Staff AI Data Transformation Architect

Job description

Our mission at Oura is to empower every person to own their inner potential. Our award-winning products help our global community gain a deeper knowledge of their readiness, activity, and sleep quality by using their Oura Ring and its connected app. We've helped millions of people understand and improve their health by providing daily insights and practical steps to inspire healthy lifestyles. Empowering the world starts with living our values and empowering our team. As a quickly growing company focused on helping people live healthier and happier lives, we ensure that our team members have what they need to do their best work — both in and out of the office. At Ōura, we're on a mission to make health a daily practice for everyone. With our award-winning Ōura Ring and app, we've empowered over a million people to improve their sleep, understand their bodies, and transform their health. Now, we're looking for a distinguished AI leader to turn scattered experimentation into safe, scalable, everyday practice across the entire company.We are seeking a Staff AI Data Transformation Architect to lead as the senior-most individual contributor driving Data AI enablement across our Data team at Ōura, reporting to VP, Data Engineering and Analytics. This is a highly visible, org-defining role that combines two things usually split across separate jobs: the strategic work of building a company-wide AI roadmap and operating model, and the hands-on work of running the operational backbone — access, governance, licensing — that makes that practice safe and scalable.There are no direct reports in this role: strategy, influence, credibility, and hands-on execution are your primary tools. This role is distinguished from Senior by its company-wide scope of influence, the level of ambiguity it resolves, and the expectation that you define the problem as much as solve it. What You Will Do: Define and own the Data AI enablement roadmap across data, product, engineering, science and business functions, prioritizing the transformation opportunities with the highest leverage. Build the operating model for repeatable adoption: playbooks, shared tooling, evaluation frameworks, and success metrics that turn isolated pilots into standard practice. Establish a shared language and evaluation framework that leaders across the company use to assess AI opportunities consistently. Drive build-vs-buy decisions for AI tooling, influencing vendor strategy and technology investment at a company-wide level. Own the AI tool estate end-to-end: integration of data and access models within the Databricks platform, multi-agent workflows, token management, and partner with AI Productivity to power the AI ecosystem with a trusted data layer. Hold the governance line: partner with AI Governance, Security, Privacy, and Legal to define guardrails for responsible AI use in a health-sensitive environment, with auditable change management. Keep AI spend efficient: own observability, productivity best practices and reclamation cycles, and the usage insights that inform renewal, expansion, and build/buy decisions. Architect Ōura's Data AI strategy leveraging Databricks and AWS to process terabyte–petabyte scale data with global consistency. Lead the design of machine-readable contracts, vector-based data architectures and Retrieval Augmented Generation (RAG) patterns to enable LLM-powered reporting and Agentic AI at production scale, including an enterprise context plane: ontology, semantic layer, knowledge graph, and context layer. Partner closely with data analysts and engineers to assess, prioritize, and remediate data readiness (quality, structure, access, documentation) against the AI roadmap's use cases. Partner with leadership to drive workflow redesign, working directly with functional leaders to change how their teams operate — not just what tools they have access to. Build internal AI fluency through reusable training, a strong practitioner community, and self-service documentation that let teams and other admins operate independently. Scale a small number of high-value pilots into standard, company-wide practice rather than leaving them as isolated experiments. Act as the primary bridge between executive intent, functional leaders, and enabling functions (Security, Legal, IT, Software). Drive alignment across AI productivity, engineering and business units, establishing trust early and