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ŌURA
ŌURA
201 – 500 Angajați
Asistență medicalăConsultanțăIndustria prelucrătoare
ŌURA dezvoltă inele inteligente și tehnologii pentru sănătate care transformă datele biometrice de zi cu zi în informații practice despre starea de bine. Inelul Oura monitorizează indicatori precum somnul, ritmul cardiac, activitatea și stresul, prin intermediul unui dispozitiv ușor, conceput pentru purtare continuă. Compania se concentrează pe monitorizarea personală a sănătății în domenii precum sănătatea femeilor, sănătatea inimii și starea generală de bine, combinând date orientate către asistența medicală cu hardware și software accesibile consumatorilor. Pentru persoanele aflate în căutarea unui loc de muncă, ŌURA se află la intersecția dintre tehnologia medicală și producție, iar activitatea sa este legată de dezvoltarea unor produse care îi ajută pe oameni să își înțeleagă mai bine sănătatea.
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Staff AI Data Transformation Architect (Hybrid) at ŌURA

Lead ŌURA’s enterprise AI adoption across health data, product, engineering, science, and business teams. Build governance, tooling, Databricks and AWS platforms, and production LLM and RAG capabilities.

USStatele Unite ale AmericiiCACalifornia
169.150 $ – 233.000 $ / an
HibridNormă întreagăLiderLimbă Obligatorie: englezăInteligență artificială
AWSCloudPython

Descriere

  • Own the Data AI enablement roadmap across data, product, engineering, science, and business functions.
  • Develop repeatable AI adoption models supported by playbooks, shared tools, evaluation methods, and measurable outcomes.
  • Create common terminology and assessment frameworks for evaluating AI opportunities.
  • Guide build-versus-buy decisions, vendor strategy, and organization-wide AI technology investments.
  • Manage the enterprise AI toolset, including Databricks data and access integration, multi-agent workflows, token controls, and trusted data foundations.
  • Collaborate with Governance, Security, Privacy, and Legal teams to establish responsible-AI safeguards and auditable change processes.
  • Improve AI spending efficiency through observability, productivity practices, reclamation programs, and usage analysis.
  • Set the Data AI architecture strategy for Databricks and AWS environments operating at terabyte-to-petabyte scale.
  • Lead machine-readable data contracts, vector architectures, RAG implementations, and enterprise context-plane components for production LLM reporting and agentic AI.
  • Evaluate data readiness and coordinate remediation with analysts and engineers.
  • Redesign workflows in partnership with functional leaders.
  • Increase internal AI capability through training, practitioner communities, and self-service guidance.
  • Expand successful pilots into standardized company-wide practices.
  • Connect executive priorities with functional leaders and enabling teams.
  • Align AI productivity initiatives with engineering and business-unit goals.
  • Define operating standards for functional AI champions and AI-tool administrators.
  • Represent Data AI enablement in cross-company forums and present adoption and impact metrics to leadership.

Cerințe

  • At least 8 years of experience scaling AI data solutions, products, or technology adoption from pilots into sustained organization-wide practice with measurable results.
  • Experience creating durable operating models, standard procedures, and measurement systems that remain effective beyond individual projects.
  • A record of influencing senior leaders and building alignment around long-term technology investments.
  • Strong technical understanding of LLMs and implementation patterns, with the judgment to distinguish practical value from hype.
  • Hands-on administration of at least one enterprise AI productivity platform, such as Cursor, Claude, ChatGPT Enterprise, Glean, Gemini, coding assistants, or LLM API gateways, including user and group management and configuration troubleshooting.
  • Regular use of LLM assistants and agents with appropriate judgment, validation, and verification.
  • A practical, experience-based perspective on when to build versus buy technology.
  • Experience with enterprise data platforms, including lakehouse environments using AI or LLMs.
  • Practical knowledge of role-based access and permissions across multi-domain or multi-region environments.
  • Experience working with Security, Privacy, and Legal teams to define and implement responsible-AI controls in regulated or health-sensitive settings.
  • Ability to influence senior stakeholders and align R&D and business units without formal authority.
  • Ability to create and maintain SOPs, playbooks, and training that nontechnical users and administrators can follow independently.
  • Excellent written and verbal communication, from hands-on troubleshooting discussions to executive steering committees.
  • Preferred: Databricks Certified Administrator or Databricks Certified Data Engineer certification.
  • Preferred: Familiarity with data mesh principles and domain-oriented ownership models.
  • Preferred: Experience supporting ML workflows with MLflow, Feature Store, or Databricks Model Serving.
  • Preferred: Knowledge of dbt for data transformation and analytics engineering.
  • Preferred: Cloud and AI token FinOps experience, including cost-allocation tags, budget alerts, and tiered-storage optimization.
  • Preferred: Exposure to Databricks Lakehouse Federation or comparable cross-platform query federation approaches.
  • Preferred: Previous experience serving as an AI champion or leading a similar transformation initiative.
  • Preferred: Familiarity with scripting or configuration automation, such as Python, shell, or configuration-as-code, to reduce manual administration.
  • Preferred: Experience in health technology, consumer health, wearables, or another regulated, high-trust sector.
  • Preferred: Advanced experience designing production data architectures for LLM, RAG, and vector-based applications.
  • Preferred: Experience with MLOps frameworks, Vertex AI, MLflow, and production AI/ML lifecycle management.
  • Preferred: Ability to define data requirements for agentic AI and interactive self-service analytics systems.
  • Work location must be in the United States; applicants living in Alaska, Delaware, Iowa, Mississippi, Nebraska, South Dakota, West Virginia, or Wisconsin are not considered.

Beneficii

  • Competitive compensation with salary and equity components
  • Health, dental, vision, and mental health support
  • An Ōura Ring, along with discounts for employees’ friends and family
  • 20 days of paid time off, 13 paid holidays, and 8 flexible wellness days
  • Paid sick leave and parental leave

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