• 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 earning a seat at the table through credibility.
• Set the operating standard that functional AI Champions and any dedicated AI-tool administrators run to, without owning their day-to-day work directly.
• Represent Data AI enablement in cross-company forums, making adoption and impact metrics visible and discussed at the leadership level.

This is a hybrid role out of our San Francisco office.

• 8+ years of experience scaling AI data solutions, products or technology adoption beyond pilots into sustained, organization-wide practice, with measurable outcomes.
• A track record of building durable operating models, SOPs, and measurement systems that outlast any one project.
• History of influencing and aligning senior leadership on long-horizon technology investments.
• Deep technical fluency in LLMs and implementation patterns, with sound judgment for separating real value from hype.
• Direct, hands-on experience administering at least one enterprise AI productivity tool (e.g., Cursor, Claude, ChatGPT Enterprise, Glean, Gemini, coding assistants, LLM API gateways), including user/group management and configuration troubleshooting.
• Demonstrated AI fluency: uses LLM assistants and agents in daily work with sound judgment and verification.
• Clear point of view on when to build vs. buy, grounded in real experience rather than generic opinion.
• Experience in enterprise data platforms including lakehouse using AI/LLMs.
• Practical experience with role-based access models and permissions in multi-domain/region zones.
• Comfort partnering with Security, Privacy, and Legal to define and operationalize guardrails for responsible AI use in a regulated or health-sensitive environment.
• Ability to influence senior stakeholders and drive alignment across R&D and business units without formal authority.
• Proven ability to design, document, and maintain SOPs, playbooks, and training that non-technical users and other admins can follow independently.
• Strong written and verbal communication skills, able to flex between a hands-on troubleshooting conversation and an executive steering committee.

• Databricks Certified Administrator or Databricks Certified Data Engineer certification.
• Familiarity with data mesh principles and domain-oriented data ownership models.
• Experience supporting ML workflows — MLflow, Feature Store, or Databricks Model Serving.
• Knowledge of dbt for data transformation and analytics engineering workflows.
• Cloud/AI token FinOps experience — cost allocation tagging, budget alerting, and multi-tier storage optimization.
• Exposure to Databricks Lakehouse Federation or cross-platform query federation patterns.
• Prior experience as an AI Champion or similar transformation role in another function or company.
• Familiarity with scripting or configuration automation (e.g., Python, shell, configuration-as-code) to reduce manual admin work.
• Experience in health tech, consumer health, wearables, or another regulated, high-trust industry.
• Deep expertise designing data architectures for LLM, RAG, and vector-based use cases in production environments.
• Experience with MLOps frameworks, VertexAI, MLflow, and production AI/ML lifecycle management.
• Ability to define data requirements for Agentic AI and interactive self-serve analytical systems.

At Ōura, we care about you and your well-being. Everyone here at Ōura has a ring of their own and we are continually looking to improve employee health.

• Competitive salary and equity packages
• Health, dental, vision insurance, and mental health resources
• An Ōura Ring of your own plus employee discounts for friends & family
• 20 days of paid time off plus 13 paid holidays plus 8 days of flexible wellness time off
• Paid sick leave and parental leave

Ōura takes a market-based approach to pay, which may vary depending on your location. US locations are categorized into tiers based on a cost of labor index for that geographic area. While most offers will be closer to the starting range, successful candidates' pay will be determined based on job-related skills, experience, qualifications, work location, internal peer equity, and market conditions. These ranges may be modified in the future.

Region 1 $198,050 - $233,000

Region 2 $180,200 - $212,000

Region 3 $169,150 - $199,000

A recruiter can determine your Region based on your US location.

We are not considering candidates residing in the following states: Alaska (AK), Delaware (DE), Iowa (IA), Mississippi (MS), Nebraska (NE), South Dakota (SD), West Virginia (WV), and Wisconsin (WI).

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• Official offers are sent through Docusign after a verbal offer, not via text or email.

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