Director, Data Engineering - OptumRx Technology - Remote

UnitedHealth Group•Schaumburg, IL

2d•$134,600 - $230,800•Remote

About The Position

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.

Requirements

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

Responsibilities

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
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
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
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
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
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
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
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
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