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.
#OptumInsightTechThe 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.
Primary 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
You'll be rewarded and recognized for your performance in an environment that will challenge you and give you clear direction on what it takes to succeed in your role as well as provide development for other roles you may be interested in.
Required Qualifications:- 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
Preferred Qualifications:- 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
*All employees working remotely will be required to adhere to UnitedHealth Group's Telecommuter Policy.
Pay is based on several factors including but not limited to local labor markets, education, work experience, certifications, etc. In addition to your salary, we offer benefits such as, a comprehensive benefits package, incentive and recognition programs, equity stock purchase and 401k contribution (all benefits are subject to eligibility requirements). No matter where or when you begin a career with us, you'll find a far-reaching choice of benefits and incentives. The salary for this role will range from $91,700 to $163,700 annually based on full-time employment. We comply with all minimum wage laws as applicable.
Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.
Application Deadline: This will be posted for a minimum of 2 business days or until a sufficient candidate pool has been collected. Job posting may come down early due to volume of applicants.
