QA Engineer- Data Engineering

India Health Link; Bengaluru; On-site; INR 900,000 - 1,500,000; Full time

Job summary

India Health Link in Bengaluru is seeking a QA Engineer to own quality assurance for data engineering projects and provide QA support across teams. You will validate data pipelines, write SQL for data quality, and contribute to testing of web and mobile apps as needed.

You will collaborate with data engineers, design test plans, build automated data quality checks, and help improve QA processes. Experience with ETL/ELT, data warehousing concepts, and tools like Jira, Power BI/Tableau is a plus.

Qualifications

• Experience testing data pipelines, ETL/ELT projects.
• Strong SQL skills to validate data quality independently.
• Knowledge of data warehousing concepts and data models.
• Experience with Jira and Agile/Scrum workflows.

Responsibilities

• Validate end-to-end data pipelines (source-to-target) and data completeness.
• Write and run SQL queries to test data quality across ETL/ELT workflows.
• Design and maintain test plans, cases, and traceability for data projects.
• Build automated data quality checks and integrate into pipelines.
• Review reports/dashboards (Power BI/Tableau) against source data.
• Perform smoke, regression, and integration testing after deployments.
• Support testing on web/app projects when required.
• Log defects and track resolution with engineers.

Skills

SQL queries

Data pipelines testing

Jira

Agile/Scrum

Data quality

Communication

Tools

Azure Data Factory

Azure Synapse

Databricks

Power BI

Tableau

Python

pytest

Job description

We're looking for a QA Engineer to own quality assurance for our data engineering projects, while also providing QA support across other project teams as needed. You'll work closely with data engineers to validate pipelines and data products, and step in on functional testing for web and app projects when required. This role is ideal for someone who is detail-oriented, comfortable working with data at scale, and able to juggle priorities across multiple teams.

Responsibilities

• Validate data pipelines end-to-end: source-to-target checks, transformations, data completeness, accuracy, and reconciliation
• Write and execute SQL queries to test data quality across ETL/ELT workflows
• Design and maintain test plans, test cases, and traceability documentation for data projects
• Build automated data quality checks and integrate them into pipeline workflows
• Test reports and dashboards (e.g., Power BI/Tableau) against source data to verify metrics and business logic
• Perform smoke, regression, and integration testing after pipeline deployments and schema changes
• Support functional, regression, and UAT testing on other projects (web/app) when required
• Log, track, and verify defects; work closely with data engineers and developers through resolution
• Participate in requirement reviews to identify testability gaps and edge cases early
• Contribute to improving QA processes, standards, and documentation across teams

Requirements

• Experience testing data pipelines, ETL, or data warehouse projects
• Strong SQL skills — able to write complex queries for data validation independently
• Understanding of data warehousing concepts (schemas, transformations, incremental loads, slowly changing dimensions)
• Experience with test management and defect tracking tools (e.g., Jira)
• Ability to read and understand pipeline logic and data models to design meaningful tests
• Strong attention to detail and ability to switch context across multiple projects
• Good communication skills for working with cross-functional teams
• Exposure to Agile/Scrum ways of working

Nice to Have

• Experience with Azure data services (Azure Data Factory, Synapse, ADLS)
• Hands-on experience with Databricks (notebooks, Delta Lake, job runs)
• Test automation skills — Python, pytest, or data quality frameworks like Great Expectations or dbt tests
• Experience testing APIs (e.g., Postman) or basic web automation (e.g., Selenium/Playwright)
• Familiarity with CI/CD pipelines (e.g., Azure DevOps, GitHub Actions)