Principal 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, and data they need to feel their best. Here, you will find a culture guided by diversity and inclusion, talented peers, comprehensive benefits, and career development opportunities. Come make an impact on the communities we serve as you help us advance health equity on a global scale. Join us to start  Caring. Connecting. Growing together. Location: Remote You'll enjoy the flexibility to telecommute* from anywhere within the U.S. as you take on some tough challenges.

Summary As a Principal Data Engineer on our Optum Technology team, you will drive data architecture, platform engineering, and AI/ML data pipeline integration to build scalable, resilient, and performant enterprise data systems. In this role, you will lead the technical design, development, and deployment of complex big data infrastructure and AI-powered data solutions. You will collaborate with software engineers, data scientists, product managers, and business leaders to build robust data pipelines, deploy machine learning applications into production, and convert healthcare data into actionable insights while emphasizing responsible AI practices Primary Responsibilities Design, develop, and deploy AI-powered data solutions, machine learning pipelines, and automations to address complex business challenges with an emphasis on responsible AI practices Architect, design, and implement complex, scalable data models, transformation frameworks, and multi-platform solutions across full technology stacks and enterprise domains Leverage broad expertise across big data, streaming, cloud, and relational database technologies to optimize system performance, eliminate unnecessary complexity, and build scalable scheduling and ETL/ELT pipelines Evaluate emerging technology trends, AI/ML frameworks, and platform automation to inform solution design, platform strategy, and enterprise innovation Champion adherence to enterprise security, compliance, and data governance standards, proactively identifying, mitigating, and addressing security gaps in solution design and operational workflows Drive a culture of continuous quality by leading root-cause analyses and building diagnostic instrumentation to demonstrate the business value of automated CI/CD chains Serve as a Subject Matter Expert (SME), mentor, and escalation point for complex technical challenges, fostering technical excellence across engineering teams Translate complex technical concepts into clear business terms, partnering with executive leadership, cross-functional stakeholders, and customers to guide strategic decisions and digital channel solutions 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.

HS Diploma required 8 years of data engineering experience designing, building, and deploying enterprise-scale data architecture and pipelines 5 years of experience with relational databases, NoSQL, and cloud data warehousing platforms (e.g., Snowflake, Databricks) 5 years of experience in data modeling (dimensional, relational, or data vault) and schema design 5 years of experience with programming languages such as Python, Scala, Java, or SQL 3 years of experience with big data processing frameworks (e.g., Spark, Hadoop) or real-time streaming technologies (e.g., Kafka, Flink) 3 years of experience with cloud platforms (AWS, Azure, or GCP), infrastructure as code, and automated CI/CD pipelines Preferred Qualifications Experience with MLOps frameworks, model monitoring tools, or feature store platforms (e.g., MLflow, Feast, Kubeflow) Experience in