ETL QA

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.

• AI &

Prompt Engineering

• 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.

• Knowledge

Base & Documentation

• 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.

• Testing & QA Practices
• 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).

Key Attributes

• 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.