Senior Data Quality Automation Engineer

Best Buy Canada

2d•$100,000 - $106,000•Remote

About The Position

Are you driven by the challenge of validating complex data systems and ensuring the reliability of data products that power business‑critical decisions? Do you excel at debugging distributed data workflows, analyzing transformations, and engineering automated validation solutions for large‑scale data environments? We are expanding our Data Quality Engineering function and seeking technically strong contributors who can design, implement, and optimize automated data validation frameworks across our data platform. As a senior data quality automation engineer, you will work closely with data engineering, analytics, and platform teams to enforce rigorous data quality standards, embed automated validation into data lifecycle processes, and ensure the correctness and stability of our pipelines and data products. We practice a remote‑first model working model, leveraging in-person interactions at our head office, in beautiful Vancouver, BC for strategic, collaborative, and social purposes. Apply now and grow your career with our technology team as a senior data quality automation engineer!

Requirements

5+ years of Python development experience in data‑focused environments.
3+ years of advanced test automation experience with PyTest, Selenium, Playwright, or RestAssured.
2+ years leading automation or data quality initiatives within an agile setting.
Expertise designing and implementing CI/CD‑based automation pipelines for data validation.
Strong background in developing or architecting testing frameworks for large‑scale or distributed data systems.
Direct experience validating complex ETL systems, transformation pipelines, and analytics/reporting layers.
Proficiency with Airflow, Spark, Hive, SQL, Allure, Great Expectations, and related technologies.

Responsibilities

Build and maintain automated data validation frameworks to ensure accuracy, completeness, and integrity across ETL pipelines and downstream consumer systems.
Develop Python‑based automated tests for source‑to‑target validation, business logic verification, schema enforcement, anomaly detection, and historical data consistency.
Validate data logic and end‑to‑end flows across batch and streaming data systems using Airflow, Spark, Hive, SQL, and related tooling.
Embed automated data quality checks into CI/CD pipelines to enable continuous validation, high signal‑to‑noise defect detection, and rapid feedback across environments.
Collaborate with product, data engineering, analytics, and platform teams to define data quality requirements, validation criteria, and coverage strategies.
Investigate data issues across pipelines and distributed systems, perform detailed root‑cause analysis using logs and data snapshots, and document actionable, reproducible defects in JIRA.