Data & Analytics QA Specialist

We’re looking for a QA Engineer with strong hands-on skills to help ensure high quality across our data and analytics work. In this role, you’ll design and run tests for data pipelines and ETL workflows, build and maintain automation, and work closely with Data Engineers and business stakeholders to validate data accuracy and reliability.

If you enjoy digging into data, improving testing frameworks, and building practical automation that catches issues early, you’ll feel at home here.

Responsibilities:

• Design and improve testing processes for data pipelines, ETL workflows, and analytics outputs.

• Develop and maintain automated tests and utilities in Python, PySpark, and Spark SQL to validate transformations and data integrity.

• Execute and report on test cases for new features and bug fixes using appropriate tools and frameworks.

• Collaborate with Data Engineers to validate end-to-end pipelines and support delivery during busy periods (including occasional help with data transformations).

• Apply best practices across test automation, integration testing, performance testing, and manual testing where needed.

• Write clean, scalable test code that’s easy to maintain and provides good coverage.

• Create and maintain documentation for test processes, test cases, and results.

• Work with stakeholders to understand testing needs and recommend practical solutions aligned with business goals.

• Build simple Power BI dashboards to track test results and quality metrics.

Requirements:

• Strong hands-on experience with Python (automation, scripting, data validation).

• Good working knowledge of PySpark and Spark SQL, especially for testing large-scale data transformations.

• Experience with Azure Data Factory for orchestrating and validating ETL workflows.

• Experience with Azure Databricks for data processing and testing data pipelines at scale.

• Ability to build simple Power BI reports for visibility into QA results.

• Familiarity with testing frameworks and approaches used in data/analytics environments.

• Understanding of QA practices for data-centric systems (automation, integration, performance testing, data quality checks).

• Strong communication skills and a collaborative mindset.