Senior QA Engineer (Data & Analytics)

This is a hybrid role, onsite in College Park, MD 1-2 days a week. C2C candidates will not be considered.

We are seeking a highly skilled and self-directed Senior QA Engineer to lead comprehensive quality engineering initiatives across our Enterprise Data & Analytics Platform. Reporting to the Sr. Director – Analysis, Change and Quality, this role will define and implement advanced automated testing strategies spanning the full data lifecycle to ensure data reliability, platform scalability, and AI/BI model accuracy.

This position requires deep expertise in automated testing of data pipelines within Databricks environments, along with hands-on experience in data governance and master data management tools such as Azure Purview and Profisee MDM.

Key Responsibilities

Data Platform Quality Engineering

• Architect and implement scalable automated testing frameworks using PySpark and Databricks-native tools to validate data across Raw, Curated, and Mart layers (medallion architecture).
• Design metadata-driven test frameworks with full CI/CD integration to ensure coverage across ingestion, transformation, and consumption layers.
• Develop and execute complex data reconciliation testing across 10+ source systems, ensuring completeness, accuracy, and consistency from source through data marts.
• Establish standards for data lineage, traceability, and auditability, ensuring each transformation step can be validated and reproduced.
• Define and implement advanced data quality KPIs, integrating automated dashboards to track quality trends and proactively identify risk.

AI/ML & BI Model Testing

• Design and execute AI/ML model validation strategies, including input/output validation, statistical accuracy checks, bias detection, and edge-case scenario testing.
• Develop reusable QA templates and best practices for AI/BI model retraining and release cycles.
• Ensure data integrity across feature engineering, model training, scoring, and reporting layers.

Testing Lifecycle & UAT Leadership

• Own the end-to-end testing lifecycle across QA, Staging, and Production environments, clearly defining entry/exit criteria and release sign-offs.
• Lead development of QA user stories and acceptance criteria aligned to business requirements.
• Plan, facilitate, and manage User Acceptance Testing (UAT) for AI/BI applications, dashboards, data feeds, and integrations.
• Prepare business-aligned UAT scenarios, guide stakeholders through testing, and capture actionable feedback.
• Drive defect triage, resolution coordination, retesting, and production readiness validation.

Agile & DevOps Integration

• Operate within a SAFe Agile framework, participating in PI planning, sprint ceremonies, and cross-team collaboration.
• Partner with DevOps, Data Engineers, Data Scientists, and Product Owners to embed automated testing within CI/CD pipelines (e.g., Azure DevOps).
• Manage environment readiness, test data provisioning, and release activities aligned to Agile delivery cadence.
• Provide regular status reporting to project and senior leadership on QA milestones, risks, and outcomes.

Required Skills & Expertise

• 5+ years of QA experience in enterprise data platforms, including hands-on work with Databricks and Profisee MDM.
• Deep understanding of data lakehouse architecture and medallion design patterns (Raw, Curated, Mart layers).
• Advanced expertise in data testing methodologies and automation frameworks.
• Strong proficiency in PySpark and SQL for data validation and reconciliation testing.
• Demonstrated experience validating AI/BI models from feature engineering through scoring and reporting.
• Experience integrating automated testing within CI/CD pipelines (e.g., Azure DevOps).
• Strong knowledge of data governance principles, including lineage validation, audit trails, and compliance testing.
• Excellent analytical and problem-solving skills with the ability to operate effectively in fast-paced, enterprise environments.