Data QA Engineer

As a Data QA Engineer, you will ensure the reliability, accuracy and performance of our client’s data solutions. Operating remotely, you will work closely with Data Engineers, Architects and Analysts to design and execute automated testing strategies that validate data pipelines, integrations and transformations across cloud platforms. Your work will underpin data quality and ensure that insights are built on trusted, consistent data.

• Design and implement automated tests for data pipelines, ETL processes and APIs across AWS and Azure environments.
• Develop and maintain test frameworks that validate data quality, completeness and integrity.
• Collaborate with engineering teams to identify data defects, root causes and performance issues.
• Support continuous integration and deployment by embedding automated testing into CI/CD pipelines.
• Validate source-to-target data mapping, transformations and business logic.
• Produce clear test documentation and communicate test results to technical and non-technical stakeholders.
• Monitor data test coverage and define quality metrics for ongoing improvement.
• Contribute to process refinement, ensuring scalable and efficient data testing practices.

• Strong understanding of data engineering concepts, data models and ETL workflows.
• Experience creating automated data validation scripts or frameworks using SQL, Python, or PySpark.
• Proficient in testing data within cloud ecosystems (AWS, Azure (preferred) or GCP).
• Experience with testing frameworks or tools such as PyTest, Great Expectations, or similar.
• Familiarity with CI/CD practices and tools such as Git, Jenkins, or Azure DevOps.
• Ability to diagnose complex data issues and work collaboratively with engineering teams to resolve them.
• Strong analytical mindset and attention to detail.
• Clear, concise communicator able to present technical findings simply.

• Experience testing in big data environments using Databricks, Snowflake, or Redshift.
• Knowledge of data governance and lineage tracking tools.
• Exposure to data performance and load testing.
• Experience in an Agile delivery environment.