Maryland · Noida · full time · mid level
Job Summary We are seeking a QA Analyst to support quality assurance and User Acceptance Testing (UAT) activities for an Enterprise Data and AI Platform delivering AI and BI solutions built on Databricks. The platform enables complex data products, including AI and ML use cases, business intelligence and reporting, and data integrations with internal and external systems. This role is hands-on and execution-focused in data, analytics, or platform testing. Reporting to the Senior Director – Analysis, Change and Quality, the QA Analyst will work within an Agile environment, collaborating closely with Data Engineers, Developers, Product Owners, and business stakeholders to ensure high-quality data solutions. Key Responsibilities The QA Analyst will execute test cases and test scenarios for data pipelines on Databricks, BI dashboards, reports, and self-service analytics, as well as APIs and data integrations that support internal and external systems. The role also involves testing data exchanges and downstream consumption use cases. The candidate will perform functional, integration, regression, and smoke testing under the guidance of the QA Lead and senior team members. They will validate data accuracy, completeness, and consistency using SQL and basic scripting where applicable, and support data quality checks and reconciliation activities across source and target systems. The role includes logging, tracking, and retesting defects while working closely with development teams until resolution. The QA Analyst will also support User Acceptance Testing by developing and executing UAT test cases and scenarios aligned to business requirements, assisting business users during UAT cycles, capturing feedback, reproducing issues, documenting defects clearly, and tracking UAT progress rigorously. They will participate in defect triage and retesting to ensure readiness for production releases. The role involves assisting with data validation activities using tools and frameworks such as SQL-based checks and data quality or observability tools like Monte Carlo and Great Expectations. The candidate will gain exposure to data governance tools such as Azure Purview and Profisee MDM, supporting validation and quality use cases as assigned. The QA Analyst will work within an Agile framework, participating in daily stand-ups, sprint planning and reviews, and retrospectives. They will collaborate with Data Engineers, QA Leads, Product Owners, and DevOps teams to align testing activities with sprint goals. The role includes supporting test data setup, environment validation, and release readiness activities, as well as executing and maintaining test cases in test management tools such as Azure DevOps. The candidate will follow established QA processes, templates, and standards, and contribute to continuous improvement by identifying gaps, risks, and opportunities to enhance test coverage. Required Skills and Experience The candidate should hold a bachelor’s degree in computer science, with a focus on data analytics and visualization. They should have at least two years of direct experience in data and analytics projects, particularly in interpreting complex data for stakeholder decision-making. Intermediate SQL skills for data validation and reconciliation are required, along with experience executing functional and regression testing. The role requires experience in maintaining automated reporting using Python and designing Power BI reports for data-driven applications. The candidate should be familiar with defect tracking and test management tools such as Azure DevOps, JIRA, TestRail, or similar platforms, and have a basic understanding of APIs and integration testing concepts. Experience with automation frameworks using technologies such as C++, JavaScript, and React is expected. The candidate should also have experience working in an Agile, Scrum, or SAFe Agile environment, along with familiarity with Git, Workbench, MDM tools, and testing REST APIs. Strong written and verbal communication skills are important, along with a willingness to learn and develop solutions that expedite testing. Preferred Qualifications Exposure to Tableau, Databricks, cloud data platforms, or modern analytics environments is preferred. Awareness of data quality, data governance, or MDM concepts is beneficial. An ISTQB or other QA certification is considered a plus.