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.
Primary Responsibilities:- 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 cloud, analytics, MLOps, GenAI, and data governance
- Create career growth paths and succession plans
- Foster a culture of innovation, accountability, and continuous learning
- Key Success Metrics
- Data quality and reliability
- AI adoption and business value realization
- Platform availability and performance
- Data product usage and customer satisfaction
- Engineering productivity and automation
- Cloud cost optimization
- Regulatory and governance compliance
- Team engagement and retention
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.
Required Qualifications: - Undergraduate degree or equivalent experience
- Hands-on experience with AI, creating agentic solutions
- Solid knowledge of Unix and shell scripting
- Solid understanding of DWH principles, Spark/Databricks, Azure Architecture
- Solid understanding of Data Architecture and Azure Cloud
- Understanding of QA and testing automation process
- Understanding and knowledge of Agile
*All employees working remotely will be required to adhere to UnitedHealth Group's Telecommuter Policy.
Pay is based on several factors including but not limited to local labor markets, education, work experience, certifications, etc. In addition to your salary, we offer benefits such as, a comprehensive benefits package, incentive and recognition programs, equity stock purchase and 401k contribution (all benefits are subject to eligibility requirements). No matter where or when you begin a career with us, you'll find a far-reaching choice of benefits and incentives. The salary for this role will range from $134,600 - $230,800 annually based on full-time employment. We comply with all minimum wage laws as applicable.
Application Deadline: This will be posted for a minimum of 2 business days or until a sufficient candidate pool has been collected. Job posting may come down early due to volume of applicants.
