Job Description

Data QA Engineer

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Location : Kolkata, India (Onsite/Hybrid)

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Experience : 2–4 years

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Employment Type: Full time

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ABOUT THE ROLE

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

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validating ETL pipelines and data platforms built on Azure Data Factory, Databricks, and

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Microsoft Fabric, along with the downstream tables, reports, and models they feed. The core

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

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

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separate.

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KEY RESPONSIBILITIES

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- Design and execute test plans covering source-to-target validation and transformation logic
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- for ETL pipelines
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- Write SQL and PySpark scripts to verify accuracy, completeness, and consistency in Delta
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- Lake tables
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- Perform regression testing on every pipeline change; a successful pipeline run does not
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- guarantee correct output, and validation must be independent of execution status
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- Build and maintain reusable data quality checks (e.g., Great Expectations, dbt tests, or
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- custom PySpark frameworks) in place of one-off manual queries
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- Reconcile data between source systems and target Lakehouse/Warehouse layers, and
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- investigate root cause when discrepancies are found
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- Validate schema conformance, null/duplicate handling, referential integrity, and business
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- rule adherence across Bronze/Silver/Gold layers
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- Validate Microsoft Fabric artifacts — Lakehouses, Warehouses, and semantic models —
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- including DirectLake mode behavior and cross-domain data consistency
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- Apply consistent QA methodology across tools; the underlying platform (Databricks, Fabric,
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- or otherwise) should not change how rigorously data is validated
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- Document test cases and defects with enough detail for engineers to act on them without
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- requiring additional clarification
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- Work directly with the data engineering team on requirement clarification and defect
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- resolution
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REQUIRED SKILLS

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- Strong SQL, with the ability to write validation queries independently
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- Working proficiency in Python/PySpark for scripting data checks
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- Solid understanding of ETL concepts: staging, transformations, incremental loads, SCD
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- handling
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- Hands-on experience with Azure Data Factory and Databricks
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- Working knowledge of Microsoft Fabric (Lakehouse, Warehouse, semantic models)
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- Familiarity with Delta Lake and medallion architecture
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- Ability to read transformation logic and determine expected output
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- General data QA methodology that transfers across tools and platforms, not skills tied to a
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- single vendor stack
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- Experience with defect tracking and structured test documentation (Jira, TestRail, or
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- equivalent) PREFERRED QUALIFICATIONS
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- Experience with a data quality framework (Great Expectations, Deequ, dbt tests)
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- Familiarity with Unity Catalog and general data governance/lineage tooling
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- Experience validating pipelines in a regulated domain (pharma, healthcare, finance) where
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- lineage and auditability are requirements
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- Exposure to CI/CD for test automation
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- Azure, Databricks, or Fabric certification
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QUALIFICATIONS

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

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

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experience without pipeline exposure does not meet this requirement

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CANDIDATE FIT

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

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

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

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data depth is expected from day one.

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