Join a global organization dedicated to improving health outcomes through technology. Optum offers a collaborative environment with talented peers, comprehensive benefits, and ample career development opportunities. You will play a key role in advancing health optimization on a global scale, impacting communities and helping people live healthier lives.

This role is part of a culture that emphasizes caring, connection, and growth. We are committed to making the health system work better for everyone by addressing barriers to good health and promoting equitable care.

Design, develop, and maintain scalable data pipelines and ETL/ELT processes for batch and streaming data. Build and optimize data ingestion frameworks using technologies like Apache Spark and Kafka. Integrate data from diverse internal and external sources, ensuring quality and consistency. Develop and manage data orchestration workflows for critical datasets. Design and optimize data warehouses, data lakes, and lakehouse architectures, working with platforms such as Snowflake and Delta Lake. Build efficient data models for enterprise reporting, analytics, and AI/ML initiatives. Define and maintain data contracts, metadata standards, and lineage frameworks.

Ensure data security and compliance with HIPAA, HITRUST, and SOC 2 requirements by implementing encryption, IAM, and RBAC controls. Design data classification, masking, and tokenization solutions for sensitive healthcare data. Own data quality, observability, and reliability for business domains, establishing monitoring and alerting. Troubleshoot production issues, optimize performance, and ensure platform stability. Support and enhance cloud-based data platforms, preferably on AWS, implementing CI/CD and Infrastructure as Code. Collaborate with Analytics, BI, and Data Science teams to enable advanced analytics and AI solutions. Create and maintain technical documentation and architecture diagrams. Mentor junior engineers and promote best practices in data engineering.

A Bachelor's or Master's degree in Computer Science, Information Technology, Data Engineering, Engineering, or a related field is required. We are looking for candidates with over 4 years of experience in Data Engineering or Data Platform Engineering. Proven experience in building and maintaining large-scale ETL/ELT pipelines and data processing systems is essential.

Proficiency in SQL and Python is mandatory, alongside hands-on experience with Apache Spark and distributed data processing frameworks. Familiarity with streaming technologies like Kafka, and cloud data platforms such as Snowflake, Delta Lake, or Apache Iceberg is expected. A strong understanding of data warehousing, lakehouse architectures, data governance, metadata management, and data lineage is crucial. Experience with CI/CD, Infrastructure as Code, and DevOps practices is required. Excellent analytical, troubleshooting, and problem-solving skills, coupled with solid communication and stakeholder management capabilities, are vital for success in this role.