Senior Data Engineer

UnitedHealth Group•Eden Prairie, MN

17h•$91,700 - $163,700•Remote

About The Position

Information is the lifeblood of the healthcare industry—everything depends on it. At Optum Insight Technology, you’ll help us work on streamlining the flow of information between payers, healthcare providers and various other stakeholders to deliver the right insights to the right places at the right times, driving better outcomes for patients, reducing friction in the health system and lowering costs. Every day our work directly impacts the world for the better, in meaningful and profound ways. We live in a time of unprecedented technical capability and possibility. Health care is at a pivotal point in this journey where even small gains can lead to major transformation. You could be a part of that – you have tremendous skill and the potential to make a lasting impact. Optum Insight Technology is uniquely positioned to bring your skills to bear on these pressing and life-changing technical challenges. The health care industry has an immediate need for your drive, innovation, passion and technical insight. Help us help the millions of people we serve each day. The Clinical Decision Support (CDS) Engineering team, a unit within the Optum Insight Technology organization, is responsible for building commercial products that help payers and providers with administrative and clinician-focused CDS solutions. We are seeking a strong Data Engineer with deep experience building scalable data pipelines and data processing solutions using Databricks, Spark, and Python. This role will lead the design and delivery of reliable, performant, and maintainable data workflows while partnering closely with product managers, architects, software engineers, and analytics stakeholders. Experience enabling downstream reporting and analytics is preferred, including familiarity with Power BI or other BI tools. You’ll 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, Data Engineering, Software Engineering, or a related field
5+ years of professional experience in software engineering or data engineering
3+ years of hands-on experience building production data pipelines and distributed data processing solutions
3+ Expertise with Databricks, Apache Spark, and Python
3+ Experience with analyzing and optimizing Spark data pipelines
3+ Experience designing and maintaining reliable, scalable, and observable data pipelines
3+ Experience with data modeling, ETL/ELT patterns, and integrating data from multiple sources

Nice To Haves

Experience with Power BI or other BI and data visualization tools
1+ years of healthcare industry experience
Experience with SQL, Delta Lake, and modern lakehouse or cloud data platform patterns
Experience with software development methodologies such as Agile and Scrum
Strong problem-solving, analytical, communication, and collaboration skills
Experience with cloud computing platforms such as Azure, AWS, or Google Cloud
Experience with DevOps practices, CI/CD pipelines, and modern engineering workflows
Knowledge of containerization technologies such as Docker and Kubernetes
Experience with data governance, lineage, and quality monitoring practices
Experience leveraging AI tooling to accelerate development and improve engineering productivity
Experience establishing best practices for data engineering quality, testing, and operational excellence

Responsibilities

Design, develop, test, deploy, and maintain scalable data pipelines and data processing workflows using Databricks, Spark, and Python
Build and optimize batch and streaming data solutions that support analytics, operational reporting, and product capabilities
Partner with product managers, architects, software engineers, and analytics stakeholders to define data requirements and deliver trusted datasets
Design and implement data models, transformation logic, and storage patterns that improve data quality, usability, and performance
Integrate data from multiple internal and external sources, ensuring reliability, observability, lineage, and secure handling of sensitive information
Optimize Spark jobs and Databricks workloads for scalability, cost efficiency, and operational resilience
Write clean, secure, well-documented, and testable code following engineering standards and best practices
Use AI-assisted coding tools to accelerate development, improve test coverage, and explore implementation options while validating output for correctness and maintainability
Perform and contribute to code reviews, helping maintain high standards for quality, security, maintainability, and data correctness
Collaborate on troubleshooting and resolving complex data integration, pipeline, and production support challenges
Contribute to CI/CD, observability, governance, and engineering practices that improve data platform reliability and developer productivity