QA Engineer (Data & Analytics)

Responsibilities:

• Define and implement data quality validation strategies across pipelines, data lakes, and warehouses
• Verify data consistency, integrity, completeness, and transformations from source systems to downstream analytics platforms like ThoughtSpot and Databricks
• Develop and execute end-to-end data validation and reconciliation tests to ensure accurate reporting and insights
• Work with engineers to implement data contracts, schema validations, and anomaly detection mechanisms using tools like Great Expectations
• Perform exploratory data testing, investigating discrepancies and ensuring data correctness across multiple environments
• Validate data migrations, ensuring business continuity and minimal disruption during cloud transitions (e.g. Databricks to GCP migration)
• Collaborate with data engineers, analysts, and software engineers to establish and enforce data quality standards
• Work closely with teams to define data validation criteria, SLAs for data freshness, and monitoring strategies
• Participate in design and planning discussions to ensure data observability, lineage tracking, and governance are embedded early
• Champion best practices in data testing, monitoring, and alerting, ensuring quick detection and resolution of data quality issues
• Leverage data quality metrics to identify trends, gaps, and areas for improvement
• Explore and advocate for new tools, frameworks, and methodologies to enhance data testing efficiency
• Support data performance testing by validating query performance, scalability, and system reliability
• Assist with data security, compliance, and privacy testing, ensuring alignment with regulatory and organisational standards

Requirements:

• Proven experience in QA for data projects, with a focus on data validation, pipelines, and analytics testing
• Strong understanding of data engineering workflows, ETL processes, and data lake architectures
• Experience testing data transformations, APIs, and integrations in cloud-based environments (AWS, GCP, or Azure)
• Familiarity with data quality frameworks like Great Expectations or similar tools
• Hands-on experience working with SQL for data validation and reconciliation
• Knowledge of BI and analytics tools (e.g. ThoughtSpot, Power BI, Looker, Tableau) and how to validate reports
• Excellent communication skills, with the ability to collaborate and advocate for quality across teams
• Detail-oriented, analytical, and proactive, with a passion for data quality and accuracy
• A natural problem solver who is able to investigate data issues and drive resolutions
• Strong collaborator and communicator, bridging gaps between QA, engineering, and data teams
• Adaptable and curious, keen to explore emerging data quality trends and best practices
• Knowledge of English at B2 level (minimum)

Nice to have:

• Experience in testing large-scale data migrations (e.g. Databricks to GCP)
• Understanding of data observability, lineage tracking, and monitoring tools
• Experience with performance testing for data platforms (query performance, scalability)
• Awareness of data security and governance best practices
• Knowledge of machine learning model validation and AI testing

Offer:

• Private medical care
• Co-financing for the sports card
• Training & learning opportunities
• Constant support of dedicated consultant
• Employee referral program