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Salary: USD 198,050 - 233,000 / annual
Ōura is seeking a Staff AI Data Transformation Architect to lead company-wide AI and data enablement as a senior individual contributor. This is a highly visible, organization-defining role that combines strategic AI roadmap development with hands-on operational execution. You will report to the VP of Data Engineering and Analytics with no direct reports; influence, credibility, and execution are your primary tools.
Key Responsibilities:
- Define and own the Data AI enablement roadmap across data, product, engineering, science, and business functions, prioritizing high-leverage transformation opportunities.
- Build repeatable adoption operating models: playbooks, shared tooling, evaluation frameworks, and success metrics that scale isolated pilots into standard practice.
- Establish shared language and evaluation frameworks for consistent AI opportunity assessment across leadership.
- Drive build-vs-buy decisions for AI tooling and influence vendor strategy and technology investment company-wide.
- Own the AI tool estate end-to-end: Databricks platform integration, data and access models, multi-agent workflows, token management, and partnership with AI Productivity teams.
- Partner with AI Governance, Security, Privacy, and Legal to define guardrails for responsible AI use in a health-sensitive environment with auditable change management.
- Manage AI spend efficiency through observability, productivity best practices, reclamation cycles, and usage insights informing renewals and build/buy decisions.
- Architect Ōura's Data AI strategy leveraging Databricks and AWS for terabyte–petabyte scale data processing with global consistency.
- Lead design of machine-readable contracts, vector-based data architectures, and RAG patterns for LLM-powered reporting and Agentic AI at production scale, including enterprise context planes (ontology, semantic layer, knowledge graph).
- Partner with data analysts and engineers to assess, prioritize, and remediate data readiness against AI roadmap use cases.
- Drive workflow redesign with functional leaders to transform how teams operate, not just their tool access.
- Build internal AI fluency through reusable training, practitioner communities, and self-service documentation enabling independent team operation.
- Scale high-value pilots into standard company-wide practice rather than isolated experiments.
- Act as 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 and earning a seat at leadership tables.
- Set operating standards for functional AI Champions and tool administrators without direct day-to-day ownership.
- Represent Data AI enablement in cross-company forums, making adoption and impact metrics visible at leadership level.
About Ōura:
Ōura's mission is to empower every person to own their inner potential. The award-winning Ōura Ring and connected app help millions of people gain deeper knowledge of their readiness, activity, and sleep quality, providing daily insights and practical steps to inspire healthy lifestyles.
Requirements:
- 8+ years of experience scaling AI data solutions, products, or technology adoption beyond pilots into sustained, organization-wide practice with measurable outcomes.
- Track record of building durable operating models, SOPs, and measurement systems that outlast individual projects.
- 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: daily use of LLM assistants and agents with sound judgment and verification.
- Clear point of view on build vs. buy decisions grounded in real experience.
- Experience with 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 regulated or health-sensitive environments.
- 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 for non-technical users and other admins.
- Strong written and verbal communication skills, able to flex between hands-on troubleshooting and executive steering committee discussions.
Nice to Have:
- 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, Databricks Model Serving).
- Knowledge of dbt for data transformation and analytics engineering.
- Cloud/AI token FinOps experience (cost allocation tagging, budget alerting, 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.
- Familiarity with scripting or configuration automation (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 use cases in production.
- 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.
