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About the Role
We are seeking an ETL QA Engineer experienced in Databricks and PySpark, with exposure to AI-assisted development and prompt engineering. This role involves validating large-scale data pipelines, ensuring data quality, and utilizing AI tools to enhance testing efficiency and coverage. You will also contribute to building and validating AI-powered knowledge bases.
Responsibilities
- Validate large-scale data pipelines.
- Ensure data quality.
- Leverage tools like GitHub Copilot, GitHub Actions/Runners, and AI agents to improve testing efficiency and coverage.
- Contribute to building and validating AI-powered knowledge bases.
- Craft effective prompts for large language models (LLMs) for coding, testing, and documentation.
- Build AI-driven knowledge bases or documentation systems.
- Translate complex data/QA concepts into clear knowledge articles.
Requirements
- Strong hands-on experience in Databricks (notebooks, jobs, clusters, Delta Lake, SQL).
- Expert-level PySpark skills, including transformations, actions, window functions, and optimization techniques.
- Solid understanding of ETL/ELT concepts, data warehousing, and data modeling (star/snowflake schemas, dimension/fact tables).
- Proven experience in data validation, data quality checks, and reconciliation techniques.
- Practical experience with GitHub, GitHub Copilot, and GitHub Actions/Runners for CI/CD pipelines.
- Experience crafting effective prompts for large language models (LLMs) for coding, testing, and documentation.
- Exposure to agent-based architectures or tools (e.g., workflow/agent frameworks that orchestrate multi-step AI tasks).
- Familiarity with AI-based tools for code analysis, test generation, or knowledge management.
- Experience building AI-driven knowledge bases or documentation systems (e.g., using vector search, embeddings, or LLM-based retrieval).
- Strong documentation skills, with the ability to translate complex data/QA concepts into clear knowledge articles.
- Strong understanding of QA methodologies: test planning, test design, defect management, and regression testing.
- Experience with automated testing frameworks (Python-based testing libraries such as pytest, unittest, or similar).
- Familiarity with data quality tools/practices (e.g., validation rules, thresholds, anomaly detection).
- Strong analytical and problem-solving skills with a detail-oriented mindset.
- Passion for data quality, automation, and continuous improvement.
- Ability to work in an agile, fast-paced environment and collaborate across multiple teams like Business and operations team and AD team.
- Curiosity and openness to adopting new AI tools and practices for QA.
Skills
- Databricks
- PySpark
- ETL/ELT
- Data Warehousing
- Data Modeling
- Data Validation
- Data Quality
- Reconciliation Techniques
- GitHub
- GitHub Copilot
- GitHub Actions/Runners
- CI/CD
- Prompt Engineering
- Large Language Models (LLMs)
- Agent-based Architectures
- AI-based Tools
- Knowledge Base Development
- Vector Search
- Embeddings
- LLM-based Retrieval
- Documentation
- QA Methodologies
- Test Planning
- Test Design
- Defect Management
- Regression Testing
- Automated Testing Frameworks
- pytest
- unittest
- Data Quality Tools
- Agile Methodologies
- Problem-Solving
- Analytical Skills