Pipeline run
f02c4d28-b3d4-43c4-b2c6-65025ec98744
Client output enrichment
v2 Skill cluster · Nature of work · AI index · Tech stack maturity · Evidence · KRA descriptionvocab breakdown (legacy)
Signals
Post-classification
Captured for admin review
1 POST /skills/extract-from-jd
2 POST /skills/extract-details
3 POST /skills/final-role-output
Data Engineer
domain · Data Engineering & Analytics CASE DOMAINslug: data-engineer · id: 2 · source: db
Domain=Data Engineering & Analytics; The JD centers on building and operating scalable data pipelines, ETL/ELT, orchestration, infrastructure, and data platform tooling, which aligns most strongly with Data Engineer.
Matched skills
Matched dimensions
Matched KRAs
Resolution:
in_db
— role exists in library; skill↔dim and role↔dim links saved when applicable.
Job description
At EY, we’re all in to shape your future with confidence. We’ll help you succeed in a globally connected powerhouse of diverse teams and take your career wherever you want it to go. Join EY and help to build a better working world. Designation-Data Engineer Job Description • Develop and maintain scalable data pipelines using tools like Apache Kafka, Apache Spark, or AWS Glue to ingest, process, and transform large datasets from various sources, ensuring efficient data flow and processing. • Design and implement data models and schemas in data warehouses (e.g., Amazon Redshift, Google BigQuery) and data lakes (e.g., AWS S3, Azure Data Lake) to support analytics and reporting needs. • Collaborate with data scientists and analysts to understand data requirements, ensuring data availability and accessibility for analytics, machine learning, and reporting. • Utilize ETL tools and frameworks (e.g., Apache NiFi, Talend, or custom Python scripts) to automate data extraction, transformation, and loading processes, ensuring data quality and integrity. • Monitor and optimize data pipeline performance using tools like Apache Airflow or AWS Step Functions, implementing best practices for data processing and workflow management. • Write, test, and maintain scripts in Python, SQL, or Bash for data processing, automation tasks, and data validation, ensuring high code quality and performance. • Implement CI/CD practices for data engineering workflows using tools like Jenkins, GitLab CI, or Azure DevOps, automating the deployment of data pipelines and infrastructure changes. • Collaborate with DevOps teams to integrate data solutions into existing infrastructure, leveraging Infrastructure as Code (IaC) tools like Terraform or AWS CloudFormation for provisioning and managing resources. • Manage containerized data applications using Docker and orchestrate them with Kubernetes, ensuring scalability and reliability of data processing applications. • Implement monitoring and logging solutions using tools like Prometheus, Grafana, or ELK Stack to track data pipeline performance, troubleshoot issues, and ensure data quality. • Ensure compliance with data governance, security best practices, and data privacy regulations, embedding DevSecOps principles in data workflows. • Participate in code reviews and contribute to the development of best practices for data engineering, data quality, and DevOps methodologies. • Mentor junior data engineers, providing guidance on data engineering practices, data architecture, and DevOps tools and techniques. • Contribute to the documentation of data architecture, processes, and workflows for knowledge sharing, compliance, and onboarding purposes. • Demonstrate strong communication skills to collaborate effectively with cross-functional teams, including data science, analytics, and business stakeholders. Desired Profile • Seeking a DevOps Engineer with 5+ years of hands-on Cloud and DevOps experience, including significant leadership. Requires a Bachelor's/master’s in computer science. • Must have expert proficiency in Terraform and extensive experience across at least two major cloud platforms (AWS, Azure, GCP). Strong hands-on experience with Kubernetes, Helm charts, and designing/optimizing CI/CD pipelines (e.g., Jenkins, GitLab CI) is essential. Proficiency in Python and scripting (Bash/PowerShell) is also a must. • Valued experience includes leading cloud migrations, contributing to RFP/RFI processes, and mentoring teams. Excellent problem-solving, communication, and collaboration skills are critical. Experience with configuration management (Ansible, Puppet) and DevSecOps principles is required; OpenShift is a plus. Experience • 10 years and above Education • B.Tech. / BS in Computer Science Technical Skills & Certifications • Certifications in cloud platforms (e.g., AWS Certified Solutions Architect, Azure Administrator, Google Professional Cloud Architect). • Terraform, Kubernetes, Python, CI/CD, Ansible, Security tools, Monitoring tools. EY | Building a better working world EY is building a better working world by creating new value for clients, people, society and the planet, while building trust in capital markets. Enabled by data, AI and advanced technology, EY teams help clients shape the future with confidence and develop answers for the most pressing issues of today and tomorrow. EY teams work across a full spectrum of services in assurance, consulting, tax, strategy and transactions. Fueled by sector insights, a globally connected, multi-disciplinary network and diverse ecosystem partners, EY teams can provide services in more than 150 countries and territories.
Skills from this JD
Each row merges API 1 extraction, API 2 library match / v3 orchestration (dimensions + locked dims), and API 3 persistence tags.
Aliases — catalog
- Apache Kafka (CANONICAL) primary
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Tool
- Sub-category
- Event Streaming Tool
- Vendor
- Apache Software Foundation
- License
- apache_2
- Year introduced
- 2011
- Confidence
- 0.90
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Apache Kafka is broadly adopted in production and appears frequently in job descriptions for event streaming, data pipelines, and microservices; it remains a common hiring-pipeline staple across backend and platform roles.
Skill profile (library / DB)
- Skill nature
- TOOL
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 13
- Sub-category id
- 128
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
Messaging and Event Streaming Catalog dimension db id 8
Library dimension (catalog)
Roles linked in library: Backend Developer, Data Engineer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Messaging and Event Streaming
messaging-and-event-streaming
|
✓ | ✓ | 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 |
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Cloud Platforms
- Sub-category
- general
- Skill nature
- PLATFORM
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Aliases — catalog
- Amazon Redshift (CANONICAL) primary
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Service
- Sub-category
- Data Warehouse Service
- Vendor
- Amazon Web Services
- License
- proprietary
- Year introduced
- 2012
- Confidence
- 0.97
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Commonly listed in data/analytics job descriptions and widely used as AWS’s managed warehouse; strong vendor adoption and steady JD volume signal broad market demand.
Skill profile (library / DB)
- Skill nature
- CLOUD_SERVICE
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 11
- Sub-category id
- 118
- 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
- BigQuery (CANONICAL) primary
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Service
- Sub-category
- Data Warehouse Service
- Vendor
- License
- proprietary
- Year introduced
- 2011
- Confidence
- 0.98
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: BigQuery appears frequently in data/analytics job descriptions and is a core Google Cloud warehouse offering, with broad enterprise adoption and strong ecosystem support.
Skill profile (library / DB)
- Skill nature
- CLOUD_SERVICE
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 11
- Sub-category id
- 118
- 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
|
Aliases — catalog
- AWS S3 (CANONICAL) primary
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Service
- Sub-category
- Object Storage Service
- Vendor
- Amazon Web Services
- License
- proprietary
- Year introduced
- 2006
- Confidence
- 0.99
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: AWS S3 is a core cloud storage service routinely listed in cloud/data engineering JDs and remains a standard AWS offering with broad ecosystem support; no vendor sunset or replacement signal exists.
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 Platforms & Hosting Providers Catalog dimension db id 278
Library dimension (catalog)
Roles linked in library: .NET Backend Developer, Kotlin Backend Developer, Scala Backend Developer, Web Developer
-
Cloud Platforms & Managed Services Catalog dimension db id 221
Library dimension (catalog)
Roles linked in library: Fullstack Developer, Go Backend Developer, Node.js Backend Developer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Cloud Platforms & Hosting Providers
cloud-platforms-hosting-providers
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Cloud Platforms & Managed Services
cloud-platforms-managed-services
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Cloud Platforms
- Sub-category
- 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
- TOOL
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Data Engineering Tools
- Sub-category
- general
- Skill nature
- TOOL
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Aliases — catalog
- Python (CANONICAL) primary
- Python 2 (VERSION)
- Python 2.x (VERSION)
- Python 3 (VERSION)
- Python 3.10 (VERSION)
- Python 3.11 (VERSION)
- Python 3.12 (VERSION)
- Python 3.x (VERSION)
- py (VERSION)
- py2 (VERSION)
- py3 (VERSION)
- python 3 (VERSION)
- python 3.x (VERSION)
- python2 (VERSION)
- python3 (VERSION)
- python3.x (VERSION)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Language
- Sub-category
- Programming Language
- Vendor
- PSF
- License
- mit
- Year introduced
- 1991
- Confidence
- 0.99
- Version strategy
- SEPARATE_ENTITY
- Version tag
- 3
Maturity reasoning: Python appears in a very high volume of job descriptions across data, backend, automation, and ML roles, and remains a default hiring-pipeline language on major job boards and tech stacks.
Skill profile (library / DB)
- Skill nature
- LANGUAGE
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 6
- Sub-category id
- 96
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
Cloud Security Scripting & DSL Languages Catalog dimension db id 248
Library dimension (catalog)
Roles linked in library: Cloud Security Engineer
-
Programming Languages Catalog dimension db id 1
Library dimension (catalog)
Roles linked in library: Backend Developer, Fullstack Developer, Fullstack Developer
-
Programming Languages and Scripting Catalog dimension db id 59
Library dimension (catalog)
Roles linked in library: Cyber Security Engineer
-
Programming Languages for Data Work Catalog dimension db id 21
Library dimension (catalog)
Roles linked in library: Data Engineer
-
Programming Languages for ML Systems Catalog dimension db id 39
Library dimension (catalog)
Roles linked in library: ML Engineer, MLOps Engineer
-
Programming Languages for XR Catalog dimension db id 97
Library dimension (catalog)
Roles linked in library: AR/VR Engineer
-
Python Programming Catalog dimension db id 290
Library dimension (catalog)
Roles linked in library: Python Backend Developer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Cloud Security Scripting & DSL Languages
cloud-security-scripting-dsl-languages
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Programming Languages
programming-languages
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Programming Languages and Scripting
programming-languages-and-scripting
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Programming Languages for Data Work
programming-languages-for-data-work
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved |
|
Programming Languages for ML Systems
programming-languages-for-ml-systems
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Programming Languages for XR
programming-languages-for-xr
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Python Programming
python-programming
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Aliases — catalog
- SQL (CANONICAL) primary
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Language
- Sub-category
- Query Language
- Vendor
- ANSI
- License
- unknown
- Year introduced
- 1974
- Confidence
- 0.99
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: SQL appears in a large share of data, backend, and analytics job descriptions and remains the default query language for PostgreSQL, MySQL, and cloud warehouses like Snowflake/BigQuery.
Skill profile (library / DB)
- Skill nature
- LANGUAGE
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 6
- Sub-category id
- 97
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
Pega Programming Languages & DSLs Catalog dimension db id 267
Library dimension (catalog)
Roles linked in library: Pega Developer
-
Programming Languages for Data Work Catalog dimension db id 21
Library dimension (catalog)
Roles linked in library: Data Engineer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Pega Programming Languages & DSLs
pega-programming-languages-dsls
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Programming Languages for Data Work
programming-languages-for-data-work
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved |
Aliases — catalog
- Bash (VERSION)
- Bash 3.x (VERSION)
- Bash 4.x (VERSION)
- Bash 5.x (VERSION)
- GNU Bash (VERSION)
- bash (VERSION)
- bash 3 (VERSION)
- bash 3.x (VERSION)
- bash 4 (VERSION)
- bash 4.x (VERSION)
- bash 5 (VERSION)
- bash 5.x (VERSION)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Language
- Sub-category
- Shell Language
- Vendor
- GNU Project
- License
- gpl_v3
- Year introduced
- 1989
- Confidence
- 0.99
- Version strategy
- SEPARATE_ENTITY
- Version tag
- 5.x
Maturity reasoning: Bash appears in many DevOps, SRE, and Linux admin job descriptions and remains the default shell on most Unix-like systems, with no vendor sunset or clear replacement in mainstream hiring.
Skill profile (library / DB)
- Skill nature
- LANGUAGE
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 6
- Sub-category id
- 238
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
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
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
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 |
Aliases — catalog
- Apache Airflow (CANONICAL) primary
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Tool
- Sub-category
- Workflow Orchestration Tool
- Vendor
- Apache Software Foundation
- License
- apache_2
- Year introduced
- 2015
- Confidence
- 0.98
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Frequently listed in data engineering JDs and widely adopted for workflow orchestration; strong GitHub activity and managed offerings from AWS/GCP/Azure signal broad market demand.
Skill profile (library / DB)
- Skill nature
- TOOL
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 13
- Sub-category id
- 130
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
Data Pipeline Orchestration Catalog dimension db id 23
Library dimension (catalog)
Roles linked in library: Data Engineer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Data Pipeline Orchestration
data-pipeline-orchestration
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved |
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Cloud Platforms
- Sub-category
- general
- Skill nature
- PLATFORM
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Aliases — catalog
- Jenkins (CANONICAL) primary
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Tool
- Sub-category
- Ci Cd Tool
- Vendor
- CloudBees
- License
- mit
- Year introduced
- 2011
- Confidence
- 0.99
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Jenkins remains a common CI/CD requirement in job postings and enterprise DevOps stacks, with broad plugin ecosystem and long-running GitHub activity despite newer alternatives.
Skill profile (library / DB)
- Skill nature
- TOOL
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 13
- Sub-category id
- 184
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
CI/CD Pipeline Platforms Catalog dimension db id 150
Library dimension (catalog)
Roles linked in library: DevOps Engineer
-
CI/CD for Machine Learning Catalog dimension db id 56
Library dimension (catalog)
Roles linked in library: ML Engineer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
CI/CD Pipeline Platforms
ci-cd-pipeline-platforms
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
CI/CD for Machine Learning
ci-cd-for-machine-learning
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Aliases — catalog
- GitLab CI (CANONICAL) primary
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Service
- Sub-category
- Ci Cd Service
- Vendor
- GitLab Inc.
- License
- mit
- Year introduced
- 2011
- Confidence
- 0.93
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Commonly listed in DevOps/CI-CD job descriptions and widely used in GitLab-hosted pipelines; strong market presence alongside Jenkins/GitHub Actions rather than a niche tool.
Skill profile (library / DB)
- Skill nature
- CLOUD_SERVICE
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 11
- Sub-category id
- 178
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
CI/CD Pipeline Platforms Catalog dimension db id 150
Library dimension (catalog)
Roles linked in library: DevOps Engineer
-
CI/CD for Machine Learning Catalog dimension db id 56
Library dimension (catalog)
Roles linked in library: ML Engineer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
CI/CD Pipeline Platforms
ci-cd-pipeline-platforms
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
CI/CD for Machine Learning
ci-cd-for-machine-learning
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Aliases — catalog
- Azure DevOps (CANONICAL)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Platform
- Sub-category
- Devops Platform
- Vendor
- Microsoft
- License
- proprietary
- Year introduced
- 2018
- Confidence
- 0.93
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Azure DevOps appears in many enterprise job descriptions for CI/CD, boards, and repos, and Microsoft continues active product support and updates; it remains a common hiring-pipeline skill alongside GitHub Actions/Jenkins.
Skill profile (library / DB)
- Skill nature
- PLATFORM
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 9
- Sub-category id
- 170
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
CI/CD Pipeline Platforms Catalog dimension db id 150
Library dimension (catalog)
Roles linked in library: DevOps Engineer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
CI/CD Pipeline Platforms
ci-cd-pipeline-platforms
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Aliases — catalog
- Terraform (CANONICAL) primary
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Tool
- Sub-category
- Infrastructure As Code Tool
- Vendor
- HashiCorp
- License
- mpl
- Year introduced
- 2014
- Confidence
- 0.93
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Terraform is broadly listed in DevOps/SRE/cloud JDs and remains a standard IaC tool across AWS/Azure/GCP; HashiCorp’s ecosystem and widespread GitHub usage signal strong market adoption.
Skill profile (library / DB)
- Skill nature
- TOOL
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 13
- Sub-category id
- 191
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
Infrastructure & Security Automation Frameworks Catalog dimension db id 249
Library dimension (catalog)
Roles linked in library: Cloud Security Engineer
-
Infrastructure as Code Catalog dimension db id 132
Library dimension (catalog)
Roles linked in library: Cloud Architect, DevOps Engineer
-
Infrastructure as Code for ML Catalog dimension db id 57
Library dimension (catalog)
Roles linked in library: ML Engineer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Infrastructure & Security Automation Frameworks
infrastructure-security-automation-frameworks
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Infrastructure as Code
infrastructure-as-code
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Infrastructure as Code for ML
infrastructure-as-code-for-ml
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Aliases — catalog
- AWS CloudFormation (CANONICAL) primary
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Service
- Sub-category
- Infrastructure As Code Service
- Vendor
- Amazon Web Services
- License
- proprietary
- Year introduced
- 2011
- Confidence
- 0.90
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Common IaC skill in AWS job postings and widely used for provisioning; AWS continues to maintain and promote CloudFormation alongside newer tools like CDK, indicating strong market presence.
Skill profile (library / DB)
- Skill nature
- CLOUD_SERVICE
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 11
- Sub-category id
- 181
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
Infrastructure as Code Catalog dimension db id 132
Library dimension (catalog)
Roles linked in library: Cloud Architect, DevOps Engineer
-
Infrastructure as Code for ML Catalog dimension db id 57
Library dimension (catalog)
Roles linked in library: ML Engineer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Infrastructure as Code
infrastructure-as-code
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Infrastructure as Code for ML
infrastructure-as-code-for-ml
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Aliases — catalog
- Docker (CANONICAL) primary
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Tool
- Sub-category
- Containerization Tool
- Vendor
- Docker, Inc.
- License
- apache_2
- Year introduced
- 2013
- Confidence
- 0.98
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Docker is a hiring-pipeline staple: it appears in many DevOps, backend, and platform JDs, and remains a standard containerization tool alongside Kubernetes in production stacks.
Skill profile (library / DB)
- Skill nature
- TOOL
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 13
- Sub-category id
- 63
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
Containerization and Image Builds Catalog dimension db id 152
Library dimension (catalog)
Roles linked in library: DevOps Engineer
-
Deployment and Cloud Platforms Catalog dimension db id 418
Library dimension (catalog)
Roles linked in library: Ruby Backend Developer
-
Deployment and Runtime Configuration Catalog dimension db id 13
Library dimension (catalog)
Roles linked in library: .NET Backend Developer, Backend Developer, Go Backend Developer, PHP Backend Developer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Containerization and Image Builds
containerization-and-image-builds
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Deployment and Cloud Platforms
deployment-and-cloud-platforms
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Deployment and Runtime Configuration
deployment-and-runtime-configuration
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Aliases — catalog
- Kubernetes (CANONICAL) primary
- Kubernetes 1.0+ (VERSION)
- Kubernetes 1.x (VERSION)
- Kubernetes v1 (VERSION)
- k8s (VERSION)
- kubernetes 1.x (VERSION)
- kubernetes latest (VERSION)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Platform
- Sub-category
- Container Orchestration Platform
- Vendor
- Cloud Native Computing Foundation
- License
- apache_2
- Year introduced
- 2014
- Confidence
- 0.90
- Version strategy
- SEPARATE_ENTITY
- Version tag
- 1.30
Maturity reasoning: Broadly adopted in cloud-native stacks; Kubernetes appears in a large share of DevOps/SRE job descriptions and is the default orchestration platform across major cloud vendors.
Skill profile (library / DB)
- Skill nature
- PLATFORM
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 9
- Sub-category id
- 557
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
Container Orchestration Platforms Catalog dimension db id 134
Library dimension (catalog)
Roles linked in library: Cloud Architect, DevOps Engineer
-
Kubernetes for ML Workloads Catalog dimension db id 47
Library dimension (catalog)
Roles linked in library: ML Engineer, MLOps Engineer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Container Orchestration Platforms
container-orchestration-platforms
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Kubernetes for ML Workloads
kubernetes-for-ml-workloads
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Aliases — catalog
- Prometheus (CANONICAL) primary
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Platform
- Sub-category
- Monitoring Platform
- Vendor
- Cloud Native Computing Foundation
- License
- apache_2
- Year introduced
- 2012
- Confidence
- 0.62
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Prometheus is widely listed in DevOps/SRE job descriptions and is a standard CNCF monitoring stack component, often paired with Grafana for production observability.
Skill profile (library / DB)
- Skill nature
- PLATFORM
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 9
- Sub-category id
- 2837
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
Backend Observability, Logging, and Diagnostics Catalog dimension db id 388
Library dimension (catalog)
Roles linked in library: Kotlin Backend Developer, Scala Backend Developer
-
Observability and Incident Response Catalog dimension db id 10
Library dimension (catalog)
Roles linked in library: .NET Backend Developer, Backend Developer, Node.js Backend Developer, PHP Backend Developer
-
Observability and Incident Triage Catalog dimension db id 155
Library dimension (catalog)
Roles linked in library: DevOps Engineer
-
Observability and Operations Catalog dimension db id 143
Library dimension (catalog)
Roles linked in library: Cloud Architect
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Backend Observability, Logging, and Diagnostics
backend-observability-logging-and-diagnostics
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Observability and Incident Response
observability-and-incident-response
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Observability and Incident Triage
observability-and-incident-triage
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Observability and Operations
observability-and-operations
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Aliases — catalog
- Grafana (CANONICAL) primary
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Platform
- Sub-category
- Observability Platform
- Vendor
- Grafana Labs
- License
- apache_2
- Year introduced
- 2014
- Confidence
- 0.90
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Grafana appears in many DevOps/SRE job descriptions and is a standard observability dashboarding tool alongside Prometheus and Loki; strong GitHub/community adoption and broad vendor integrations signal mainstream use.
Skill profile (library / DB)
- Skill nature
- PLATFORM
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 9
- Sub-category id
- 176
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
Backend Observability, Logging, and Diagnostics Catalog dimension db id 388
Library dimension (catalog)
Roles linked in library: Kotlin Backend Developer, Scala Backend Developer
-
Observability and Incident Response Catalog dimension db id 10
Library dimension (catalog)
Roles linked in library: .NET Backend Developer, Backend Developer, Node.js Backend Developer, PHP Backend Developer
-
Observability and Incident Triage Catalog dimension db id 155
Library dimension (catalog)
Roles linked in library: DevOps Engineer
-
Observability and Operations Catalog dimension db id 143
Library dimension (catalog)
Roles linked in library: Cloud Architect
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Backend Observability, Logging, and Diagnostics
backend-observability-logging-and-diagnostics
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Observability and Incident Response
observability-and-incident-response
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Observability and Incident Triage
observability-and-incident-triage
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Observability and Operations
observability-and-operations
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Monitoring Tools
- Sub-category
- general
- Skill nature
- TOOL
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Aliases — catalog
- DevOps (CANONICAL)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Methodology
- Sub-category
- Devops Methodology
- Confidence
- 0.97
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: DevOps appears in a large share of software and platform engineering job descriptions, often alongside CI/CD, Kubernetes, and cloud tooling; it is a standard hiring-pipeline keyword rather than a niche specialty.
Skill profile (library / DB)
- Skill nature
- METHODOLOGY
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 8
- Sub-category id
- 922
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
CI/CD Pipeline Platforms Catalog dimension db id 150
Library dimension (catalog)
Roles linked in library: DevOps Engineer
-
Deployment and Release Patterns Catalog dimension db id 140
Library dimension (catalog)
Roles linked in library: Cloud Architect
-
Infrastructure as Code Catalog dimension db id 132
Library dimension (catalog)
Roles linked in library: Cloud Architect, DevOps Engineer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
CI/CD Pipeline Platforms
ci-cd-pipeline-platforms
|
— | — |
Skipped — no persistable v3 meta for new skill
skill_not_in_db_v3_proposed
|
|
Deployment and Release Patterns
deployment-and-release-patterns
|
— | — |
Skipped — no persistable v3 meta for new skill
skill_not_in_db_v3_proposed
|
|
Infrastructure as Code
infrastructure-as-code
|
— | — |
Skipped — no persistable v3 meta for new skill
skill_not_in_db_v3_proposed
|
Aliases — catalog
- Code Review (CANONICAL)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- SoftSkill
- Sub-category
- Code Review
- Confidence
- 0.96
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Code review is a standard hiring-pipeline requirement in engineering JDs and is built into major platforms like GitHub/GitLab pull-request workflows, indicating broad adoption.
Skill profile (library / DB)
- Skill nature
- PRACTICE
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 58
- Sub-category id
- 364
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
React Frontend Development Catalog dimension db id 96
Library dimension (catalog)
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
React Frontend Development
d_init_01
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Aliases — catalog
- AWS (CANONICAL) primary
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Platform
- Sub-category
- Cloud Platform
- Vendor
- Amazon
- License
- other_open
- Year introduced
- 2006
- Confidence
- 0.99
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: AWS is a hiring-pipeline staple: it appears in a large share of cloud/DevOps job descriptions and dominates public cloud market share, with broad certification and vendor ecosystem support.
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 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
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Cloud Platforms
cloud-platforms
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved |
|
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) |
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
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) |
Aliases — catalog
- GCP (CANONICAL) primary
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Platform
- Sub-category
- Cloud Platform
- Vendor
- License
- other_open
- Year introduced
- 2011
- Confidence
- 0.99
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: GCP appears frequently in cloud/platform job descriptions and is a major hyperscaler alongside AWS/Azure, with broad enterprise adoption and active vendor investment.
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 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
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Cloud Platforms
cloud-platforms
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved |
|
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) |
Aliases — catalog
- Helm (CANONICAL) primary
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Tool
- Sub-category
- Package Manager Tool
- Vendor
- CNCF
- License
- apache_2
- Year introduced
- 2015
- Confidence
- 0.98
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Helm is widely listed in Kubernetes/platform engineering JDs and is the de facto package manager for Kubernetes charts, with strong GitHub adoption and vendor docs from CNCF ecosystem.
Skill profile (library / DB)
- Skill nature
- TOOL
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 13
- Sub-category id
- 67
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
Container Orchestration Platforms Catalog dimension db id 134
Library dimension (catalog)
Roles linked in library: Cloud Architect, DevOps Engineer
-
Deployment and Runtime Configuration Catalog dimension db id 13
Library dimension (catalog)
Roles linked in library: .NET Backend Developer, Backend Developer, Go Backend Developer, PHP Backend Developer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Container Orchestration Platforms
container-orchestration-platforms
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Deployment and Runtime Configuration
deployment-and-runtime-configuration
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Aliases — catalog
- PowerShell (CANONICAL) primary
- PowerShell 5 (VERSION)
- PowerShell 5.1 (VERSION)
- PowerShell 6 (VERSION)
- PowerShell 7 (VERSION)
- PowerShell 7.x (VERSION)
- PowerShell Core (VERSION)
- Windows PowerShell (VERSION)
- powershell 7 (VERSION)
- powershell 7.x (VERSION)
- powershell core (VERSION)
- ps 7 (VERSION)
- pwsh (VERSION)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Language
- Sub-category
- Scripting Language
- Vendor
- Microsoft
- License
- mit
- Year introduced
- 2006
- Confidence
- 0.98
- Version strategy
- SEPARATE_ENTITY
- Version tag
- 7
Maturity reasoning: Common in Windows/admin and DevOps job descriptions; Microsoft continues active development and it remains a standard automation language alongside Bash in enterprise tooling.
Skill profile (library / DB)
- Skill nature
- LANGUAGE
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 6
- Sub-category id
- 38
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
Programming Languages and Scripting Catalog dimension db id 59
Library dimension (catalog)
Roles linked in library: Cyber Security 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 and Scripting
programming-languages-and-scripting
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Programming Languages for ML Systems
programming-languages-for-ml-systems
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Aliases — catalog
- Ansible (CANONICAL) primary
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Tool
- Sub-category
- Configuration Management Tool
- Vendor
- Red Hat
- License
- apache_2
- Year introduced
- 2012
- Confidence
- 0.99
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Commonly listed in DevOps/SRE job descriptions for config management and automation; strong GitHub usage and Red Hat/AWX ecosystem signal broad adoption.
Skill profile (library / DB)
- Skill nature
- TOOL
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 13
- Sub-category id
- 576
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
Configuration Management Catalog dimension db id 133
Library dimension (catalog)
Roles linked in library: Cloud Architect, DevOps Engineer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Configuration Management
configuration-management
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Aliases — catalog
- Puppet (CANONICAL) primary
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Tool
- Sub-category
- Configuration Management Tool
- Vendor
- Puppet, Inc.
- License
- apache_2
- Year introduced
- 2005
- Confidence
- 0.99
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Puppet still appears in some DevOps/infra JDs, but far less than Terraform/Ansible; GitHub activity and community momentum are comparatively modest, indicating specialized legacy use rather than broad adoption.
Skill profile (library / DB)
- Skill nature
- TOOL
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 13
- Sub-category id
- 576
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
Configuration Management Catalog dimension db id 133
Library dimension (catalog)
Roles linked in library: Cloud Architect, DevOps Engineer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Configuration Management
configuration-management
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Container Orchestration
- Sub-category
- general
- Skill nature
- PLATFORM
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
All API 3 persistence rows
Same grid as the skill-extractor “Persistence items” table: one row per (skill × dimension) work item.
| Skill | Tag | Dimension | Skill↔dim | Role↔dim | Outcome | Notes |
|---|---|---|---|---|---|---|
| Apache Kafka | in_db |
Messaging and Event Streaming
messaging-and-event-streaming
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved | |
| Apache Spark | in_db |
ETL and ELT Tooling
etl-and-elt-tooling
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved | |
| Amazon Redshift | in_db |
Cloud Data Warehouses
cloud-data-warehouses
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved | |
| Google BigQuery | new |
Cloud Data Warehouses
cloud-data-warehouses
|
— | — | Skipped — no persistable v3 meta for new skill | skill_not_in_db_v3_proposed |
| AWS S3 | in_db |
Cloud Platforms & Hosting Providers
cloud-platforms-hosting-providers
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| AWS S3 | in_db |
Cloud Platforms & Managed Services
cloud-platforms-managed-services
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Python | in_db |
Cloud Security Scripting & DSL Languages
cloud-security-scripting-dsl-languages
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Python | in_db |
Programming Languages
programming-languages
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Python | in_db |
Programming Languages and Scripting
programming-languages-and-scripting
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Python | in_db |
Programming Languages for Data Work
programming-languages-for-data-work
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved | |
| Python | in_db |
Programming Languages for ML Systems
programming-languages-for-ml-systems
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Python | in_db |
Programming Languages for XR
programming-languages-for-xr
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Python | in_db |
Python Programming
python-programming
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| SQL | in_db |
Pega Programming Languages & DSLs
pega-programming-languages-dsls
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| SQL | in_db |
Programming Languages for Data Work
programming-languages-for-data-work
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved | |
| Bash | in_db |
Programming Languages and Scripting
programming-languages-and-scripting
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Bash | in_db |
Programming Languages for Data Work
programming-languages-for-data-work
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved | |
| Apache Airflow | in_db |
Data Pipeline Orchestration
data-pipeline-orchestration
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved | |
| Jenkins | in_db |
CI/CD Pipeline Platforms
ci-cd-pipeline-platforms
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Jenkins | in_db |
CI/CD for Machine Learning
ci-cd-for-machine-learning
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| GitLab CI | in_db |
CI/CD Pipeline Platforms
ci-cd-pipeline-platforms
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| GitLab CI | in_db |
CI/CD for Machine Learning
ci-cd-for-machine-learning
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Azure DevOps | in_db |
CI/CD Pipeline Platforms
ci-cd-pipeline-platforms
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Terraform | in_db |
Infrastructure & Security Automation Frameworks
infrastructure-security-automation-frameworks
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Terraform | in_db |
Infrastructure as Code
infrastructure-as-code
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Terraform | in_db |
Infrastructure as Code for ML
infrastructure-as-code-for-ml
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| AWS CloudFormation | in_db |
Infrastructure as Code
infrastructure-as-code
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| AWS CloudFormation | in_db |
Infrastructure as Code for ML
infrastructure-as-code-for-ml
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Docker | in_db |
Containerization and Image Builds
containerization-and-image-builds
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Docker | in_db |
Deployment and Cloud Platforms
deployment-and-cloud-platforms
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Docker | in_db |
Deployment and Runtime Configuration
deployment-and-runtime-configuration
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Kubernetes | in_db |
Container Orchestration Platforms
container-orchestration-platforms
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Kubernetes | in_db |
Kubernetes for ML Workloads
kubernetes-for-ml-workloads
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Prometheus | in_db |
Backend Observability, Logging, and Diagnostics
backend-observability-logging-and-diagnostics
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Prometheus | in_db |
Observability and Incident Response
observability-and-incident-response
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Prometheus | in_db |
Observability and Incident Triage
observability-and-incident-triage
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Prometheus | in_db |
Observability and Operations
observability-and-operations
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Grafana | in_db |
Backend Observability, Logging, and Diagnostics
backend-observability-logging-and-diagnostics
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Grafana | in_db |
Observability and Incident Response
observability-and-incident-response
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Grafana | in_db |
Observability and Incident Triage
observability-and-incident-triage
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Grafana | in_db |
Observability and Operations
observability-and-operations
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| DevSecOps | new |
CI/CD Pipeline Platforms
ci-cd-pipeline-platforms
|
— | — | Skipped — no persistable v3 meta for new skill | skill_not_in_db_v3_proposed |
| DevSecOps | new |
Deployment and Release Patterns
deployment-and-release-patterns
|
— | — | Skipped — no persistable v3 meta for new skill | skill_not_in_db_v3_proposed |
| DevSecOps | new |
Infrastructure as Code
infrastructure-as-code
|
— | — | Skipped — no persistable v3 meta for new skill | skill_not_in_db_v3_proposed |
| Code Review | in_db |
React Frontend Development
d_init_01
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| AWS | in_db |
Cloud Platforms
cloud-platforms
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved | |
| AWS | in_db |
Cloud Platforms for AI Deployment
cloud-platforms-for-ai-deployment
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| AWS | in_db |
Cloud Provider Platforms
cloud-provider-platforms
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| AWS | in_db |
Cloud Security Posture Tools
cloud-security-posture-tools
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| 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) | |
| GCP | in_db |
Cloud Platforms
cloud-platforms
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved | |
| GCP | in_db |
Cloud Platforms for AI Deployment
cloud-platforms-for-ai-deployment
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| GCP | in_db |
Cloud Provider Platforms
cloud-provider-platforms
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| GCP | in_db |
Cloud Security Posture Tools
cloud-security-posture-tools
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Helm | in_db |
Container Orchestration Platforms
container-orchestration-platforms
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Helm | in_db |
Deployment and Runtime Configuration
deployment-and-runtime-configuration
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| PowerShell | in_db |
Programming Languages and Scripting
programming-languages-and-scripting
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| PowerShell | in_db |
Programming Languages for ML Systems
programming-languages-for-ml-systems
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Ansible | in_db |
Configuration Management
configuration-management
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Puppet | in_db |
Configuration Management
configuration-management
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Library artifacts (this run)
| Kind | Detail | DB id |
|---|---|---|
| canonical_skill_proposed | AWS Glue | type=Cloud Platforms subtype=general nature=PLATFORM lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Azure Data Lake | type=Cloud Platforms subtype=general nature=PLATFORM lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Apache NiFi | type=Data Engineering Tools subtype=general nature=TOOL lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Talend | type=Data Engineering Tools subtype=general nature=TOOL lifespan=MULTI_YEAR | |
| canonical_skill_proposed | AWS Step Functions | type=Cloud Platforms subtype=general nature=PLATFORM lifespan=MULTI_YEAR | |
| canonical_skill_proposed | ELK Stack | type=Monitoring Tools subtype=general nature=TOOL lifespan=MULTI_YEAR | |
| canonical_skill_proposed | OpenShift | type=Container Orchestration subtype=general nature=PLATFORM lifespan=MULTI_YEAR | |
| dimension_skill_link_proposed | Google BigQuery ↔ Cloud Data Warehouses | |
| role_dimension_link_proposed | Data Engineer ↔ Cloud Data Warehouses | |
| dimension_skill_link_proposed | DevSecOps ↔ CI/CD Pipeline Platforms | |
| dimension_skill_link_proposed | DevSecOps ↔ Deployment and Release Patterns | |
| dimension_skill_link_proposed | DevSecOps ↔ Infrastructure as Code |
nano JD Parser — gpt-4.1-nano click to toggle
Certifications
Show raw JSON
{
"JD_type": "pass",
"about_company": {
"source_marker": {
"first_5_words": "EY is building a better",
"last_5_words": "countries and territories."
},
"text": "EY is building a better working world by creating new value for clients, people, society and the planet, while building trust in capital markets.\n\nEnabled by data, AI and advanced technology, EY teams help clients shape the future with confidence and develop answers for the most pressing issues of today and tomorrow.\n\nEY teams work across a full spectrum of services in assurance, consulting, tax, strategy and transactions. Fueled by sector insights, a globally connected, multi-disciplinary network and diverse ecosystem partners, EY teams can provide services in more than 150 countries and territories.",
"word_count": 84
},
"certifications": [
"AWS Certified Solutions Architect",
"Azure Administrator",
"Google Professional Cloud Architect"
],
"company_name": "EY",
"ctc": null,
"domain": {
"primary": {
"aliases": [
"ITES",
"BPO",
"Tech Consulting"
],
"domain": "IT Services \u0026 Consulting"
},
"secondary": null
},
"education": [
{
"level": "Bachelor\u0027s",
"qualification": "BTECH/BE - Computer Science",
"raw": "B.Tech. / BS in Computer Science",
"requirement": "required"
}
],
"experience": {
"max": null,
"min": 5,
"raw": "5+ years of hands-on Cloud and DevOps experience"
},
"job_locations": [],
"role": "Data Engineer",
"role_aliases": [
"Data Engineer",
"Data Pipeline Engineer",
"ETL Engineer"
],
"role_archetype": "Engineering",
"roles_and_responsibilities": [
{
"bullet_count": 15,
"heading": "Job Description",
"heading_was_present": true,
"source_marker": {
"first_5_words": "\u2022 Develop and maintain scalable",
"last_5_words": "data science, analytics, and business stakeholders."
},
"text": "\u2022 Develop and maintain scalable data pipelines using tools like Apache Kafka, Apache Spark, or AWS Glue to ingest, process, and transform large datasets from various sources, ensuring efficient data flow and processing.\n\u2022 Design and implement data models and schemas in data warehouses (e.g., Amazon Redshift, Google BigQuery) and data lakes (e.g., AWS S3, Azure Data Lake) to support analytics and reporting needs.\n\u2022 Collaborate with data scientists and analysts to understand data requirements, ensuring data availability and accessibility for analytics, machine learning, and reporting.\n\u2022 Utilize ETL tools and frameworks (e.g., Apache NiFi, Talend, or custom Python scripts) to automate data extraction, transformation, and loading processes, ensuring data quality and integrity.\n\u2022 Monitor and optimize data pipeline performance using tools like Apache Airflow or AWS Step Functions, implementing best practices for data processing and workflow management.\n\u2022 Write, test, and maintain scripts in Python, SQL, or Bash for data processing, automation tasks, and data validation, ensuring high code quality and performance.\n\u2022 Implement CI/CD practices for data engineering workflows using tools like Jenkins, GitLab CI, or Azure DevOps, automating the deployment of data pipelines and infrastructure changes.\n\u2022 Collaborate with DevOps teams to integrate data solutions into existing infrastructure, leveraging Infrastructure as Code (IaC) tools like Terraform or AWS CloudFormation for provisioning and managing resources.\n\u2022 Manage containerized data applications using Docker and orchestrate them with Kubernetes, ensuring scalability and reliability of data processing applications.\n\u2022 Implement monitoring and logging solutions using tools like Prometheus, Grafana, or ELK Stack to track data pipeline performance, troubleshoot issues, and ensure data quality.\n\u2022 Ensure compliance with data governance, security best practices, and data privacy regulations, embedding DevSecOps principles in data workflows.\n\u2022 Participate in code reviews and contribute to the development of best practices for data engineering, data quality, and DevOps methodologies.\n\u2022 Mentor junior data engineers, providing guidance on data engineering practices, data architecture, and DevOps tools and techniques.\n\u2022 Contribute to the documentation of data architecture, processes, and workflows for knowledge sharing, compliance, and onboarding purposes.\n\u2022 Demonstrate strong communication skills to collaborate effectively with cross-functional teams, including data science, analytics, and business stakeholders.",
"word_count": 366
},
{
"bullet_count": 3,
"heading": "Desired Profile",
"heading_was_present": true,
"source_marker": {
"first_5_words": "\u2022 Seeking a DevOps Engineer",
"last_5_words": "is required; OpenShift is a plus."
},
"text": "\u2022 Seeking a DevOps Engineer with 5+ years of hands-on Cloud and DevOps experience, including significant leadership. Requires a Bachelor\u0027s/master\u2019s in computer science.\n\u2022 Must have expert proficiency in Terraform and extensive experience across at least two major cloud platforms (AWS, Azure, GCP). Strong hands-on experience with Kubernetes, Helm charts, and designing/optimizing CI/CD pipelines (e.g., Jenkins, GitLab CI) is essential. Proficiency in Python and scripting (Bash/PowerShell) is also a must.\n\u2022 Valued experience includes leading cloud migrations, contributing to RFP/RFI processes, and mentoring teams. Excellent problem-solving, communication, and collaboration skills are critical. Experience with configuration management (Ansible, Puppet) and DevSecOps principles is required; OpenShift is a plus.",
"word_count": 118
}
],
"urls": []
}
API 1 — extract-from-jd click to toggle
{
"final_skills": [
{
"is_primary": true,
"skill_name": "Apache Kafka"
},
{
"is_primary": true,
"skill_name": "Apache Spark"
},
{
"is_primary": true,
"skill_name": "AWS Glue"
},
{
"is_primary": true,
"skill_name": "Amazon Redshift"
},
{
"is_primary": true,
"skill_name": "Google BigQuery"
},
{
"is_primary": true,
"skill_name": "AWS S3"
},
{
"is_primary": true,
"skill_name": "Azure Data Lake"
},
{
"is_primary": true,
"skill_name": "Apache NiFi"
},
{
"is_primary": true,
"skill_name": "Talend"
},
{
"is_primary": true,
"skill_name": "Python"
},
{
"is_primary": true,
"skill_name": "SQL"
},
{
"is_primary": true,
"skill_name": "Bash"
},
{
"is_primary": true,
"skill_name": "Apache Airflow"
},
{
"is_primary": true,
"skill_name": "AWS Step Functions"
},
{
"is_primary": true,
"skill_name": "Jenkins"
},
{
"is_primary": true,
"skill_name": "GitLab CI"
},
{
"is_primary": true,
"skill_name": "Azure DevOps"
},
{
"is_primary": true,
"skill_name": "Terraform"
},
{
"is_primary": true,
"skill_name": "AWS CloudFormation"
},
{
"is_primary": true,
"skill_name": "Docker"
},
{
"is_primary": true,
"skill_name": "Kubernetes"
},
{
"is_primary": true,
"skill_name": "Prometheus"
},
{
"is_primary": true,
"skill_name": "Grafana"
},
{
"is_primary": true,
"skill_name": "ELK Stack"
},
{
"is_primary": true,
"skill_name": "DevSecOps"
},
{
"is_primary": true,
"skill_name": "Code Review"
},
{
"is_primary": true,
"skill_name": "AWS"
},
{
"is_primary": true,
"skill_name": "Azure"
},
{
"is_primary": true,
"skill_name": "GCP"
},
{
"is_primary": true,
"skill_name": "Helm"
},
{
"is_primary": true,
"skill_name": "PowerShell"
},
{
"is_primary": true,
"skill_name": "Ansible"
},
{
"is_primary": true,
"skill_name": "Puppet"
},
{
"is_primary": false,
"skill_name": "OpenShift"
}
],
"jd_role": {
"display_name": "Data Engineer",
"rationale": null,
"role_aliases": [
"Data Engineer",
"Data Pipeline Engineer",
"ETL Engineer"
],
"role_archetype": "Engineering",
"slug": ""
},
"nano_parsed": {
"JD_type": "pass",
"about_company": {
"source_marker": {
"first_5_words": "EY is building a better",
"last_5_words": "countries and territories."
},
"text": "EY is building a better working world by creating new value for clients, people, society and the planet, while building trust in capital markets.\n\nEnabled by data, AI and advanced technology, EY teams help clients shape the future with confidence and develop answers for the most pressing issues of today and tomorrow.\n\nEY teams work across a full spectrum of services in assurance, consulting, tax, strategy and transactions. Fueled by sector insights, a globally connected, multi-disciplinary network and diverse ecosystem partners, EY teams can provide services in more than 150 countries and territories.",
"word_count": 84
},
"certifications": [
"AWS Certified Solutions Architect",
"Azure Administrator",
"Google Professional Cloud Architect"
],
"company_name": "EY",
"ctc": null,
"domain": {
"primary": {
"aliases": [
"ITES",
"BPO",
"Tech Consulting"
],
"domain": "IT Services \u0026 Consulting"
},
"secondary": null
},
"education": [
{
"level": "Bachelor\u0027s",
"qualification": "BTECH/BE - Computer Science",
"raw": "B.Tech. / BS in Computer Science",
"requirement": "required"
}
],
"experience": {
"max": null,
"min": 5,
"raw": "5+ years of hands-on Cloud and DevOps experience"
},
"job_locations": [],
"role": "Data Engineer",
"role_aliases": [
"Data Engineer",
"Data Pipeline Engineer",
"ETL Engineer"
],
"role_archetype": "Engineering",
"roles_and_responsibilities": [
{
"bullet_count": 15,
"heading": "Job Description",
"heading_was_present": true,
"source_marker": {
"first_5_words": "\u2022 Develop and maintain scalable",
"last_5_words": "data science, analytics, and business stakeholders."
},
"text": "\u2022 Develop and maintain scalable data pipelines using tools like Apache Kafka, Apache Spark, or AWS Glue to ingest, process, and transform large datasets from various sources, ensuring efficient data flow and processing.\n\u2022 Design and implement data models and schemas in data warehouses (e.g., Amazon Redshift, Google BigQuery) and data lakes (e.g., AWS S3, Azure Data Lake) to support analytics and reporting needs.\n\u2022 Collaborate with data scientists and analysts to understand data requirements, ensuring data availability and accessibility for analytics, machine learning, and reporting.\n\u2022 Utilize ETL tools and frameworks (e.g., Apache NiFi, Talend, or custom Python scripts) to automate data extraction, transformation, and loading processes, ensuring data quality and integrity.\n\u2022 Monitor and optimize data pipeline performance using tools like Apache Airflow or AWS Step Functions, implementing best practices for data processing and workflow management.\n\u2022 Write, test, and maintain scripts in Python, SQL, or Bash for data processing, automation tasks, and data validation, ensuring high code quality and performance.\n\u2022 Implement CI/CD practices for data engineering workflows using tools like Jenkins, GitLab CI, or Azure DevOps, automating the deployment of data pipelines and infrastructure changes.\n\u2022 Collaborate with DevOps teams to integrate data solutions into existing infrastructure, leveraging Infrastructure as Code (IaC) tools like Terraform or AWS CloudFormation for provisioning and managing resources.\n\u2022 Manage containerized data applications using Docker and orchestrate them with Kubernetes, ensuring scalability and reliability of data processing applications.\n\u2022 Implement monitoring and logging solutions using tools like Prometheus, Grafana, or ELK Stack to track data pipeline performance, troubleshoot issues, and ensure data quality.\n\u2022 Ensure compliance with data governance, security best practices, and data privacy regulations, embedding DevSecOps principles in data workflows.\n\u2022 Participate in code reviews and contribute to the development of best practices for data engineering, data quality, and DevOps methodologies.\n\u2022 Mentor junior data engineers, providing guidance on data engineering practices, data architecture, and DevOps tools and techniques.\n\u2022 Contribute to the documentation of data architecture, processes, and workflows for knowledge sharing, compliance, and onboarding purposes.\n\u2022 Demonstrate strong communication skills to collaborate effectively with cross-functional teams, including data science, analytics, and business stakeholders.",
"word_count": 366
},
{
"bullet_count": 3,
"heading": "Desired Profile",
"heading_was_present": true,
"source_marker": {
"first_5_words": "\u2022 Seeking a DevOps Engineer",
"last_5_words": "is required; OpenShift is a plus."
},
"text": "\u2022 Seeking a DevOps Engineer with 5+ years of hands-on Cloud and DevOps experience, including significant leadership. Requires a Bachelor\u0027s/master\u2019s in computer science.\n\u2022 Must have expert proficiency in Terraform and extensive experience across at least two major cloud platforms (AWS, Azure, GCP). Strong hands-on experience with Kubernetes, Helm charts, and designing/optimizing CI/CD pipelines (e.g., Jenkins, GitLab CI) is essential. Proficiency in Python and scripting (Bash/PowerShell) is also a must.\n\u2022 Valued experience includes leading cloud migrations, contributing to RFP/RFI processes, and mentoring teams. Excellent problem-solving, communication, and collaboration skills are critical. Experience with configuration management (Ansible, Puppet) and DevSecOps principles is required; OpenShift is a plus.",
"word_count": 118
}
],
"urls": []
},
"rejected": false,
"rejection_reason": null,
"run_id": "f02c4d28-b3d4-43c4-b2c6-65025ec98744",
"stage3_signals": {
"alias_found": true,
"alias_match_roles": [
{
"display_name": "Data Engineer",
"kra_matches": null,
"matched_count": null,
"matched_skills": null,
"role_id": 2,
"score": 1.0,
"slug": "data-engineer",
"total_count": null
}
],
"kra_match_roles": [
{
"display_name": "Data Engineer",
"kra_matches": [
{
"kra_text": "Develops batch and real-time streaming data pipelines using Apache Spark, Apache Kafka, Apache Flink, or Airflow for data movement and processing at scale.",
"sentence": "Develop and maintain scalable data pipelines using tools like Apache Kafka, Apache Spark, or AWS Glue to ingest, process, and transform large datasets from various sources, ensuring efficient data flow and processing.",
"similarity": 0.7982
},
{
"kra_text": "Designs dimensional models, star schemas, data vault structures, and curated data mart tables to support BI tools and self-service analytics consumption.",
"sentence": "Design and implement data models and schemas in data warehouses (e.g. , Amazon Redshift, Google BigQuery) and data lakes (e.g. , AWS S3, Azure Data Lake) to support analytics and reporting needs.",
"similarity": 0.7333
},
{
"kra_text": "Develops batch and real-time streaming data pipelines using Apache Spark, Apache Kafka, Apache Flink, or Airflow for data movement and processing at scale.",
"sentence": "Monitor and optimize data pipeline performance using tools like Apache Airflow or AWS Step Functions, implementing best practices for data processing and workflow management.",
"similarity": 0.7175
}
],
"matched_count": null,
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"role_id": 2,
"score": 0.7497,
"slug": "data-engineer",
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},
{
"display_name": "DevOps Engineer",
"kra_matches": [
{
"kra_text": "Manages container orchestration with Kubernetes and Docker, deploying applications as pods, managing namespaces, and configuring auto-scaling across cloud environments.",
"sentence": "Manage containerized data applications using Docker and orchestrate them with Kubernetes, ensuring scalability and reliability of data processing applications.",
"similarity": 0.7274
},
{
"kra_text": "Builds and maintains CI/CD pipelines using Jenkins, GitHub Actions, GitLab CI, or CircleCI to automate build, test, security scanning, and deployment workflows.",
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"similarity": 0.6993
},
{
"kra_text": "Provisions and manages cloud infrastructure on AWS, Azure, or GCP using Terraform or CloudFormation to enforce infrastructure-as-code standards.",
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],
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},
{
"display_name": "Fullstack Developer",
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{
"kra_text": "Delivers features through CI/CD pipelines using automated tests, staged rollouts, feature flags, and incremental deployments.",
"sentence": "Implement CI/CD practices for data engineering workflows using tools like Jenkins, GitLab CI, or Azure DevOps, automating the deployment of data pipelines and infrastructure changes.",
"similarity": 0.6262
},
{
"kra_text": "Designs and queries relational databases like PostgreSQL and document stores like MongoDB, writing migrations, indexes, and optimized queries.",
"sentence": "Design and implement data models and schemas in data warehouses (e.g. , Amazon Redshift, Google BigQuery) and data lakes (e.g. , AWS S3, Azure Data Lake) to support analytics and reporting needs.",
"similarity": 0.567
},
{
"kra_text": "Delivers features through CI/CD pipelines using automated tests, staged rollouts, feature flags, and incremental deployments.",
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}
],
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"matched_skills": null,
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"slug": "full-stack-engineer",
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},
{
"display_name": "Backend Developer",
"kra_matches": [
{
"kra_text": "Adds structured logging, metrics, distributed tracing, and alerting to improve system observability and support production debugging.",
"sentence": "Implement monitoring and logging solutions using tools like Prometheus, Grafana, or ELK Stack to track data pipeline performance, troubleshoot issues, and ensure data quality.",
"similarity": 0.5744
},
{
"kra_text": "Configures Docker containers, deployment descriptors, environment variables, and CI/CD pipeline stages for backend service releases.",
"sentence": "Implement CI/CD practices for data engineering workflows using tools like Jenkins, GitLab CI, or Azure DevOps, automating the deployment of data pipelines and infrastructure changes.",
"similarity": 0.5467
},
{
"kra_text": "Configures Docker containers, deployment descriptors, environment variables, and CI/CD pipeline stages for backend service releases.",
"sentence": "Strong hands-on experience with Kubernetes, Helm charts, and designing/optimizing CI/CD pipelines (e.g. , Jenkins, GitLab CI) is essential.",
"similarity": 0.4883
}
],
"matched_count": null,
"matched_skills": null,
"role_id": 1,
"score": 0.5365,
"slug": "backend-engineer",
"total_count": null
},
{
"display_name": "MLOps Engineer",
"kra_matches": [
{
"kra_text": "Sets up model monitoring dashboards, data drift detection, prediction performance tracking, and alert routing for production ML systems.",
"sentence": "Implement monitoring and logging solutions using tools like Prometheus, Grafana, or ELK Stack to track data pipeline performance, troubleshoot issues, and ensure data quality.",
"similarity": 0.541
},
{
"kra_text": "Automates ML platform operations including scheduled retraining triggers, pipeline orchestration, evaluation workflows, and alerting configuration.",
"sentence": "Monitor and optimize data pipeline performance using tools like Apache Airflow or AWS Step Functions, implementing best practices for data processing and workflow management.",
"similarity": 0.5408
},
{
"kra_text": "Automates ML platform operations including scheduled retraining triggers, pipeline orchestration, evaluation workflows, and alerting configuration.",
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}
],
"matched_count": null,
"matched_skills": null,
"role_id": 16,
"score": 0.5331,
"slug": "ml-ops-engineer",
"total_count": null
}
],
"skill_match_roles": [
{
"display_name": "DevOps Engineer",
"kra_matches": null,
"matched_count": 15,
"matched_skills": [
"AWS",
"AWS CloudFormation",
"Ansible",
"Azure",
"Azure DevOps",
"Docker",
"GCP",
"GitLab CI",
"Grafana",
"Helm",
"Jenkins",
"Kubernetes",
"Prometheus",
"Puppet",
"Terraform"
],
"role_id": 10,
"score": 0.4545,
"slug": "devops-engineer",
"total_count": 33
},
{
"display_name": "Cloud Architect",
"kra_matches": null,
"matched_count": 11,
"matched_skills": [
"AWS",
"AWS CloudFormation",
"Ansible",
"Azure",
"GCP",
"Grafana",
"Helm",
"Kubernetes",
"Prometheus",
"Puppet",
"Terraform"
],
"role_id": 9,
"score": 0.3333,
"slug": "cloud-architect",
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},
{
"display_name": "ML Engineer",
"kra_matches": null,
"matched_count": 10,
"matched_skills": [
"AWS",
"AWS CloudFormation",
"Azure",
"GCP",
"GitLab CI",
"Jenkins",
"Kubernetes",
"PowerShell",
"Python",
"Terraform"
],
"role_id": 3,
"score": 0.303,
"slug": "ml-engineer",
"total_count": 33
},
{
"display_name": "Data Engineer",
"kra_matches": null,
"matched_count": 10,
"matched_skills": [
"AWS",
"Amazon Redshift",
"Apache Airflow",
"Apache Kafka",
"Apache Spark",
"Azure",
"Bash",
"GCP",
"Python",
"SQL"
],
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"score": 0.303,
"slug": "data-engineer",
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},
{
"display_name": "Backend Developer",
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"matched_count": 9,
"matched_skills": [
"AWS",
"Apache Kafka",
"Azure",
"Docker",
"GCP",
"Grafana",
"Helm",
"Prometheus",
"Python"
],
"role_id": 1,
"score": 0.2727,
"slug": "backend-engineer",
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}
]
},
"stage4_decision": {
"alias_collision_detected": false,
"case": "DOMAIN",
"chosen_role": {
"display_name": "Data Engineer",
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"matched_count": null,
"matched_skills": null,
"role_id": 2,
"score": 0.98,
"slug": "data-engineer",
"total_count": null
},
"confidence": 0.98,
"is_new_role": false,
"llm2_fired": false,
"llm2_reasoning": null,
"matched_dimensions": [
"Scalable data pipeline engineering",
"Data warehousing and data lake design",
"ETL automation and data quality",
"Workflow orchestration and performance tuning",
"CI/CD for data engineering",
"Infrastructure as Code for data platforms",
"Containerized data application operations",
"Monitoring, logging, and observability",
"Data governance and security compliance"
],
"matched_kras": [
"Develop and maintain scalable data pipelines",
"Ingest, process, and transform large datasets",
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"Automate data extraction, transformation, and loading processes",
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"Write, test, and maintain scripts in Python, SQL, or Bash",
"Implement CI/CD practices for data engineering workflows",
"Integrate data solutions into existing infrastructure",
"Manage containerized data applications using Docker and Kubernetes",
"Implement monitoring and logging solutions",
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"matched_skills": [
"Apache Kafka",
"Apache Spark",
"AWS Glue",
"Amazon Redshift",
"Google BigQuery",
"AWS S3",
"Azure Data Lake",
"Apache NiFi",
"Talend",
"Python",
"SQL",
"Bash",
"Apache Airflow",
"AWS Step Functions",
"Jenkins",
"GitLab CI",
"Azure DevOps",
"Terraform",
"AWS CloudFormation",
"Docker"
],
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"new_role_slug": null,
"queued": false,
"reasoning": "Domain=Data Engineering \u0026 Analytics; The JD centers on building and operating scalable data pipelines, ETL/ELT, orchestration, infrastructure, and data platform tooling, which aligns most strongly with Data Engineer.",
"sub_role": null
},
"stage5_updates": {
"centroid_n_after": 231,
"centroid_updated": true,
"collision_log_id": null,
"new_kra_attached": null,
"new_skills_attached": [
{
"is_primary": true,
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"role_display_name": "Data Engineer",
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},
{
"is_primary": true,
"queue_id": 11505,
"role_display_name": "Data Engineer",
"role_slug": "data-engineer",
"skill_name": "Google BigQuery",
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},
{
"is_primary": true,
"queue_id": 11506,
"role_display_name": "Data Engineer",
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},
{
"is_primary": true,
"queue_id": 11507,
"role_display_name": "Data Engineer",
"role_slug": "data-engineer",
"skill_name": "Apache NiFi",
"status": "pending"
},
{
"is_primary": true,
"queue_id": 11508,
"role_display_name": "Data Engineer",
"role_slug": "data-engineer",
"skill_name": "Talend",
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},
{
"is_primary": true,
"queue_id": 11509,
"role_display_name": "Data Engineer",
"role_slug": "data-engineer",
"skill_name": "AWS Step Functions",
"status": "pending"
},
{
"is_primary": true,
"queue_id": 11510,
"role_display_name": "Data Engineer",
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"skill_name": "ELK Stack",
"status": "pending"
},
{
"is_primary": true,
"queue_id": 11511,
"role_display_name": "Data Engineer",
"role_slug": "data-engineer",
"skill_name": "DevSecOps",
"status": "pending"
},
{
"is_primary": false,
"queue_id": 11512,
"role_display_name": "Data Engineer",
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"skill_name": "OpenShift",
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}
],
"queue_entry_id": null,
"v3_pipeline_triggered": false,
"v3_role_slug": null,
"v3_run_id": null
}
}
API 2 — extract-details
{
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"alias_persisted": false,
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"input_term": "Apache Kafka",
"matched_canonical": {
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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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{
"alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
"alias_persisted": false,
"existing_alias_id": 2004,
"existing_alias_text": "Apache Spark",
"input_term": "Apache Spark",
"matched_canonical": {
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"is_extractable": true,
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"matched_via": "alias"
},
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"alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
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"is_extractable": true,
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},
"matched_via": "alias"
},
{
"alias_persist_skipped_reason": "TODO: REMOVE AFTER TESTING \u2014 alias DB write disabled",
"alias_persisted": false,
"existing_alias_id": 300,
"existing_alias_text": "BigQuery",
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"matched_via": "embedding_alias"
},
{
"alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
"alias_persisted": false,
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{
"alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
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},
"matched_via": "alias"
},
{
"alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
"alias_persisted": false,
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"existing_alias_text": "SQL",
"input_term": "SQL",
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"is_extractable": true,
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},
"matched_via": "alias"
},
{
"alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
"alias_persisted": false,
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"matched_via": "alias"
},
{
"alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
"alias_persisted": false,
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"existing_alias_text": "Apache Airflow",
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},
"matched_via": "alias"
},
{
"alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
"alias_persisted": false,
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},
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},
{
"alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
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"is_extractable": true,
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},
"matched_via": "alias"
},
{
"alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
"alias_persisted": false,
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"is_extractable": true,
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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",
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"existing_alias_text": "Terraform",
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},
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},
{
"alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
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"is_also_category": false,
"is_extractable": true,
"skill_nature": "CLOUD_SERVICE",
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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": 198,
"existing_alias_text": "Docker",
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"matched_canonical": {
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"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": 1267,
"existing_alias_text": "Kubernetes",
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},
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},
{
"alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
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"existing_alias_text": "Prometheus",
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},
"matched_via": "alias"
},
{
"alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
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"existing_alias_text": "Grafana",
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},
"matched_via": "alias"
},
{
"alias_persist_skipped_reason": "TODO: REMOVE AFTER TESTING \u2014 alias DB write disabled",
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},
"matched_via": "embedding_alias"
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{
"alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
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"is_also_category": false,
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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",
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"is_extractable": true,
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},
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{
"alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
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"existing_alias_text": "Azure",
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"is_primary": true,
"match_strategy": "CASE_INSENSITIVE"
}
],
"canonical": {
"category_id": 9,
"display_name": "GCP",
"id": 186,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "PLATFORM",
"slug": "gcp",
"sub_category_id": 46,
"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"dimensions": [
{
"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"
},
"input_skill": "GCP",
"llm_role": null,
"roles_from_db": [
{
"display_name": ".NET Backend Developer",
"id": 83,
"rationale": null,
"role_archetype": "Engineering",
"slug": "dotnet-backend-developer",
"source": "db"
},
{
"display_name": "Backend Developer",
"id": 1,
"rationale": null,
"role_archetype": "A Backend Engineer designs, builds, and maintains the server-side logic and data handling that power applications and services. They focus on implementing reliable business functionality, integrating with other systems, and ensuring the backend is scalable, maintainable, and observable.",
"slug": "backend-engineer",
"source": "db"
},
{
"display_name": "Cyber Security Engineer",
"id": 5,
"rationale": null,
"role_archetype": null,
"slug": "cybersecurity-engineer",
"source": "db"
},
{
"display_name": "Data Engineer",
"id": 2,
"rationale": null,
"role_archetype": null,
"slug": "data-engineer",
"source": "db"
},
{
"display_name": "DevOps Engineer",
"id": 10,
"rationale": null,
"role_archetype": null,
"slug": "devops-engineer",
"source": "db"
},
{
"display_name": "Fullstack Developer",
"id": 15,
"rationale": null,
"role_archetype": null,
"slug": "full-stack-engineer",
"source": "db"
},
{
"display_name": "Go Backend Developer",
"id": 81,
"rationale": null,
"role_archetype": "Engineering",
"slug": "go-backend-developer",
"source": "db"
},
{
"display_name": "Java Backend Developer",
"id": 79,
"rationale": null,
"role_archetype": "Engineering",
"slug": "java-backend-developer",
"source": "db"
},
{
"display_name": "Kotlin Backend Developer",
"id": 84,
"rationale": null,
"role_archetype": "Engineering",
"slug": "kotlin-server-backend-developer",
"source": "db"
},
{
"display_name": "ML Engineer",
"id": 3,
"rationale": null,
"role_archetype": null,
"slug": "ml-engineer",
"source": "db"
},
{
"display_name": "MLOps Engineer",
"id": 16,
"rationale": null,
"role_archetype": null,
"slug": "ml-ops-engineer",
"source": "db"
},
{
"display_name": "Node.js Backend Developer",
"id": 82,
"rationale": null,
"role_archetype": "Engineering",
"slug": "node-backend-developer",
"source": "db"
},
{
"display_name": "Python Backend Developer",
"id": 80,
"rationale": null,
"role_archetype": "Engineering",
"slug": "python-backend-developer",
"source": "db"
},
{
"display_name": "Scala Backend Developer",
"id": 87,
"rationale": null,
"role_archetype": "Engineering",
"slug": "scala-backend-developer",
"source": "db"
}
]
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Cloud Platforms for AI Deployment",
"id": 211,
"rationale": "Major cloud services that provide infrastructure and managed services for AI workloads.",
"slug": "cloud-platforms-for-ai-deployment",
"source": "db"
},
"input_skill": "GCP",
"llm_role": null,
"roles_from_db": [
{
"display_name": "AI Engineer",
"id": 13,
"rationale": null,
"role_archetype": null,
"slug": "ai-engineer",
"source": "db"
}
]
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Cloud Provider Platforms",
"id": 131,
"rationale": "Major cloud platforms and their core service ecosystems used to design target-state architectures, choose deployment boundaries, and evaluate managed capabilities. This is the primary substrate for cloud architecture decisions.",
"slug": "cloud-provider-platforms",
"source": "db"
},
"input_skill": "GCP",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Cloud Architect",
"id": 9,
"rationale": null,
"role_archetype": null,
"slug": "cloud-architect",
"source": "db"
},
{
"display_name": "Cloud Security Engineer",
"id": 23,
"rationale": null,
"role_archetype": null,
"slug": "cloud-security-engineer",
"source": "db"
}
]
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Cloud Security Posture Tools",
"id": 64,
"rationale": "Cloud-native security platforms used to assess misconfiguration, workload exposure, and cloud control coverage. This dimension includes the major CNAPP/CSPM/CWPP vendors and cloud security services the role reviews and tunes.",
"slug": "cloud-security-posture-tools",
"source": "db"
},
"input_skill": "GCP",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Cloud Security Engineer",
"id": 23,
"rationale": null,
"role_archetype": null,
"slug": "cloud-security-engineer",
"source": "db"
},
{
"display_name": "Cyber Security Engineer",
"id": 5,
"rationale": null,
"role_archetype": null,
"slug": "cybersecurity-engineer",
"source": "db"
}
]
}
],
"input_skill": "GCP",
"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": "Helm",
"alias_type": "CANONICAL",
"id": 204,
"is_primary": true,
"match_strategy": "CASE_INSENSITIVE"
}
],
"canonical": {
"category_id": 13,
"display_name": "Helm",
"id": 63,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "TOOL",
"slug": "helm",
"sub_category_id": 67,
"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"dimensions": [
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Container Orchestration Platforms",
"id": 134,
"rationale": "Platforms that schedule and manage containerized workloads across clusters and environments. Cloud Architects need these to define workload placement standards, cluster boundaries, and platform capabilities.",
"slug": "container-orchestration-platforms",
"source": "db"
},
"input_skill": "Helm",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Cloud Architect",
"id": 9,
"rationale": null,
"role_archetype": null,
"slug": "cloud-architect",
"source": "db"
},
{
"display_name": "DevOps Engineer",
"id": 10,
"rationale": null,
"role_archetype": null,
"slug": "devops-engineer",
"source": "db"
}
]
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Deployment and Runtime Configuration",
"id": 13,
"rationale": "Configuration and release artifacts that control how backend services run in environments. Includes environment variables, manifests, feature flags, and release-safe configuration management.",
"slug": "deployment-and-runtime-configuration",
"source": "db"
},
"input_skill": "Helm",
"llm_role": null,
"roles_from_db": [
{
"display_name": ".NET Backend Developer",
"id": 83,
"rationale": null,
"role_archetype": "Engineering",
"slug": "dotnet-backend-developer",
"source": "db"
},
{
"display_name": "Backend Developer",
"id": 1,
"rationale": null,
"role_archetype": "A Backend Engineer designs, builds, and maintains the server-side logic and data handling that power applications and services. They focus on implementing reliable business functionality, integrating with other systems, and ensuring the backend is scalable, maintainable, and observable.",
"slug": "backend-engineer",
"source": "db"
},
{
"display_name": "Go Backend Developer",
"id": 81,
"rationale": null,
"role_archetype": "Engineering",
"slug": "go-backend-developer",
"source": "db"
},
{
"display_name": "PHP Backend Developer",
"id": 86,
"rationale": null,
"role_archetype": "Engineering",
"slug": "php-backend-developer",
"source": "db"
}
]
}
],
"input_skill": "Helm",
"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": "PowerShell",
"alias_type": "CANONICAL",
"id": 583,
"is_primary": true,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "PowerShell 5",
"alias_type": "VERSION",
"id": 585,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "PowerShell 5.1",
"alias_type": "VERSION",
"id": 588,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "PowerShell 6",
"alias_type": "VERSION",
"id": 586,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "PowerShell 7",
"alias_type": "VERSION",
"id": 587,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "PowerShell 7.x",
"alias_type": "VERSION",
"id": 589,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "PowerShell Core",
"alias_type": "VERSION",
"id": 590,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Windows PowerShell",
"alias_type": "VERSION",
"id": 591,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "powershell 7",
"alias_type": "VERSION",
"id": 2400,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "powershell 7.x",
"alias_type": "VERSION",
"id": 2401,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "powershell core",
"alias_type": "VERSION",
"id": 2402,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "ps 7",
"alias_type": "VERSION",
"id": 2398,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "pwsh",
"alias_type": "VERSION",
"id": 584,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
}
],
"canonical": {
"category_id": 6,
"display_name": "PowerShell",
"id": 297,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "LANGUAGE",
"slug": "powershell",
"sub_category_id": 38,
"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"dimensions": [
{
"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": "PowerShell",
"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 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": "PowerShell",
"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": "PowerShell",
"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": "Ansible",
"alias_type": "CANONICAL",
"id": 1262,
"is_primary": true,
"match_strategy": "CASE_INSENSITIVE"
}
],
"canonical": {
"category_id": 13,
"display_name": "Ansible",
"id": 721,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "TOOL",
"slug": "ansible",
"sub_category_id": 576,
"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"dimensions": [
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Configuration Management",
"id": 133,
"rationale": "Host and fleet configuration tools used to standardize operating system state, bootstrap servers, and enforce baseline settings. This is distinct from IaC because it focuses on in-guest configuration rather than cloud resource provisioning.",
"slug": "configuration-management",
"source": "db"
},
"input_skill": "Ansible",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Cloud Architect",
"id": 9,
"rationale": null,
"role_archetype": null,
"slug": "cloud-architect",
"source": "db"
},
{
"display_name": "DevOps Engineer",
"id": 10,
"rationale": null,
"role_archetype": null,
"slug": "devops-engineer",
"source": "db"
}
]
}
],
"input_skill": "Ansible",
"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": "Puppet",
"alias_type": "CANONICAL",
"id": 1264,
"is_primary": true,
"match_strategy": "CASE_INSENSITIVE"
}
],
"canonical": {
"category_id": 13,
"display_name": "Puppet",
"id": 723,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "TOOL",
"slug": "puppet",
"sub_category_id": 576,
"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"dimensions": [
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Configuration Management",
"id": 133,
"rationale": "Host and fleet configuration tools used to standardize operating system state, bootstrap servers, and enforce baseline settings. This is distinct from IaC because it focuses on in-guest configuration rather than cloud resource provisioning.",
"slug": "configuration-management",
"source": "db"
},
"input_skill": "Puppet",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Cloud Architect",
"id": 9,
"rationale": null,
"role_archetype": null,
"slug": "cloud-architect",
"source": "db"
},
{
"display_name": "DevOps Engineer",
"id": 10,
"rationale": null,
"role_archetype": null,
"slug": "devops-engineer",
"source": "db"
}
]
}
],
"input_skill": "Puppet",
"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": "OpenShift",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": {
"derived": {
"category": "Container Orchestration",
"skill_nature": "PLATFORM",
"sub_category": "general",
"typical_lifespan": "MULTI_YEAR",
"version_strategy": "UNVERSIONED",
"volatility": "MEDIUM"
},
"enrichment": null,
"keep_log": [],
"locked_dimensions": [],
"merge_log": [],
"placed": null,
"relationships": null,
"skill_id": "openshift",
"split_log": [],
"typed": null,
"warnings": []
},
"source_tag": "llm",
"was_in_llm_skills": true
}
],
"unmatched_skills": [
"AWS Glue",
"Azure Data Lake",
"Apache NiFi",
"Talend",
"AWS Step Functions",
"ELK Stack",
"OpenShift"
]
}
API 3 — final-role-output
{
"chosen_role": {
"display_name": "Data Engineer",
"id": 2,
"rationale": "Domain=Data Engineering \u0026 Analytics; The JD centers on building and operating scalable data pipelines, ETL/ELT, orchestration, infrastructure, and data platform tooling, which aligns most strongly with Data Engineer.",
"role_archetype": null,
"slug": "data-engineer",
"source": "db"
},
"chosen_role_resolution": "in_db",
"final_input_skills": [
{
"skill": "Apache Kafka",
"tag": "in_db"
},
{
"skill": "Apache Spark",
"tag": "in_db"
},
{
"skill": "AWS Glue",
"tag": "new"
},
{
"skill": "Amazon Redshift",
"tag": "in_db"
},
{
"skill": "Google BigQuery",
"tag": "in_db"
},
{
"skill": "AWS S3",
"tag": "in_db"
},
{
"skill": "Azure Data Lake",
"tag": "new"
},
{
"skill": "Apache NiFi",
"tag": "new"
},
{
"skill": "Talend",
"tag": "new"
},
{
"skill": "Python",
"tag": "in_db"
},
{
"skill": "SQL",
"tag": "in_db"
},
{
"skill": "Bash",
"tag": "in_db"
},
{
"skill": "Apache Airflow",
"tag": "in_db"
},
{
"skill": "AWS Step Functions",
"tag": "new"
},
{
"skill": "Jenkins",
"tag": "in_db"
},
{
"skill": "GitLab CI",
"tag": "in_db"
},
{
"skill": "Azure DevOps",
"tag": "in_db"
},
{
"skill": "Terraform",
"tag": "in_db"
},
{
"skill": "AWS CloudFormation",
"tag": "in_db"
},
{
"skill": "Docker",
"tag": "in_db"
},
{
"skill": "Kubernetes",
"tag": "in_db"
},
{
"skill": "Prometheus",
"tag": "in_db"
},
{
"skill": "Grafana",
"tag": "in_db"
},
{
"skill": "ELK Stack",
"tag": "new"
},
{
"skill": "DevSecOps",
"tag": "in_db"
},
{
"skill": "Code Review",
"tag": "in_db"
},
{
"skill": "AWS",
"tag": "in_db"
},
{
"skill": "Azure",
"tag": "in_db"
},
{
"skill": "GCP",
"tag": "in_db"
},
{
"skill": "Helm",
"tag": "in_db"
},
{
"skill": "PowerShell",
"tag": "in_db"
},
{
"skill": "Ansible",
"tag": "in_db"
},
{
"skill": "Puppet",
"tag": "in_db"
},
{
"skill": "OpenShift",
"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": "Messaging and Event Streaming",
"id": 8,
"rationale": "Transport-layer systems used to move events and decouple producers from consumers. Data engineers use these systems to ingest, buffer, and distribute event data before downstream processing.",
"slug": "messaging-and-event-streaming",
"source": "db"
},
"dimension_id": 8,
"input_skill": "Apache Kafka",
"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": "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": "Data Engineer",
"id": 2,
"rationale": null,
"role_archetype": null,
"slug": "data-engineer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 145,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"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": "Apache Spark",
"llm_role": null,
"matched_chosen_role": true,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension saved",
"role_dimension_saved": true,
"roles_from_db": [
{
"display_name": "Data Engineer",
"id": 2,
"rationale": null,
"role_archetype": null,
"slug": "data-engineer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 1350,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 2,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Cloud Data Warehouses",
"id": 22,
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"source": "db"
},
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"matched_chosen_role": true,
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"role_dimension_saved": true,
"roles_from_db": [
{
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"slug": "data-engineer",
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],
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},
{
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"slug": "cloud-data-warehouses",
"source": "db"
},
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"input_skill": "Google BigQuery",
"llm_role": null,
"matched_chosen_role": true,
"outcome_line": "Skipped \u2014 no persistable v3 meta for new skill",
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "Data Engineer",
"id": 2,
"rationale": null,
"role_archetype": null,
"slug": "data-engineer",
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}
],
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"skill_id": null,
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},
{
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"dimension": {
"difficulty_hint": "well_known",
"display_name": "Cloud Platforms \u0026 Hosting Providers",
"id": 278,
"rationale": "Familiarity with vendor-specific hosting and backend services for deploying and scaling web applications.",
"slug": "cloud-platforms-hosting-providers",
"source": "db"
},
"dimension_id": 278,
"input_skill": "AWS S3",
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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": ".NET Backend Developer",
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{
"display_name": "Kotlin Backend Developer",
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{
"display_name": "Scala Backend Developer",
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{
"display_name": "Web Developer",
"id": 25,
"rationale": null,
"role_archetype": null,
"slug": "web-developer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 1460,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 2,
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"slug": "cloud-platforms-managed-services",
"source": "db"
},
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"input_skill": "AWS S3",
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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": "Fullstack Developer",
"id": 15,
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"slug": "full-stack-engineer",
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},
{
"display_name": "Go Backend Developer",
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},
{
"display_name": "Node.js Backend Developer",
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],
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"skill_id": 1460,
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},
{
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"display_name": "Cloud Security Scripting \u0026 DSL Languages",
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"rationale": "Proficiency in programming and domain-specific languages used to automate and script cloud security controls.",
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"source": "db"
},
"dimension_id": 248,
"input_skill": "Python",
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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": "Cloud Security Engineer",
"id": 23,
"rationale": null,
"role_archetype": null,
"slug": "cloud-security-engineer",
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}
],
"skill_dimension_saved": true,
"skill_id": 5,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 2,
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"difficulty_hint": "well_known",
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"slug": "programming-languages",
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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)",
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"roles_from_db": [
{
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"slug": "backend-engineer",
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},
{
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},
{
"display_name": "Fullstack Developer",
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"slug": "fullstack-developer",
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}
],
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"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 2,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Programming Languages and Scripting",
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"slug": "programming-languages-and-scripting",
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},
"dimension_id": 59,
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"matched_chosen_role": false,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
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"roles_from_db": [
{
"display_name": "Cyber Security Engineer",
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"role_archetype": null,
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}
],
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},
{
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"display_name": "Programming Languages for Data Work",
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"slug": "programming-languages-for-data-work",
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},
"dimension_id": 21,
"input_skill": "Python",
"llm_role": null,
"matched_chosen_role": true,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension saved",
"role_dimension_saved": true,
"roles_from_db": [
{
"display_name": "Data Engineer",
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"slug": "data-engineer",
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}
],
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"skill_id": 5,
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},
{
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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": [
{
"display_name": "ML Engineer",
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"slug": "ml-engineer",
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},
{
"display_name": "MLOps Engineer",
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"slug": "ml-ops-engineer",
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}
],
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},
{
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"display_name": "Programming Languages for XR",
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"rationale": "Primary implementation languages used to build immersive client features, interaction logic, and device-specific runtime behavior. This is the core coding surface for AR/VR experiences.",
"slug": "programming-languages-for-xr",
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},
"dimension_id": 97,
"input_skill": "Python",
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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": "AR/VR Engineer",
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],
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},
{
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},
"dimension_id": 290,
"input_skill": "Python",
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"matched_chosen_role": false,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
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"roles_from_db": [
{
"display_name": "Python Backend Developer",
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],
"skill_dimension_saved": true,
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},
{
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"display_name": "Pega Programming Languages \u0026 DSLs",
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},
"dimension_id": 267,
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"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
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"roles_from_db": [
{
"display_name": "Pega Developer",
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}
],
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"skill_id": 101,
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},
{
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},
"dimension_id": 21,
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"matched_chosen_role": true,
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"role_dimension_saved": true,
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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": "Data Engineer",
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],
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},
{
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},
"dimension_id": 23,
"input_skill": "Apache Airflow",
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"roles_from_db": [
{
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"slug": "data-engineer",
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}
],
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},
{
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"rationale": "Systems used to define, run, and maintain automated build and deployment workflows. This cluster is coherent because the role owns delivery automation end to end, including pipeline reliability and promotion logic.",
"slug": "ci-cd-pipeline-platforms",
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},
"dimension_id": 150,
"input_skill": "Jenkins",
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"matched_chosen_role": false,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
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"roles_from_db": [
{
"display_name": "DevOps Engineer",
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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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"roles_from_db": [
{
"display_name": "ML Engineer",
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}
],
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},
{
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"slug": "ci-cd-pipeline-platforms",
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},
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"input_skill": "GitLab CI",
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"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
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"roles_from_db": [
{
"display_name": "DevOps Engineer",
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}
],
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"skill_id": 282,
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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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"roles_from_db": [
{
"display_name": "ML Engineer",
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}
],
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},
{
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"display_name": "CI/CD Pipeline Platforms",
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},
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"input_skill": "Azure DevOps",
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"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "DevOps Engineer",
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],
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},
{
"chosen_role_id": 2,
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"display_name": "Infrastructure \u0026 Security Automation Frameworks",
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"rationale": "Frameworks and libraries for provisioning, configuring, and automating cloud security infrastructure.",
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"source": "db"
},
"dimension_id": 249,
"input_skill": "Terraform",
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"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "Cloud Security Engineer",
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"role_archetype": null,
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}
],
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"skill_id": 286,
"skill_tag": "in_db",
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},
{
"chosen_role_id": 2,
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"difficulty_hint": "well_known",
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"rationale": "Declarative provisioning and environment definition tools used to codify cloud infrastructure, repeatable environments, and platform standards. Cloud Architects use these to express reference architectures and guardrails.",
"slug": "infrastructure-as-code",
"source": "db"
},
"dimension_id": 132,
"input_skill": "Terraform",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "Cloud Architect",
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"rationale": null,
"role_archetype": null,
"slug": "cloud-architect",
"source": "db"
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LLM Calls
Every model call made for this run, in pipeline order. Click a card to see the model's response.