O
$169,150 – $233,000
Listed on Oura’s own careers site. You apply with them directly — we never stand between you and the employer.
What this role is
This role leads the strategic and operational transformation of AI and data practices across Ōura, building company-wide adoption frameworks, governance guardrails, and technical infrastructure without direct reports. It suits someone with deep AI platform experience who thrives as a technical leader, influencing senior stakeholders to move beyond isolated pilots into sustained, measurable organizational practice.
Our summary, not Oura’s wording. The full posting is on their site.
Skills this role names
- Amazon Web Services (AWS)
- Databricks
- Dialectical Behavior Therapy (DBT)
- MLflow
- Python
- Retrieval-Augmented Generation
- Role-Based Access Control (RBAC)
- Vector Databases
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What they ask for
Required
- 8+ years scaling AI data solutions beyond pilots to organization-wide practice with measurable outcomes
- Track record building operating models, SOPs, and measurement systems
- History influencing senior leadership on long-horizon technology investments
- Deep technical fluency in LLMs and implementation patterns
- Hands-on experience administering enterprise AI productivity tools (e.g., Cursor, Claude, ChatGPT Enterprise, Glean, Gemini)
- Daily use of LLM assistants and agents with sound judgment and verification
- Sound judgment on build vs. buy decisions grounded in real experience
- Experience with enterprise data platforms including lakehouse with AI/LLMs
- Practical experience with role-based access models and permissions in multi-domain/region environments
- Ability to partner with Security, Privacy, and Legal on AI guardrails in regulated or health-sensitive settings
- Ability to influence senior stakeholders and drive cross-unit alignment without formal authority
- Proven ability to design, document, and maintain SOPs and training for non-technical users
- Strong written and verbal communication across technical troubleshooting and executive contexts
Nice to have
- Databricks Certified Administrator or Certified Data Engineer certification
- Familiarity with data mesh principles and domain-oriented data ownership
- Experience supporting ML workflows (MLflow, Feature Store, Databricks Model Serving)
- Knowledge of dbt for data transformation and analytics engineering
- Cloud/AI token FinOps experience (cost allocation, budget alerting, storage optimization)
- Exposure to Databricks Lakehouse Federation or cross-platform query federation
- Prior AI Champion or similar transformation role experience
- Scripting or configuration automation skills (Python, shell, configuration-as-code)
- Experience in health tech, consumer health, wearables, or regulated high-trust industries
- Deep expertise designing data architectures for LLM, RAG, and vector-based production use cases
- Experience with MLOps frameworks, VertexAI, and production AI/ML lifecycle management
- Ability to define data requirements for Agentic AI and self-serve analytical systems