We are seeking a highly skilled and self-directed Senior QA Engineer/SDET to drive comprehensive quality engineering for our Enterprise Data & Analytics Platform. Reporting into the Sr. Director – Analysis, Change and Quality, this role will own and implement advanced automated testing strategies across the entire data lifecycle, ensuring data reliability, data quality, and AI/BI model accuracy. This role requires deep technical expertise in automation tools to test data pipelines in data bricks and data quality frameworks.

Key Responsibilities:

· Architect and implement robust automated testing frameworks leveraging PySpark and Databricks-native tools for data validation across Raw, Curated, and Mart layers.
· Design and implement data quality validation frameworks, including checks on accuracy, completeness, and consistency across transformation layers.
· Create advanced data quality KPIs, integrating them into automated dashboards to track quality trends across layers.
· Design metadata-driven tests, integrating with CI/CD pipelines, with coverage on all transformation layers.
· Lead development of QA user stories and acceptance criteria, precisely defining test scenarios for ingestion, transformation, and consumption layers.
· Perform complex data reconciliation testing across 10+ source systems, ensuring accuracy, completeness, and consistency from source through Mart.
· Own the end-to-end testing lifecycle (QA, Staging, Production), defining what and when to test at each stage and ensuring sign-off criteria are met.
· Partner closely with data engineers to troubleshoot pipeline failures, connectivity issues, and performance bottlenecks.
· Set standards for data lineage and auditability, ensuring every transformation step can be validated and traced.
· Plan, facilitate, and manage User Acceptance Testing (UAT) involving business users for data visualization tools such as Tableau running on Databricks.
· Prepare UAT test scenarios aligned with business use cases, guide users through testing, and gather actionable feedback.
· Drive defect triage, resolution, and retesting, ensuring readiness for production release.
· Work within a SAFe Agile framework, participating in PI planning, sprint ceremonies, and cross-team coordination. Collaborate with DevOps, Data Engineers, Data Scientists, and Product Owners to integrate QA into CI/CD pipelines.
· Provide regular updates to project and senior management on progress of QA milestones and tasks.
Required Skills & Expertise:
· Minimum of 5+ years of solid experience in Data Engineering with proven experience testing and validating data pipelines in Databricks, including medallion architecture.
· Proficient in creating testing framework for validating Data Quality.
· Proficient in Databricks notebook, PySpark, Python, SQL, and data quality testing.
· Expert with testing AI/BI models, ensuring data quality from feature engineering through model scoring.
· Experience in CI/CD pipelines (e.g., Azure DevOps) for automated test execution.
· Strong knowledge of data governance (data lineage, audit trails, compliance testing).
· Excellent problem-solving skills with the ability to work in a fast-paced environment.
· Experience with tools such as Azure Purview and Profisee MDM is preferred.