This role suits an experienced artificial intelligence strategist and hands-on technologist who thrives in high-ambiguity environments and aims to scale AI transformation across a growing organization. Based in a hybrid setup out of San Francisco, California, the Staff AI Data Transformation Architect leads enterprise-wide AI enablement at Ōura.
Role Overview
Ōura is hiring a Staff AI Data Transformation Architect to operate as the senior-most individual contributor driving Data AI enablement within the Data team. Reporting directly to the Vice President of Data Engineering and Analytics, this position bridges company-wide AI strategy with hands-on operational management of access, governance, and tooling. The role involves no direct reports, relying instead on strategy, influence, credibility, and technical execution to transition isolated experiments into safe, scalable daily practices across data, product, engineering, science, and business units.
Key Responsibilities
Define and own the Data AI enablement roadmap across data, product, engineering, science, and business functions.
Build operating models for repeatable adoption, including playbooks, shared tooling, evaluation frameworks, and success metrics.
Establish shared language and evaluation frameworks for leaders to assess AI opportunities consistently.
Drive build-versus-buy decisions for AI tooling, influencing vendor strategy and technology investments.
Administer the AI tool estate end-to-end, including Databricks data integrations, multi-agent workflows, token management, and trusted data layers.
Partner with AI Governance, Security, Privacy, and Legal teams to enforce guardrails and auditable change management in a health-sensitive environment.
Manage AI spend observability, productivity best practices, reclamation cycles, and usage insights.
Architect enterprise Data AI strategy leveraging Databricks and AWS for terabyte-to-petabyte scale data processing.
Design machine-readable contracts, vector-based data architectures, and Retrieval Augmented Generation (RAG) patterns for production-scale LLM reporting and Agentic AI.
Collaborate with analysts and engineers to remediate data readiness against AI roadmap use cases.
Partner with functional leaders to redesign workflows and drive internal AI fluency through training and communities.
Requirements
8+ years of experience scaling AI data solutions, products, or technology adoption beyond pilots into sustained organizational practice.
Track record of building durable operating models, standard operating procedures, and measurement systems.
History of influencing and aligning senior leadership on long-horizon technology investments.
Deep technical fluency in Large Language Models and implementation patterns.
Direct, hands-on experience administering at least one enterprise AI productivity tool with user and group management.
Demonstrated daily use of LLM assistants and agents with sound judgment and verification.
Clear perspective on build-versus-buy strategies grounded in practical experience.
Enterprise data platform experience, including lakehouses utilizing AI and LLMs.
Practical experience with role-based access models and permissions across multi-domain or multi-region zones.
Comfort partnering with Security, Privacy, and Legal functions in regulated or health-sensitive environments.
Proven ability to design and maintain self-service documentation and standard operating procedures.
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 machine learning workflows like MLflow, Feature Store, or Databricks Model Serving.
Knowledge of dbt for data transformation and analytics engineering workflows.
Cloud and AI token FinOps experience encompassing cost allocation tagging and budget alerting.
Exposure to Databricks Lakehouse Federation or cross-platform query federation patterns.
Prior experience as an AI Champion or in a similar transformation role.
Familiarity with scripting or configuration automation using Python, shell, or configuration-as-code.
Experience in health tech, consumer health, wearables, or regulated industries.
Deep expertise in designing data architectures for LLM, RAG, and vector-based production use cases.
Experience with MLOps frameworks, VertexAI, and production lifecycle management.
Ability to define data requirements for Agentic AI and interactive self-serve analytical systems.
Skills and Tools
Databricks
AWS
Large Language Models (LLMs)
Retrieval Augmented Generation (RAG)
Python
dbt
MLflow
VertexAI
Cursor
Claude
ChatGPT Enterprise
Glean
Gemini
Salary and Benefits
Competitive salary ranging from $198,050 to $233,000.
An Oura Ring for employees to support personal health and well-being.
About the Company
Ōura produces the award-winning Oura Ring and connected app, helping a global community gain deeper insights into readiness, activity, and sleep quality. The company focuses on empowering individuals to own their inner potential and make health a daily practice.
How to Apply
Apply directly on the official company careers page.
