Role Overview

We are looking for an ETL QA Engineer with solid experience in
Databricks and PySpark, combined with hands-on exposure to AI-assisted
development and prompt engineering. The ideal candidate will be responsible for
validating large-scale data pipelines, ensuring data quality, and leveraging
tools like GitHub Copilot, GitHub Actions/Runners, and AI agents to improve
testing efficiency and coverage. You will also contribute to building and
validating AI-powered knowledge bases.

Required Skills & Experience

- Core
Technical Skills

- 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.

- 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.

📌 ETL QA (Toronto)
🏢 Leading
📍 Toronto