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 stateStay 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 themDesign 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 prototypesEvaluate emerging AI research, technologies, and architecture trends - including RAG, agentic patterns, and vector/embedding systems - to inform solution design and strategic investment decisionsAuthor reference architectures, design patterns, and data contracts that dozens of engineering teams can adopt and build against, grounded in what your pilots have proven worksEstablish data governance, data quality, privacy, security, and AI safety frameworks that protect business assets while accelerating delivery for the teams building on top of themDrive strategic architectural alignment across large engineering organizations through technical influence, documentation, and validated proof points rather than delivery authorityPartner closely with engineering, security, legal, compliance, and infrastructure leaders to evaluate, build, buy, or retire core data technologies and shape multi-year platform investmentsLeverage 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 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: Master's degree or advanced training in Computer Science, Data Science, or a related disciplineBachelor'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 organizationBackground in machine learning engineering, applied AI research, or production ML systemsPublished technical writing, conference speaking engagements, or open-source software contributions in data architecture or AI We are committed to mitigating our impact on the environment and enabling and delivering equitable care that addresses health disparities and improves health outcomes - an enterprise priority reflected in our mission.
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