SDET

Key Responsibilities: Engaging in the planning and design process. Validate ETL and data workflows running on Azure Databricks for accuracy, performance, and reliability. Ensuring code reliability and robustness along with function specificity Design, develop, and maintain automated test frameworks for data pipelines, APIs, and containerized services. Build and execute integration, performance, and regression test suites for Flask-based APIs. Implement CI/CD quality gates in containerized deployments (Kubernetes). Collaborate with data engineers, software developers, and DevOps teams to identify test gaps and drive quality improvements. Monitor and troubleshoot test failures, ensuring timely resolution of defects. Contribute to best practices for testing in cloud-native and data-intensive environments. Required Skills & Experience: Strong programming skills in Python, with proven experience writing test automation frameworks. Adept with white box testing. Hands-on experience testing data platforms (preferably Azure Databricks, Spark, or other big data frameworks). Experience with API testing (REST/Flask) using frameworks like PyTest, unittest, or similar. Proficiency in Kubernetes, Docker, and containerized environments. Familiarity with CI/CD tools (e.g., Azure DevOps, GitHub Actions, Jenkins). Strong knowledge of software testing principles including functional, regression, integration, and performance testing. Experience with SQL and validating large datasets. Preferred Qualifications: Experience with cloud platforms, especially Azure (Databricks, Data Lake, Kubernetes Service). Exposure to data quality frameworks or libraries (e.g., Great Expectations). Knowledge of monitoring tools (Prometheus, Grafana, or similar) for validating system health and performance. Strong analytical and problem-solving skills with attention to detail.