QA Engineer- Data Engineering

Phigital Care; Bengaluru; Presencial; INR 1.200.000 - 2.400.000; Jornada completa

Descripción de la vacante

Phigital Care is seeking a QA Engineer to own quality assurance for data engineering projects and provide QA support across teams. You will validate pipelines, write SQL queries, and contribute to test plans while supporting web/app testing when needed.

Ideal candidates are detail-oriented, comfortable handling data at scale, and able to juggle priorities across multiple projects in an Agile environment. This role involves cross-functional collaboration and continuous QA improvements.

Formación

• Experience testing data pipelines, ETL, or data warehouse projects.
• Strong SQL skills to write complex queries for data validation.
• Understanding of data warehousing concepts and slowly changing dimensions.
• Experience with Jira or similar test management tools.
• Good communication and ability to work across cross-functional teams.
• Agile/Scrum experience.

Responsabilidades

• Validate data pipelines end-to-end with source-to-target checks.
• Write and execute SQL queries to test data quality.
• Design and maintain test plans, cases, and traceability.
• Build automated data quality checks and integrate into pipelines.
• Test reports/dashboards (Power BI/Tableau) against source data.
• Perform smoke, regression, and integration testing after deployments.
• Support functional, regression, and UAT testing on web/app projects.
• Log and track defects; collaborate with data engineers to resolve.

Conocimientos

Data quality testing

SQL

ETL/ELT testing

Data warehousing

Test automation

Jira

Agile/Scrum

Communication

Educación

Bachelor's degree in Computer Science or related field

Herramientas

Power BI

Tableau

Descripción del empleo

About The Role

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)