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.,



Outstanding 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

RE

📌 Data Quality Assurance Lead Howrah
🏢 Finarb
📍 Howrah