Data Engineering Consultant - Python, SQL, ETL, AWS/Azure

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.

Data & Platform Engineering: Design, build, and maintain scalable, fault-tolerant data pipelines and data platforms in a cloud-native environment Own end-to-end development of data ingestion, processing, and transformation systems used by downstream services and applications Implement efficient data models and storage patterns in modern data warehouses and operational databases Production Systems & Applications: Develop and maintain production-grade backend services and applications that power data workflows and internal platforms Build and expose data services and APIs to enable secure, reliable access to data across engineering systems Ensure high availability, low latency, and resilience of data-driven applications running in production Workflow Orchestration & Reliability: Design and operate workflow orchestration frameworks to schedule, monitor, and manage complex data workloads Implement robust error handling, retries, monitoring, and alerting for all production pipelines and services Troubleshoot and resolve performance, scalability, and reliability issues in live systems Cloud & DevOps Engineering: Deploy and operate data systems using cloud infrastructure, containers, and automated CI/CD pipelines Apply Infrastructure-as-Code and automation best practices to ensure repeatable, reliable deployments Optimize cloud resources for performance, cost, and operational efficiency Engineering Excellence & Collaboration: Participate in system design reviews and contribute to architecture decisions for data and platform components Write clean, maintainable, and well-tested code following engineering best practices Mentor junior engineers and raise the overall engineering maturity of the team AI & Agentic Engineering: Develop and deploy AI agents, reusable skills, and LLM-powered workflows to automate data acquisition and integration Build and maintain agentic AI solutions leveraging enterprise data sources, APIs, and tools Implement RAG and AI-driven data enrichment capabilities to improve data quality and accessibility Create reusable AI components and orchestration patterns to increase automation and development efficiency Monitor and optimize AI agent performance, reliability, and governance Collaborate with cross-functional teams to identify and deliver AI-driven automation opportunities Comply with the terms and conditions of the employment contract, company policies and procedures, and any and all directives (such as, but not limited to, transfer and/or re-assignment to different work locations, change in teams and/or work shifts, policies in regard to flexibility of work benefits and/or work environment, alternative work arrangements, and other decisions that may arise due to the changing business environment). The Company may adopt, vary or rescind these policies and directives in its absolute discretion and without any limitation (implied or otherwise) on its ability to do so Required Qualifications: Graduate degree and 6+ years of Data Engineering experience Hands-on experience building and maintaining production data pipelines Experience working with large datasets in a cloud environment Experience with healthcare data Solid understanding of data modeling and performance optimization Technical Skills: Programming: Shell, Python, SQL Data Engineering: ETL/ELT concepts, data pipeline design Databases: Relational