W2 Role-QA Analyst With Datapipelines,Databricks

Role: QA Analyst Enterprise Data & AI Platform Location: Adelphi, MD (open to 100% remote) Duration: 4 months; possible extensions Notes: SQL is a MUST Previous experience testing Data Pipelines and Databricks is a MUST Experience with Profisee MDM is preferred but not mandatory. Experience with Azure Purview is preferred but not mandatory Job Description: Job Summary We are seeking a QA Analyst to support quality assurance and User Acceptance Testing (UAT) activities for an Enterprise Data & AI Platform delivering AI/BI solutions built on Databricks. The platform enables complex data products, including AI/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 Sr. Director Analysis, Change & 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

Test Execution & QA Support

• Execute test cases and test scenarios for:

o Data pipelines on Databricks o BI dashboards, reports, and self-service analytics o APIs and data integrations supporting internal and external systems o Data exchanges and downstream consumption use cases

• Perform functional, integration, regression, and smoke testing under guidance from QA Lead and senior team members.
• Validate data accuracy, completeness, and consistency using SQL and basic scripting where applicable.
• Support data quality checks and reconciliation activities across source and target systems.
• Log, track, and retest defects, working closely with development teams through resolution.
• Support User Acceptance Testing (UAT) by developing and executing UAT test cases and scenarios aligned to business requirements, assisting business users during UAT cycles, capturing feedback, reproducing issues, and documenting defects clearly, and tracking UAT progress rigorously.
• Participate in defect triage and retesting to ensure readiness for production releases.

Data Quality & Tool Exposure

• Assist with data validation activities using tools and frameworks such as:

o SQL-based checks o Data quality or observability tools (e.g., Monte Carlo, Great Expectations exposure)

• Gain exposure to data governance tools such as Azure Purview and Profisee MDM, supporting validation and quality use cases as assigned.

Tools, process and collaboration

• Work within an Agile framework, participating in:

o Daily stand-ups o Sprint planning and reviews o Retrospectives

• Collaborate with Data Engineers, QA Leads, Product Owners, and DevOps teams to align testing activities with sprint goals.
• Support test data setup, environment validation, and release readiness activities.
• Execute and maintain test cases in test management tools such as Azure DevOps.
• Follow established QA processes, templates, and standards.
• Contribute to continuous improvement by identifying gaps, risks, and opportunities to improve test coverage.

Required Skills & Experience

• Bachelor's degree in computer science, with focus on Data analytics and visualization.
• At least 2 years of direct experience in data and analytics projects with a focus on interpreting complex data for stakeholder decision making.
• Intermediate SQL skills for data validation and reconciliation.
• Experience executing functional and regression testing.
• Experience in maintaining automated reporting using Python.
• Designing PowerBI reports for data-driven applications.
• Familiarity with defect tracking and test management tools (Azure DevOps, JIRA, TestRail, or similar).
• Basic understanding of APIs and integration testing concepts.
• Experience in automation frameworks such as C++, JavaScript, and React.
• Experience working in an Agile/Scrum or SAFe Agile environment.
• Experience with Git, Workbench, MDM tools, and testing Rest APIs.
• Strong written and verbal communication skills are important.
• Willingness to learn and develop solutions to expedite testing.

Preferred Qualifications

• Exposure to Tableau, Databricks, cloud data platforms, or modern analytics environments.
• Awareness of data quality, data governance, or MDM concepts.

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