About This Role
This role is for an ETL QA Engineer with strong experience in Databricks and PySpark, combined with hands-on exposure to AI-assisted development and prompt engineering. The ideal candidate will validate large-scale data pipelines, ensure data quality, and leverage tools like GitHub Copilot, GitHub Actions/Runners, and AI agents to improve testing efficiency and coverage. You will also help build and validate 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
- Help build and validate AI-powered knowledge bases
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
