As a recruitment company, DCG understands that every business is powered by experienced professionals. Our management style and partnership approach enable us to meet your needs and provide continuous support. Due to our ongoing growth and the large number of recruitment projects we undertake for our partners, we are currently looking for:QA Engineer (Data & Analytics)We are looking for candidates who are available immediately, with a maximum notice period of two weeks.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