Director, Data Engineering - OptumRx Technology - Remote

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. Director, Data Engineering & AI leads the strategy, architecture, and execution of enterprise data platforms that power analytics, machine learning, and generative AI capabilities. The role is accountable for building scalable AI-ready data foundations, governing trusted data assets, enabling responsible AI adoption, and delivering business value through modern data products while leading high-performing engineering teams and driving enterprise-wide transformation. You'll enjoy the flexibility to work remotely * from anywhere within the U.S. as you take on some tough challenges.

AI-Ready Data Platform Strategy Define and drive the enterprise data engineering and AI platform vision Establish scalable data architectures to support analytics, machine learning, GenAI, and agentic AI solutions Ensure data platforms are cloud-native, secure, resilient, and cost-efficient Build AI-ready data foundations including semantic layers, metadata, lineage, and knowledge graphs Data Engineering Leadership Lead multiple data engineering teams responsible for ingestion, transformation, storage, and consumption of enterprise data Establish engineering standards, best practices, and reusable frameworks Oversee development of data pipelines, data products, APIs, and real-time streaming solutions Drive modernization from legacy platforms to cloud-based architectures AI & Machine Learning Enablement Partner with Data Science and AI teams to operationalize ML and GenAI solutions Build feature stores, vector databases, embedding pipelines, and RAG architectures Enable model training, deployment, monitoring, and lifecycle management Define standards for AI observability, explainability, and responsible AI Enterprise Data Governance Establish data quality, stewardship, lineage, cataloging, and master data management processes Ensure compliance with regulatory, privacy, and security requirements Implement governance frameworks for AI training data and AI-generated outputs Drive trusted and certified data asset programs Data Product Management Champion a data-as-a-product mindset Define ownership, SLAs, and quality standards for enterprise data products Prioritize investments based on business value and AI-readiness Measure adoption, quality, and business impact of data products Innovation & Emerging Technologies Evaluate emerging technologies in GenAI, Agentic AI, Data Fabric, Semantic Layer, Knowledge Graphs, and Intelligent Automation Lead proof-of-concepts and enterprise-scale deployment strategies Drive automation of engineering operations using AI-powered tooling Promote innovation culture across engineering teams Business & Stakeholder Engagement Partner with business, product, analytics, and technology leaders to identify AI-powered opportunities Translate business objectives into scalable data and AI capabilities Communicate technology strategy and value realization to executive leadership Influence investment decisions and roadmap priorities Financial & Operational Management Own platform budgets, vendor management, and resource planning Optimize cloud costs and platform utilization Establish KPIs for platform reliability, performance, and productivity Ensure operational excellence and adherence to SLAs Talent Development Recruit, mentor, and develop high-performing data engineering and AI engineering teams Build organizational capabilities in