Senior QA Engineer

Job Title :  QA Engineer – Data & Analytics

Location: Pune/ Mumbai/ Kochi / Chennai/ Coimbatore

Mode of Work: Hybrid

Role Description / Responsibilities

A technically skilled QA professional specializing in validating data platforms, ETL pipelines, analytical models, and large-scale data transformations. The QA Engineer works closely with developers, data engineers, product teams, and business SMEs to ensure the quality, reliability, and accuracy of data assets across the delivery lifecycle of our digital data audit products.

Core Responsibilities

Data Testing & Quality Assurance

• Design, develop, and execute test plans for data & analytics products, including ingestion pipelines, transformations, data models, and dashboards.

• Apply robust testing techniques for data completeness, consistency, accuracy, timeliness, and lineage.

Technical & Platform Responsibilities

• Write and execute SQL/PLSQL queries for data verification, profiling, and root cause analysis.

• Use Databricks to inspect data, validate notebook logic, execute test scripts, and investigate transformation failures.

• Work with Delta Lake, Spark DataFrames, and related components to test scalable data processing.

ETL/ELT and Pipeline Testing

• Validate end-to-end ETL/ELT workflows.

• Ensure correctness of data transformations and seamless movement between data layers.

• Conduct regression testing following schema evolution, code updates, or pipeline refactoring.

Collaboration & Delivery Practices

• Work closely with Data Engineers, Developers in Test, Business SMEs, and Product Owners to ensure high-quality delivery.

• Provide clarity on data issues, identify root causes, and support resolution.

• Participate in refinement sessions to assess the testability of new features and data logic.

• Contribute to automation frameworks, promoting continuous integration of data tests.

Automation & Scripting (Desirable)

• Develop automated test scripts using Python, PySpark, or Pandas.

• Contribute to automated test frameworks that validate transformations and data quality at scale.

Skills & Experience

Mandatory

• Proven experience testing data and analytics platforms.

• Advanced SQL and PL/SQL skills.

• Hands-on experience with Databricks (notebooks, Delta Lake, Spark).

• Strong understanding of ETL concepts and data transformation methodologies.

• Experience validating large datasets, data pipelines, and data models.

Desirable

• Python for test automation and data manipulation.

• PySpark for distributed data testing.

• Pandas for lightweight automation and exploratory validation.

• Exposure to CI/CD, data observability tools, or version control (Git).