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.
Primary Responsibilities:
•Data Engineering & Pipeline Development - Design, develop, and maintain scalable data pipelines and ETL/ELT processes supporting batch and streaming workloads
•Build and optimize data ingestion frameworks using technologies such as Apache Spark, Kafka, and cloud-native services
•Integrate data from multiple internal and external source systems while ensuring data quality, consistency, and governance
•Develop and maintain data orchestration workflows to ensure reliable delivery of business-critical datasets
•Data Architecture & Modeling - Design and optimize data warehouses, data lakes, and lakehouse architectures
•Build scalable and efficient data models supporting enterprise reporting, analytics, and AI/ML use cases
•Work with platforms such as Snowflake, Delta Lake, Apache Iceberg, and cloud-based data ecosystems
•Define and maintain data contracts, metadata standards, and lineage frameworks
•Data Security, Governance & Compliance - Design secure and compliant data pipelines incorporating encryption, IAM, RBAC, audit logging, and access controls
•Implement data classification, masking, tokenization, and lineage solutions for sensitive healthcare data including PII and PHI
•Support adherence to HIPAA, HITRUST, SOC 2, and enterprise security requirements
•Contribute to audit readiness by maintaining controls documentation, access reviews, lineage records, and compliance evidence
•Data Reliability & Observability - Own data quality, observability, monitoring, and reliability for assigned business domains
•Establish and monitor SLAs, data validation checks, and alerting mechanisms
•Troubleshoot production issues, optimize performance, and ensure platform stability
•Drive best practices around data testing, monitoring, and operational excellence
•Cloud & Platform Engineering - Support and enhance modern cloud-based data platforms, with a strong preference for AWS technologies
•Implement CI/CD pipelines, Infrastructure as Code (IaC), and automated deployment practices
•Optimize infrastructure utilization through right-sizing, auto-scaling, partitioning, clustering, and cost-management strategies
•Contribute to platform automation and engineering process improvements
•Analytics & AI Enablement - Collaborate with Analytics, BI, and Data Science teams to enable reporting, advanced analytics, machine learning, and AI solutions
•Develop feature stores and ML-ready datasets for predictive modeling initiatives
•Ensure data accessibility, consistency, and reliability for downstream analytical applications
•Documentation & Leadership - Create and maintain technical documentation, architecture diagrams, schemas, and operational procedures
•Document end-to-end data lineage, governance frameworks, and engineering standards
•Mentor Associate and Junior Data Engineers, promoting engineering best practices and knowledge sharing
•Stay current with emerging technologies, cloud innovations, security standards, and modern data engineering methodologies
•Comply with all applicable Company policies, procedures, and business directives, changes including those relating to work location, team assignments, work schedules, and flexible work arrangements
Required Qualifications:
•Bachelor's or Master's degree in Computer Science, Information Technology, Data Engineering, Engineering, or a related field
•4+ years of experience in Data Engineering, Data Platform Engineering, or related areas
•Solid experience building and maintaining ETL/ELT pipelines and large-scale data processing systems
•Hands-on experience with Apache Spark and distributed data processing frameworks
•Experience working with Kafka or other streaming technologies
•Experience with Snowflake, Delta Lake, Apache Iceberg, or similar cloud data platforms
•Solid knowledge of data warehousing and lakehouse architectures
•Solid understanding of data governance, metadata management, and data lineage concepts
•Familiarity with CI/CD, Infrastructure as Code, and DevOps practices
•Proficiency in SQL and Python
•Proven excellent analytical, troubleshooting, and problem-solving skills
•Proven solid communication and stakeholder management capabilities
