Senior Quality Engineer

Optum is a global organization that delivers care, aided by technology to help millions of people live healthier lives. The Senior Quality Engineer will design and maintain data quality frameworks, implement automated checks, and collaborate with teams to ensure data accuracy and integrity across various platforms. This role emphasizes the use of AI-driven insights to enhance data quality practices and support governance and audit readiness.

Responsibilities:

• Design, build, and maintain scalable data quality frameworks to validate accuracy, completeness, consistency, and timeliness of enterprise data across multiple data domains
• Develop and automate data quality checks and controls using SQL, Python, and PySpark notebooks within a Databricks environment
• Implement data validation, reconciliation, and anomaly detection logic across batch and streaming data pipelines
• Embed automated data quality checks into ETL / ELT pipelines to enforce quality gates and prevent defective data from propagating downstream
• Build reusable data quality automation components, libraries, and frameworks that can be consistently adopted across data engineering teams
• Validate Power BI datasets, semantic models, and dashboards by ensuring: Source‑to‑report data reconciliation, Metric and KPI accuracy, Aggregation, filter, and refresh correctness
• Partner with reporting and analytics teams to validate Power BI measures, calculations, and business logic against certified data sources
• Monitor and report on data quality metrics and trends, including dashboard‑level data accuracy and consistency
• Perform root cause analysis for data quality and reporting issues, collaborating with upstream data producers and downstream consumers to drive permanent fixes
• Leverage AI‑assisted data quality practices, including: GenAI‑based rule generation and test case creation, Intelligent anomaly detection and pattern recognition, Automated triage and summarization of data quality issues
• Enable AIOps‑style data observability, using AI‑driven insights to proactively identify data drift, schema changes, and metric anomalies
• Support governance and audit readiness by ensuring data quality controls, validations, and dashboard certifications are documented and traceable
• Continuously improve data quality practices through automation, standardization, and AI‑driven enhancements, reducing manual validation effort
• Design, develop, and deploy AI-powered solutions to address complex business challenges with emphasis on responsible use of AI

Requirements:

• Bachelor's degree in Computer Science, Engineering, or IT related field
• 5+ years of experience in data quality engineering, quality engineering, or data engineering roles with a strong focus on automation and frameworks with a solid understanding of data quality dimensions (accuracy, completeness, consistency, timeliness, validity)
• 5+ years of experience with SQL for data profiling, reconciliation, and validation
• 5+ years of experience implementing automated data quality frameworks embedded within ETL / ELT workflows
• 3+ years of experience in Python and PySpark, including building reusable notebooks and automation frameworks
• 3+ years of proven experience working in a Databricks environment, supporting large‑scale data pipelines and analytics platforms
• 2+ years of hands‑on experience validating Power BI datasets, dashboards, KPIs, and metrics against source systems
• Strong communication skills with the ability to translate business reporting requirements into technical quality controls