Principal Data Engineer

Optum is a global healthcare organization that uses technology to improve health outcomes and connect people with care, pharmacy benefits, and data. The Principal Data Engineer will lead the architecture, development, and deployment of scalable enterprise data systems, AI-powered data solutions, and machine learning pipelines while collaborating with technical and business stakeholders.

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

Requirements:

• 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
• Experience with MLOps frameworks, model monitoring tools, or feature store platforms (e.g., MLflow, Feast, Kubeflow)
• Experience in the healthcare industry working with health data standards (e.g., FHIR, HL7, EDI) and healthcare security/compliance regulations
• Deep expertise with cloud-native data services, enterprise data governance, and metadata management tools
• Proven track record of architecting distributed systems handling large-scale unstructured and structured data sets
• Experience implementing automated testing, data quality frameworks, and responsible AI validation within deployment pipelines
• Demonstrated experience leading technical architecture design, governing data security standards, and mentoring software/data engineering teams
• 3 years of experience building, integrating, and deploying machine learning pipelines, AI models, or AI-powered data applications into production environments