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

- Design and execute test plans covering source-to-target validation and transformation logic

/n

- for ETL pipelines

/n

- Write SQL and PySpark scripts to verify accuracy, completeness, and consistency in Delta

/n

- Lake tables

/n

- Perform regression testing on every pipeline change; a successful pipeline run does not

/n

- guarantee correct output, and validation must be independent of execution status

/n

- Build and maintain reusable data quality checks (e.g., Outstanding Expectations, dbt tests, or

/n

- custom PySpark frameworks) in place of one-off manual queries

/n

- Reconcile data between source systems and target Lakehouse/Warehouse layers, and

/n

- investigate root cause when discrepancies are found

/n

- Validate schema conformance, null/duplicate handling, referential integrity, and business

/n

- rule adherence across Bronze/Silver/Gold layers

/n

- Validate Microsoft Fabric artifacts — Lakehouses, Warehouses, and semantic models —

/n

- including DirectLake mode behavior and cross-domain data consistency

/n

- Apply consistent QA methodology across tools; the underlying platform (Databricks, Fabric,

/n

- or otherwise) should not change how rigorously data is validated

/n

- Document test cases and defects with enough detail for engineers to act on them without

/n





- requiring additional clarification

/n

- Work directly with the data engineering team on requirement clarification and defect

/n

- resolution

/n /n REQUIRED SKILLS /n /n

- Strong SQL, with the ability to write validation queries independently

/n

- Working proficiency in Python/PySpark for scripting data checks

/n

- Solid understanding of ETL concepts: staging, transformations, incremental loads, SCD

/n

- handling

/n

- Hands-on experience with Azure Data Factory and Databricks

/n

- Working knowledge of Microsoft Fabric (Lakehouse, Warehouse, semantic models)

/n

- Familiarity with Delta Lake and medallion architecture

/n

- Ability to read transformation logic and determine expected output

/n

- General data QA methodology that transfers across tools and platforms, not skills tied to a

/n

- single vendor stack

/n

- Experience with defect tracking and structured test documentation (Jira, TestRail, or

/n

- equivalent) PREFERRED QUALIFICATIONS

/n

- Experience with a data quality framework (Great Expectations, Deequ, dbt tests)

/n

- Familiarity with Unity Catalog and general data governance/lineage tooling

/n

- Experience validating pipelines in a regulated domain (pharma, healthcare, finance) where

/n

- lineage and auditability are requirements

/n

- Exposure to CI/CD for test automation

/n

- Azure, Databricks, or Fabric certification

/n /n QUALIFICATIONS /n Bachelor’s degree in Computer Science, IT, or a related field /n 2–4 years of experience in data QA, data validation, or ETL testing; manual/UI testing /n experience without pipeline exposure does not meet this requirement /n CANDIDATE FIT /n This role requires the ability to determine why a data discrepancy occurred, not simply flag /n that one exists. Candidates whose QA background is primarily manual/UI testing and who are /n seeking to transition into data-focused work should not apply for this position; the required /n data depth is expected from day one. /n

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