Data Engineer

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 inclusion, talented peers, comprehensive benefits and career development opportunities. Come make an impact on the communities we serve as you help us advance health optimization on a global scale. Join us to start Caring. Connecting. Growing together. As a Data Engineer at Optum, you will have the opportunity to be a member of a focused team dedicated to helping make the health care system work better for everyone. Here, you will partner with talented engineers, analysts, architects, and product owners to design and deliver solutions that meet a wide range of health consumer and business needs. As part of our team, your job is to show integrity, curiosity, and technical ownership while solving complex problems in the health care data and analytics space. You will provide technical expertise as an engineer on an agile development team in the design, development, and delivery of data platforms, APIs, cloud-based solutions, and full stack capabilities. You are expected to be an accomplished engineer able to write, understand, test, and support complex code and data pipelines. You will bring strong experience in data engineering, distributed processing, cloud platforms, and application development to design scalable and reliable solutions. You will enjoy the flexibility to telecommute* from anywhere within the U.S. as you take on some tough challenges.

Collaborate with product owners, systems analysts, software engineers, architects, and business stakeholders to deliver against an agile roadmap. Design, develop, test, and maintain data-intensive applications using Snowflake, SQL, Java, Python, Spark/Databricks, and Azure cloud technologies. Build and maintain ETL/ELT pipelines to ingest, transform, validate, and publish data across enterprise platforms. Develop and support Spark and Databricks jobs for large-scale data processing and analytics workloads. Build, schedule, monitor, and troubleshoot data workflows using Airflow or similar orchestration tools. Develop and maintain APIs and backend services to support data access, integration, and downstream application needs. Design, develop, and deploy AI-powered solutions to address complex business challenges with an emphasis on responsible use of AI. Use enterprise-approved AI tools to streamline workflows, automate tasks, and drive continuous improvement in engineering productivity and delivery quality. Evaluate emerging tech trends and AI capabilities to inform solution design and strategic innovation. Support basic UI-related needs where required, including integration with frontend components, validation of API responses, and collaboration with UI teams. Work with Kafka and Kafka Connect basics for streaming data ingestion, event-driven processing, and integration patterns. Collaborate with architects and engineers to evaluate functional and non-functional requirements and deliver secure, scalable, and high-quality solutions. Follow and improve DevOps, CI/CD, deployment, monitoring, and automation practices within the team. Build and maintain configuration management, automation tools, deployment strategies, and monitoring processes. Apply software engineering practices such as reliability engineering, deployment planning, fault-tolerant architecture, test automation, and production support. Collaborate on quality strategies that ensure data platforms and applications are correct, resilient, scalable, and secure. Support applications throughout the SDLC from design and development through production deployment and support. Participate in system analysis, design walkthroughs, code reviews, defect triage, and production issue