
{
"is_primary": true,
"queue_id": 23246,
"role_display_name": "Data Engineer",
"role_slug": "data-engineer",
"skill_name": "Azure SQL Database",
"status": "pending"
},
{
"is_primary": true,
"queue_id": 23247,
"role_display_name": "Data Engineer",
"role_slug": "data-engineer",
"skill_name": "Azure SQL Data Warehouse",
"status": "pending"
},
{
"is_primary": true,
"queue_id": 23248,
"role_display_name": "Data Engineer",
"role_slug": "data-engineer",
"skill_name": "Azure Stream Analytics",
"status": "pending"
},
{
"is_primary": false,
"queue_id": 23249,
"role_display_name": "Data Engineer",
"role_slug": "data-engineer",
"skill_name": "Lean",
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}
],
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"v3_pipeline_triggered": false,
"v3_role_slug": null,
"v3_run_id": null
}
}
API 2 — extract-details
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"alias_persisted": false,
"existing_alias_id": 311,
"existing_alias_text": "Informatica",
"input_term": "Informatica",
"matched_canonical": {
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"is_also_category": false,
"is_extractable": true,
"skill_nature": "PLATFORM",
"slug": "informatica",
"sub_category_id": 114,
"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"matched_via": "alias"
},
{
"alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
"alias_persisted": false,
"existing_alias_id": 407,
"existing_alias_text": "Azure",
"input_term": "Azure",
"matched_canonical": {
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"display_name": "Azure",
"id": 188,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "PLATFORM",
"slug": "azure",
"sub_category_id": 46,
"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"matched_via": "alias"
},
{
"alias_persist_skipped_reason": "TODO: REMOVE AFTER TESTING \u2014 alias DB write disabled",
"alias_persisted": false,
"existing_alias_id": 381,
"existing_alias_text": "Azure Blob Storage",
"input_term": "Azure Data Lake Storage",
"matched_canonical": {
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"display_name": "Azure Blob Storage",
"id": 172,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "CLOUD_SERVICE",
"slug": "azure-blob-storage",
"sub_category_id": 120,
"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"matched_via": "embedding_alias"
},
{
"alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
"alias_persisted": false,
"existing_alias_id": 302,
"existing_alias_text": "Azure Synapse Analytics",
"input_term": "Azure Synapse Analytics",
"matched_canonical": {
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"display_name": "Azure Synapse Analytics",
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"is_also_category": false,
"is_extractable": true,
"skill_nature": "CLOUD_SERVICE",
"slug": "azure-synapse-analytics",
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"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"matched_via": "alias"
},
{
"alias_persist_skipped_reason": "TODO: REMOVE AFTER TESTING \u2014 alias DB write disabled",
"alias_persisted": false,
"existing_alias_id": 302,
"existing_alias_text": "Azure Synapse Analytics",
"input_term": "Azure Synapse",
"matched_canonical": {
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"is_also_category": false,
"is_extractable": true,
"skill_nature": "CLOUD_SERVICE",
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"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"matched_via": "embedding_alias"
},
{
"alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
"alias_persisted": false,
"existing_alias_id": 67,
"existing_alias_text": "Python",
"input_term": "Python",
"matched_canonical": {
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"is_also_category": false,
"is_extractable": true,
"skill_nature": "LANGUAGE",
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"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"matched_via": "alias"
},
{
"alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
"alias_persisted": false,
"existing_alias_id": 271,
"existing_alias_text": "SQL",
"input_term": "SQL",
"matched_canonical": {
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"display_name": "SQL",
"id": 101,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "LANGUAGE",
"slug": "sql",
"sub_category_id": 97,
"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"matched_via": "alias"
},
{
"alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
"alias_persisted": false,
"existing_alias_id": 272,
"existing_alias_text": "Scala",
"input_term": "Scala",
"matched_canonical": {
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"display_name": "Scala",
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"is_also_category": false,
"is_extractable": true,
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"slug": "scala",
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"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"matched_via": "alias"
},
{
"alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
"alias_persisted": false,
"existing_alias_id": 2010,
"existing_alias_text": "Hadoop",
"input_term": "Hadoop",
"matched_canonical": {
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"display_name": "Hadoop",
"id": 1351,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "FRAMEWORK",
"slug": "hadoop",
"sub_category_id": 91,
"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"matched_via": "alias"
},
{
"alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
"alias_persisted": false,
"existing_alias_id": 2510,
"existing_alias_text": "spark",
"input_term": "Spark",
"matched_canonical": {
"category_id": 5,
"display_name": "Apache Spark",
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"is_also_category": false,
"is_extractable": true,
"skill_nature": "FRAMEWORK",
"slug": "apache-spark",
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"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"matched_via": "alias"
},
{
"alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
"alias_persisted": false,
"existing_alias_id": 4198,
"existing_alias_text": "Hive",
"input_term": "Hive",
"matched_canonical": {
"category_id": 3,
"display_name": "Hive",
"id": 2754,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "TOOL",
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"typical_lifespan": "EVERGREEN",
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},
"matched_via": "alias"
},
{
"alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
"alias_persisted": false,
"existing_alias_id": 360,
"existing_alias_text": "Power BI",
"input_term": "Power BI",
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"is_also_category": false,
"is_extractable": true,
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"slug": "power-bi",
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},
"matched_via": "alias"
},
{
"alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
"alias_persisted": false,
"existing_alias_id": 359,
"existing_alias_text": "Tableau",
"input_term": "Tableau",
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"display_name": "Tableau",
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"is_extractable": true,
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"typical_lifespan": "EVERGREEN",
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},
"matched_via": "alias"
}
],
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},
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},
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},
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},
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},
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},
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},
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},
{
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},
{
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},
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},
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},
{
"display_name": "AI Engineer",
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},
{
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},
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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}
],
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"rationale": "Exact alias hit on data-engineer (1.0) \u2014 no other alias at this confidence; skill_top data-engineer 0.38 does not contradict",
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},
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"dimension": {
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},
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]
},
{
"dimension": {
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{
"display_name": "Engineering Manager",
"id": 121,
"rationale": null,
"role_archetype": null,
"slug": "engineering-manager",
"source": "db"
}
]
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Programming Languages and Scripting",
"id": 59,
"rationale": "Languages used to write security automation, analysis scripts, detection logic, and remediation helpers. This is the primary implementation surface for a cybersecurity engineer across tooling and response workflows.",
"slug": "programming-languages-and-scripting",
"source": "db"
},
"input_skill": "Python",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Cyber Security Engineer",
"id": 5,
"rationale": null,
"role_archetype": null,
"slug": "cybersecurity-engineer",
"source": "db"
}
]
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Programming Languages for Data Work",
"id": 21,
"rationale": "Languages used to implement data pipelines, transformations, and operational glue. This is the primary coding surface for building ingestion, enrichment, and automation logic in data engineering.",
"slug": "programming-languages-for-data-work",
"source": "db"
},
"input_skill": "Python",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Data Engineer",
"id": 2,
"rationale": null,
"role_archetype": null,
"slug": "data-engineer",
"source": "db"
}
]
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Programming Languages for ML Systems",
"id": 39,
"rationale": "Languages used to build training code, inference services, evaluation jobs, and ML glue code. This is the primary implementation surface for ML engineers across experimentation and productionization.",
"slug": "programming-languages-for-ml-systems",
"source": "db"
},
"input_skill": "Python",
"llm_role": null,
"roles_from_db": [
{
"display_name": "ML Engineer",
"id": 3,
"rationale": null,
"role_archetype": null,
"slug": "ml-engineer",
"source": "db"
},
{
"display_name": "MLOps Engineer",
"id": 16,
"rationale": null,
"role_archetype": null,
"slug": "ml-ops-engineer",
"source": "db"
}
]
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Programming Languages for XR",
"id": 97,
"rationale": "Primary implementation languages used to build immersive client features, interaction logic, and device-specific runtime behavior. This is the core coding surface for AR/VR experiences.",
"slug": "programming-languages-for-xr",
"source": "db"
},
"input_skill": "Python",
"llm_role": null,
"roles_from_db": [
{
"display_name": "AR/VR Engineer",
"id": 8,
"rationale": null,
"role_archetype": null,
"slug": "ar-vr-engineer",
"source": "db"
}
]
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Python Programming",
"id": 290,
"rationale": "Core Python language skills used to implement backend business logic, request handlers, integrations, and service internals. This is the primary coding surface for the role.",
"slug": "python-programming",
"source": "db"
},
"input_skill": "Python",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Python Backend Developer",
"id": 80,
"rationale": null,
"role_archetype": "Engineering",
"slug": "python-backend-developer",
"source": "db"
}
]
}
],
"input_skill": "Python",
"matched_via": "alias",
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": null,
"source_tag": "db",
"was_in_llm_skills": true
},
{
"aliases_in_db": [
{
"alias_text": "SQL",
"alias_type": "CANONICAL",
"id": 271,
"is_primary": true,
"match_strategy": "CASE_INSENSITIVE"
}
],
"canonical": {
"category_id": 6,
"display_name": "SQL",
"id": 101,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "LANGUAGE",
"slug": "sql",
"sub_category_id": 97,
"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"dimensions": [
{
"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"
},
"input_skill": "SQL",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Pega Developer",
"id": 24,
"rationale": null,
"role_archetype": null,
"slug": "pega-developer",
"source": "db"
}
]
},
{
"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"
},
"input_skill": "SQL",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Engineering Manager",
"id": 121,
"rationale": null,
"role_archetype": null,
"slug": "engineering-manager",
"source": "db"
}
]
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Programming Languages for Data Work",
"id": 21,
"rationale": "Languages used to implement data pipelines, transformations, and operational glue. This is the primary coding surface for building ingestion, enrichment, and automation logic in data engineering.",
"slug": "programming-languages-for-data-work",
"source": "db"
},
"input_skill": "SQL",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Data Engineer",
"id": 2,
"rationale": null,
"role_archetype": null,
"slug": "data-engineer",
"source": "db"
}
]
}
],
"input_skill": "SQL",
"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": "Scala",
"alias_type": "CANONICAL",
"id": 272,
"is_primary": true,
"match_strategy": "CASE_INSENSITIVE"
}
],
"canonical": {
"category_id": 6,
"display_name": "Scala",
"id": 102,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "LANGUAGE",
"slug": "scala",
"sub_category_id": 96,
"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"dimensions": [
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Programming Languages for Data Work",
"id": 21,
"rationale": "Languages used to implement data pipelines, transformations, and operational glue. This is the primary coding surface for building ingestion, enrichment, and automation logic in data engineering.",
"slug": "programming-languages-for-data-work",
"source": "db"
},
"input_skill": "Scala",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Data Engineer",
"id": 2,
"rationale": null,
"role_archetype": null,
"slug": "data-engineer",
"source": "db"
}
]
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Programming Languages for ML Systems",
"id": 39,
"rationale": "Languages used to build training code, inference services, evaluation jobs, and ML glue code. This is the primary implementation surface for ML engineers across experimentation and productionization.",
"slug": "programming-languages-for-ml-systems",
"source": "db"
},
"input_skill": "Scala",
"llm_role": null,
"roles_from_db": [
{
"display_name": "ML Engineer",
"id": 3,
"rationale": null,
"role_archetype": null,
"slug": "ml-engineer",
"source": "db"
},
{
"display_name": "MLOps Engineer",
"id": 16,
"rationale": null,
"role_archetype": null,
"slug": "ml-ops-engineer",
"source": "db"
}
]
}
],
"input_skill": "Scala",
"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": "Hadoop",
"alias_type": "CANONICAL",
"id": 2010,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
}
],
"canonical": {
"category_id": 5,
"display_name": "Hadoop",
"id": 1351,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "FRAMEWORK",
"slug": "hadoop",
"sub_category_id": 91,
"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"dimensions": [
{
"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"
},
"input_skill": "Hadoop",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Data Engineer",
"id": 2,
"rationale": null,
"role_archetype": null,
"slug": "data-engineer",
"source": "db"
}
]
}
],
"input_skill": "Hadoop",
"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": "Apache Spark",
"alias_type": "CANONICAL",
"id": 2004,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "apache spark 3",
"alias_type": "VERSION",
"id": 2006,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "spark",
"alias_type": "VERSION",
"id": 2510,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "spark 3",
"alias_type": "VERSION",
"id": 2007,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "spark 3.x",
"alias_type": "VERSION",
"id": 2009,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "spark3",
"alias_type": "VERSION",
"id": 2008,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
}
],
"canonical": {
"category_id": 5,
"display_name": "Apache Spark",
"id": 1350,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "FRAMEWORK",
"slug": "apache-spark",
"sub_category_id": 1021,
"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"dimensions": [
{
"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"
},
"input_skill": "Spark",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Data Engineer",
"id": 2,
"rationale": null,
"role_archetype": null,
"slug": "data-engineer",
"source": "db"
}
]
}
],
"input_skill": "Spark",
"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": "Hive",
"alias_type": "CANONICAL",
"id": 4198,
"is_primary": true,
"match_strategy": "CASE_INSENSITIVE"
}
],
"canonical": {
"category_id": 3,
"display_name": "Hive",
"id": 2754,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "TOOL",
"slug": "hive",
"sub_category_id": 2242,
"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"dimensions": [
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Local Persistence and Offline Behavior",
"id": 85,
"rationale": "On-device storage used for caching, offline support, and durable client state. This cluster is coherent because iOS apps often need to preserve user progress and data when connectivity is limited.",
"slug": "local-persistence-and-offline-behavior",
"source": "db"
},
"input_skill": "Hive",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Android Developer",
"id": 4,
"rationale": null,
"role_archetype": null,
"slug": "android-engineer",
"source": "db"
},
{
"display_name": "Flutter Developer",
"id": 74,
"rationale": null,
"role_archetype": "Engineering",
"slug": "flutter-developer",
"source": "db"
},
{
"display_name": "Hybrid Mobile Developer",
"id": 11,
"rationale": null,
"role_archetype": null,
"slug": "hybrid-mobile-developer",
"source": "db"
},
{
"display_name": "Native Mobile Developer",
"id": 75,
"rationale": null,
"role_archetype": "Engineering",
"slug": "native-mobile-developer",
"source": "db"
},
{
"display_name": "React Native Developer",
"id": 73,
"rationale": null,
"role_archetype": "Engineering",
"slug": "react-native-developer",
"source": "db"
},
{
"display_name": "iOS Developer",
"id": 6,
"rationale": null,
"role_archetype": null,
"slug": "ios-engineer",
"source": "db"
}
]
}
],
"input_skill": "Hive",
"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": "Power BI",
"alias_type": "CANONICAL",
"id": 360,
"is_primary": true,
"match_strategy": "CASE_INSENSITIVE"
}
],
"canonical": {
"category_id": 9,
"display_name": "Power BI",
"id": 151,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "PLATFORM",
"slug": "power-bi",
"sub_category_id": 111,
"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"dimensions": [
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "BI and Visualization Tools",
"id": 31,
"rationale": "Tools used to expose curated data to analysts and business users through dashboards, reports, and semantic exploration. Data engineers support these tools by shaping reliable datasets and performant models.",
"slug": "bi-and-visualization-tools",
"source": "db"
},
"input_skill": "Power BI",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Data Engineer",
"id": 2,
"rationale": null,
"role_archetype": null,
"slug": "data-engineer",
"source": "db"
}
]
}
],
"input_skill": "Power BI",
"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": "Tableau",
"alias_type": "CANONICAL",
"id": 359,
"is_primary": true,
"match_strategy": "CASE_INSENSITIVE"
}
],
"canonical": {
"category_id": 9,
"display_name": "Tableau",
"id": 150,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "PLATFORM",
"slug": "tableau",
"sub_category_id": 111,
"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"dimensions": [
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "BI and Visualization Tools",
"id": 31,
"rationale": "Tools used to expose curated data to analysts and business users through dashboards, reports, and semantic exploration. Data engineers support these tools by shaping reliable datasets and performant models.",
"slug": "bi-and-visualization-tools",
"source": "db"
},
"input_skill": "Tableau",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Data Engineer",
"id": 2,
"rationale": null,
"role_archetype": null,
"slug": "data-engineer",
"source": "db"
}
]
}
],
"input_skill": "Tableau",
"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": "Lean",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": {
"derived": {
"category": "Practices",
"skill_nature": "PRACTICE",
"sub_category": "general",
"typical_lifespan": "MULTI_YEAR",
"version_strategy": "UNVERSIONED",
"volatility": "MEDIUM"
},
"enrichment": null,
"keep_log": [],
"locked_dimensions": [],
"merge_log": [],
"placed": null,
"relationships": null,
"skill_id": "lean",
"split_log": [],
"typed": null,
"warnings": []
},
"source_tag": "llm",
"was_in_llm_skills": true
}
],
"unmatched_skills": [
"Informatica PowerCenter",
"Informatica Cloud",
"IICS",
"Azure Data Factory",
"Azure Databricks",
"Azure SQL Database",
"Azure SQL Data Warehouse",
"Azure Stream Analytics",
"Lean"
]
}
API 3 — final-role-output
{
"chosen_role": {
"display_name": "Data Engineer",
"id": 2,
"rationale": "Exact alias hit on data-engineer (1.0) \u2014 no other alias at this confidence; skill_top data-engineer 0.38 does not contradict",
"role_archetype": null,
"slug": "data-engineer",
"source": "db"
},
"chosen_role_resolution": "in_db",
"final_input_skills": [
{
"skill": "Informatica",
"tag": "in_db"
},
{
"skill": "Informatica PowerCenter",
"tag": "new"
},
{
"skill": "Informatica Cloud",
"tag": "new"
},
{
"skill": "IICS",
"tag": "new"
},
{
"skill": "Azure",
"tag": "in_db"
},
{
"skill": "Azure Data Lake Storage",
"tag": "in_db"
},
{
"skill": "Azure Synapse Analytics",
"tag": "in_db"
},
{
"skill": "Azure Synapse",
"tag": "in_db"
},
{
"skill": "Azure Data Factory",
"tag": "new"
},
{
"skill": "Azure Databricks",
"tag": "new"
},
{
"skill": "Azure SQL Database",
"tag": "new"
},
{
"skill": "Azure SQL Data Warehouse",
"tag": "new"
},
{
"skill": "Azure Stream Analytics",
"tag": "new"
},
{
"skill": "Python",
"tag": "in_db"
},
{
"skill": "SQL",
"tag": "in_db"
},
{
"skill": "Scala",
"tag": "in_db"
},
{
"skill": "Hadoop",
"tag": "in_db"
},
{
"skill": "Spark",
"tag": "in_db"
},
{
"skill": "Hive",
"tag": "in_db"
},
{
"skill": "Power BI",
"tag": "in_db"
},
{
"skill": "Tableau",
"tag": "in_db"
},
{
"skill": "Lean",
"tag": "new"
}
],
"llm_cost_api1_usd": null,
"llm_cost_api2_usd": null,
"llm_cost_api3_usd": null,
"llm_cost_total_usd": null,
"persistence": {
"items": [
{
"chosen_role_id": 2,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "ETL and ELT Tooling",
"id": 24,
"rationale": "Packaged tools for extracting, loading, and transforming data across systems. This dimension covers connector-based ingestion, transformation frameworks, and managed integration products.",
"slug": "etl-and-elt-tooling",
"source": "db"
},
"dimension_id": 24,
"input_skill": "Informatica",
"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": 117,
"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": "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,
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{
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{
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{
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{
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{
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},
{
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{
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},
{
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},
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{
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{
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{
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],
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{
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},
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{
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{
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},
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{
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{
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{
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},
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{
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},
{
"display_name": "Cyber Security Engineer",
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"skill_id": 188,
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"skipped_reason": null
},
{
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"source": "db"
},
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{
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},
{
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"source": "db"
},
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"matched_chosen_role": false,
"outcome_line": "Skipped \u2014 no persistable v3 meta for new skill",
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{
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}
],
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{
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},
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"matched_chosen_role": true,
"outcome_line": "Skipped \u2014 no persistable v3 meta for new skill",
"role_dimension_saved": false,
"roles_from_db": [
{
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}
],
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{
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},
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"roles_from_db": [
{
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{
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},
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"roles_from_db": [
{
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}
],
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{
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},
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"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
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{
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],
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},
{
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},
"dimension_id": 1,
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"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
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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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"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
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{
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},
{
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"slug": "programming-languages-for-data-work",
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},
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{
"display_name": "Data Engineer",
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"role_archetype": null,
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}
],
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},
{
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},
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"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
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{
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{
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},
{
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},
"dimension_id": 97,
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"roles_from_db": [
{
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],
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},
{
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"slug": "python-programming",
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},
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"roles_from_db": [
{
"display_name": "Python Backend Developer",
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],
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},
{
"chosen_role_id": 2,
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"display_name": "Pega Programming Languages \u0026 DSLs",
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"source": "db"
},
"dimension_id": 267,
"input_skill": "SQL",
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"matched_chosen_role": false,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "Pega Developer",
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}
],
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},
{
"chosen_role_id": 2,
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"display_name": "Programming Languages \u0026 DSLs",
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},
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"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": [
{
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],
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},
{
"chosen_role_id": 2,
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"difficulty_hint": "well_known",
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"slug": "programming-languages-for-data-work",
"source": "db"
},
"dimension_id": 21,
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"matched_chosen_role": true,
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"role_dimension_saved": true,
"roles_from_db": [
{
"display_name": "Data Engineer",
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],
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},
{
"chosen_role_id": 2,
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"difficulty_hint": "well_known",
"display_name": "Programming Languages for Data Work",
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"slug": "programming-languages-for-data-work",
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},
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"input_skill": "Scala",
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"matched_chosen_role": true,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension saved",
"role_dimension_saved": true,
"roles_from_db": [
{
"display_name": "Data Engineer",
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