Sr Director of Architecture and Strategy - Minneapolis, MN

UnitedHealth Group•Minneapolis, MN

10d•$159,300 - $273,200•Onsite

About The Position

Optum Tech is a global leader in health care innovation. Our teams develop cutting-edge solutions that help people live healthier lives and help make the health system work better for everyone. From advanced data analytics and AI to cybersecurity, we use innovative approaches to solve some of health care’s most complex challenges. Your contributions here have the potential to change lives. Ready to build the next breakthrough? Join us to start Caring. Connecting. Growing together. You will collaborate closely with engineering, security, compliance, and business leaders to translate validated architectural strategies into enterprise-wide patterns, data contracts, governance models, and technology roadmaps that enable trusted, secure, and responsible use of data and AI at scale.

Requirements

15+ years of experience in data architecture, data engineering, data analytics, information management, or closely related technical disciplines
10+ years of experience defining, evaluating, and validating enterprise-scale data architecture strategies through proofs of concept, pilots, architecture reviews, and production adoption
7+ years of experience in senior architecture, enterprise architecture, or technical leadership roles with responsibility for setting technology strategy and influencing enterprise-wide architectural direction
5+ years of hands-on experience designing and validating data models across multiple paradigms, including dimensional modeling, Data Vault, semantic layers, knowledge graphs, and vector/embedding representations
5+ years of experience with modern cloud data platforms and distributed data architectures, including Snowflake, Databricks, BigQuery, Azure Synapse, or equivalent technologies, as well as lakehouse and streaming architectures
3+ years of hands-on experience designing, prototyping, or piloting Generative AI and LLM-powered solutions, including retrieval-augmented generation (RAG), grounding strategies, evaluation frameworks, vector search, and agentic architectures
3+ years of Python and SQL experience, with the demonstrated ability to independently build prototypes, proofs of concept, and architecture validation implementations
Hands-on experience designing and evaluating enterprise metadata, data governance, data catalog, lineage, semantic, and AI-ready data platform capabilities
Proven experience establishing enterprise data architecture standards, reference architectures, data contracts, and governance models across large, distributed engineering organizations
Demonstrated ability to drive architectural transformation and technology adoption through technical leadership, executive communication, influence, and validated proof points rather than direct organizational authority
Experience working in highly regulated environments with solid security, privacy, compliance, and risk management requirements

Nice To Haves

Master’s degree or advanced training in Computer Science, Data Science, or a related discipline
Bachelor’s degree in Computer Science, Engineering, Data Science, or a related field (or equivalent professional experience)
Experience leading a data architecture function at a high-growth product company or AI-native organization
Background in machine learning engineering, applied AI research, or production ML systems
Published technical writing, conference speaking engagements, or open-source software contributions in data architecture or AI

Responsibilities

Define and evolve the target-state data architecture vision — how data is modeled, stored, governed, and served across the enterprise — with a clear path from current state to future state
Stay grounded in reality: personally build and run hands-on pilots and proof-of-concept implementations that validate architectural recommendations before asking the organization to adopt them
Design semantic and metadata layers, ontologies, and knowledge systems that let humans and AI agents reason over the same enterprise data, proven out through your own working prototypes
Evaluate emerging AI research, technologies, and architecture trends — including RAG, agentic patterns, and vector/embedding systems — to inform solution design and strategic investment decisions
Author reference architectures, design patterns, and data contracts that dozens of engineering teams can adopt and build against, grounded in what your pilots have proven works
Establish data governance, data quality, privacy, security, and AI safety frameworks that protect business assets while accelerating delivery for the teams building on top of them
Drive strategic architectural alignment across large engineering organizations through technical influence, documentation, and validated proof points rather than delivery authority
Partner closely with engineering, security, legal, compliance, and infrastructure leaders to evaluate, build, buy, or retire core data technologies and shape multi-year platform investments
Leverage enterprise-approved AI tools and automation platforms to accelerate your own pilot work and to model responsible, efficient adoption of AI for the teams that follow your patterns