Data QA Engineer

Location: Kolkata, India (Onsite/Hybrid)

Experience: 2–4 years

Employment Type: Full time

ABOUT THE ROLE

We are hiring a Data QA Engineer for our data engineering team. This role is responsible for

validating 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 core

responsibility is verifying data correctness, which is distinct from confirming that a pipeline

executed without errors — the two are frequently conflated, and this role exists to keep them

separate.

KEY RESPONSIBILITIES

- Design and execute test plans covering source-to-target validation and transformation logic
- for ETL pipelines
- Write SQL and PySpark scripts to verify accuracy, completeness, and consistency in Delta
- Lake tables
- Perform regression testing on every pipeline change;
a successful pipeline run does not
- guarantee correct output, and validation must be independent of execution status
- Build and maintain reusable data quality checks (e.G., Great Expectations, dbt tests, or
- custom PySpark frameworks) in place of one-off manual queries
- Reconcile data between source systems and target Lakehouse/Warehouse layers, and
- investigate root cause when discrepancies are found
- Validate schema conformance, null/duplicate handling, referential integrity, and business
- rule adherence across Bronze/Silver/Gold layers
- Validate Microsoft Fabric artifacts — Lakehouses, Warehouses, and semantic models —
- including DirectLake 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 without




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

REQUIRED SKILLS

- Strong SQL, with the ability to write validation queries independently
- Working proficiency in Python/PySpark for scripting data checks
- Solid understanding of ETL concepts: staging, transformations, incremental loads, SCD
- handling
- 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 a
- single vendor stack
- Experience with defect tracking and structured test documentation (Jira, TestRail, or
- equivalent)PREFERRED QUALIFICATIONS
- Experience 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) where
- lineage and auditability are requirements
- Exposure to CI/CD for test automation
- Azure, Databricks, or Fabric certification

QUALIFICATIONS

Bachelor’s degree in Computer Science, IT, or a related field

2–4 years of experience in data QA, data validation, or ETL testing;
manual/UI testing

experience without pipeline exposure does not meet this requirement

CANDIDATE FIT

This role requires the ability to determine why a data discrepancy occurred, not simply flag

that one exists. Candidates whose QA background is primarily manual/UI testing and who are

seeking to transition into data-focused work should not apply for this position;
the required

datadepth is expected from day one.

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