Senior Quality Engineer

UnitedHealth Group•Raleigh, NC

22h•Remote

About The Position

Optum is a global organization that delivers care, aided by technology to help millions of people live healthier lives. The work you do with our team will directly improve health outcomes by connecting people with the care, pharmacy benefits, data and resources they need to feel their best. Here, you will find a culture guided by diversity and inclusion, talented peers, comprehensive benefits and career development opportunities. Come make an impact on the communities we serve as you help us advance health equity on a global scale. Join us to start Caring. Connecting. Growing together. You will enjoy the flexibility to telecommute from anywhere within the U.S. as you take on some tough challenges.

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

Nice To Haves

Strong communication skills with the ability to translate business reporting requirements into technical quality controls

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