Qa Engineer

Key Responsibilities:

Quality Engineering & Test Automation

• Develop, execute, and maintain automated and manual test cases for web applications, APIs, and data platforms. Contribute to UI, API, integration, regression, and end-to-end test automation suites. Execute test plans and document test evidence to support release readiness. Identify, document, and track software defects through resolution. Participate in sprint planning, backlog refinement, defect triage, and release activities.

Data Platform Testing

• Validate data flows, transformations, analytical datasets, and data quality rules. Verify monitoring processes, dashboards, alerts, and reporting outputs to ensure data accuracy and reliability. Perform data validation and reconciliation using SQL and data analysis techniques. Support testing of ETL/ELT pipelines and cloud-based data platforms.

Cloud, API & Platform Testing

• Test REST APIs, cloud-native applications, and microservices. Validate integrations between applications, services, and data platforms. Support functional, integration, regression and performance testing activities. Participate in CI/CD-based testing and release validation activities.

Engineering Excellence

• Follow automation-first and shift-left testing practices. Collaborate with engineering teams to improve product quality and test coverage. Contribute to test automation frameworks and reusable quality assets. Continuously learn emerging technologies, testing tools, and quality engineering best practices.

Key Requirements:

• 3-5 years of Quality Engineering experience in enterprise software, data platforms, or cloud-native applications.
• Intermediate SQL and Python (PyTest, Pandas) skills for data validation, reconciliation, and test automation.
• Experience validating data flows, ETL/ELT workflows, data transformations, and analytical datasets.
• Familiarity with data quality dimensions (completeness, accuracy, consistency, timeliness, uniqueness, and validity).
• Exposure to data quality/testing frameworks such as Great Expectations, Soda, dbt Tests, or equivalent.
• Basic understanding of Airflow DAGs, batch/streaming pipelines, backfills, and late-arriving data scenarios.
• Experience with test automation tools such as Playwright, Selenium, Cypress, Postman, PyTest, and Rest Assured.
• Experience testing web applications, REST APIs, microservices, and data integrations using modern technologies such as React/Angular, JavaScript, TypeScript, Python, and FastAPI.
• Familiarity with GCP (BigQuery, Airflow, Cloud Storage), databases (PostgreSQL, MySQL, MongoDB), CI/CD pipelines, Git, and Agile methodologies.
• Strong analytical, problem-solving, communication, and collaboration skills, with a solid understanding of functional, integration, regression, performance, and security testing.

Preferred Qualifications:

• Experience with Data Quality, Data Governance, Data Observability, or Analytics platforms.
• Experience testing cloud data platforms such as BigQuery, Airflow/Composer, Dataflow, or Dataproc.
• Knowledge of data quality frameworks, rule-based validation, metadata-driven systems, and monitoring solutions.
• Experience testing dashboards and reporting platforms such as Power BI or Tableau.
• Exposure to anomaly detection using LLM-enabled methods, or intelligent automation solutions.
• Experience using AI-assisted engineering tools such as GitHub Copilot, Gemini, Claude, Cursor, or similar platforms.