Data QA Engineer Location: Kolkata, India (Onsite/Hybrid)Experience: 2–4 years Employment Type: Full-time ABOUT THE ROLEWe are hiring a Data QA Engineer for our data engineering team. This role is responsible forvalidating ETL pipelines and data platforms built on Azure Data Factory, Databricks, and Microsoft Fabric, along with the downstream tables, reports, and models they feed. The coreresponsibility is verifying data correctness, which is distinct from confirming that a pipelineexecuted without errors — the two are frequently conflated, and this role exists to keep themseparate.

KEY RESPONSIBILITIESDesign and execute test plans covering source-to-target validation and transformation logicfor ETL pipelines Write SQL and Py Spark scripts to verify accuracy, completeness, and consistency in Delta Lake tables Perform regression testing on every pipeline change; a successful pipeline run does notguarantee correct output, and validation must be independent of execution status Build and maintain reusable data quality checks (e.g., Outstanding Expectations, dbt tests, orcustom Py Spark frameworks) in place of one-off manual queries Reconcile data between source systems and target Lakehouse/Warehouse layers, andinvestigate root cause when discrepancies are found Validate schema conformance, null/duplicate handling, referential integrity, and businessrule adherence across Bronze/Silver/Gold layers Validate Microsoft Fabric artifacts — Lakehouses, Warehouses, and semantic models —including Direct Lake mode behavior and cross-domain data consistency Apply consistent QA methodology across tools; the underlying platform (Databricks, Fabric,or otherwise)



should not change how rigorously data is validated Document test cases and defects with enough detail for engineers to act on them withoutrequiring additional clarification Work directly with the data engineering team on requirement clarification and defectresolution REQUIRED SKILLSStrong SQL, with the ability to write validation queries independently Working proficiency in Python/Py Spark for scripting data checks Solid understanding of ETL concepts: staging, transformations, incremental loads, SCDhandling Hands-on experience with Azure Data Factory and Databricks Working knowledge of Microsoft Fabric (Lakehouse, Warehouse, semantic models)Familiarity with Delta Lake and medallion architecture Ability to read transformation logic and determine expected output General data QA methodology that transfers across tools and platforms, not skills tied to asingle vendor stack Experience with defect tracking and structured test documentation (Jira, Test Rail, orequivalent)PREFERRED QUALIFICATIONSExperience with a data quality framework (Great Expectations, Deequ, dbt tests)Familiarity with Unity Catalog and general data governance/lineage tooling Experience validating pipelines in a regulated domain (pharma, healthcare, finance) wherelineage and auditability are requirements Exposure to CI/CD for test automation Azure, Databricks, or Fabric certification QUALIFICATIONSBachelor’s degree in Computer Science, IT, or a related field2–4 years of experience in data QA, data validation, or ETL testing; manual/UI testingexperience without pipeline exposure does not meet this requirement CANDIDATE FITThis role requires the ability to determine why a data discrepancy occurred, not simply flagthat one exists. Candidates whose QA background is primarily manual/UI testing and who areseeking to transition into data-focused work should not apply for this position; the requireddata depth is expected from day one.

📌 Data Quality Assurance Lead (Kolkata)
🏢 Finarb
📍 Kolkata