Staff AI Data Transformation Architect

ŌURA

. Define and own the Data AI enablement roadmap across data, product, engineering, science and business functions.

Posted 10/5/2026full-timeSan Francisco • California • United StatesLead💰 $169,150 - $233,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in scaling AI data solutions and implementing durable operating models, with a strong focus on AI tool administration and responsible AI governance. Proven ability to influence senior leadership and drive alignment across technical and business functions.

Highest-signal resume keywords

AI Data Solutions ScalingLLM Implementation PatternsEnterprise AI Productivity Tool AdministrationData Architecture Design for LLMAI Governance and Compliance

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

AI Tool AdministrationData Architecture DesignLLM FluencySOP DevelopmentData Readiness AssessmentWorkflow RedesignMulti-Agent WorkflowsRole-Based Access ModelsCloud/AI Token FinOpsMLOps Frameworks

Soft Skills

Influencing Senior StakeholdersStrong Written CommunicationVerbal Communication FlexibilityCollaboration Across FunctionsTraining and Mentoring

Tools & Technologies

DatabricksAWSCursorClaudeChatGPT EnterpriseGleanGeminiMLflowFeature StoreDatabricks Model Serving

Certifications & Qualifications

Databricks Certified AdministratorDatabricks Certified Data Engineer

Industry Keywords

Health TechConsumer HealthRegulated EnvironmentData Mesh PrinciplesDomain-Oriented Data Ownership

Tech Stack

Tools & technologies

AWSCloudPython

About the role

Key responsibilities & impact

Define and own the Data AI enablement roadmap across data, product, engineering, science and business functions.
Build the operating model for repeatable AI adoption, including playbooks, shared tooling, evaluation frameworks, and success metrics.
Establish shared language and evaluation frameworks for assessing AI opportunities.
Drive build-vs-buy decisions for AI tooling, vendor strategy, and company-wide technology investment.
Own the AI tool estate, including Databricks data/access integration, multi-agent workflows, token management, and a trusted data layer.
Partner with AI Governance, Security, Privacy, and Legal to define responsible-AI guardrails and auditable change management.
Own AI spend efficiency through observability, productivity best practices, reclamation cycles, and usage insights.
Architect Data AI strategy using Databricks and AWS at terabyte–petabyte scale.
Lead machine-readable contracts, vector-based data architectures, RAG patterns, and enterprise context-plane components for production-scale LLM reporting and Agentic AI.
Assess and remediate data readiness with analysts and engineers.
Drive workflow redesign with functional leaders.
Build internal AI fluency through training, practitioner communities, and self-service documentation.
Scale high-value pilots into company-wide practice.
Bridge executive intent, functional leaders, and enabling functions.
Drive alignment across AI productivity, engineering, and business units.
Set operating standards for functional AI Champions and AI-tool administrators.
Represent Data AI enablement in cross-company forums and report adoption and impact metrics to leadership.

Requirements

What you’ll need

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.
Nice to have: Databricks Certified Administrator or Databricks Certified Data Engineer certification.
Nice to have: Familiarity with data mesh principles and domain-oriented data ownership models.
Nice to have: Experience supporting ML workflows — MLflow, Feature Store, or Databricks Model Serving.
Nice to have: Knowledge of dbt for data transformation and analytics engineering workflows.
Nice to have: Cloud/AI token FinOps experience — cost allocation tagging, budget alerting, and multi-tier storage optimization.
Nice to have: Exposure to Databricks Lakehouse Federation or cross-platform query federation patterns.
Nice to have: Prior experience as an AI Champion or similar transformation role in another function or company.
Nice to have: Familiarity with scripting or configuration automation (e.g., Python, shell, configuration-as-code) to reduce manual admin work.
Nice to have: Experience in health tech, consumer health, wearables, or another regulated, high-trust industry.
Nice to have: Deep expertise designing data architectures for LLM, RAG, and vector-based use cases in production environments.
Nice to have: Experience with MLOps frameworks, VertexAI, MLflow, and production AI/ML lifecycle management.
Nice to have: Ability to define data requirements for Agentic AI and interactive self-serve analytical systems.
US work location required; candidates residing in Alaska, Delaware, Iowa, Mississippi, Nebraska, South Dakota, West Virginia, or Wisconsin are not considered.

Benefits

Comp & perks

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