Sr. Data Platform Evangelist

Oracle Corporation; Juneau (AK); On-site; USD 180,000 - 250,000; Full time

Job summary

Oracle Corporation seeks a senior technical leader to shape AI adoption programs that move from exploration to enterprise deployment. You will engage executives, guide architects, and build demonstrations that prove business value of AI across Oracle technologies and customer environments.

You will scale programs, create reusable assets, mentor engineers, and deliver practical guidance for production-ready AI solutions with governance and security in mind.

Qualifications

• Leads AI adoption programs with executive engagement and hands-on enablement.
• Translates experiments into business outcomes with practical implementation guidance.
• Develops reuseable assets, reference implementations, and demonstrations.

Responsibilities

• Design, launch, and scale AI adoption programs.
• Architect and build practical AI solutions connected to enterprise data.
• Lead executive briefings, workshops, and technical discussions.
• Mentor professionals in AI architecture and hands-on development.
• Partner with product, engineering, and field leadership to align AI initiatives.

Skills

AI strategy

Executive engagement

Leadership

Stakeholder mgmt

Cloud architecture

Tools

Oracle Cloud

OCI

Oracle Database

Job description

Oracle is seeking an accomplished technical leader who is passionate about AI, actively builds with emerging technologies, and knows how to help organizations turn experimentation into meaningful business outcomes.

In this senior individual contributor role, you will shape and lead AI adoption programs that combine executive engagement, hands‑on experiences, technical enablement, and practical implementation guidance. You will identify valuable use cases, build working solutions, and create repeatable approaches that help customers and Oracle teams confidently apply AI.

You will bring both strategic vision and hands‑on credibility: engaging executives on AI priorities, working with architects on implementation decisions, and building demonstrations that show what is possible. You will independently lead complex initiatives and scale your impact through reusable programs, technical assets, and mentorship.

AI is the primary focus of this role. Data platforms, cloud architecture, and governance provide the foundation for connecting AI to enterprise information and workflows.

What You’ll Do

Lead AI Adoption Strategy and Programs

• Design, launch, and scale AI adoption programs that help customers progress from awareness and use‑case discovery to experimentation, validation, and implementation.
• Build integrated programs incorporating executive briefings, AI Experiences, innovation workshops, hands‑on labs, and structured technical follow‑through.
• Help customers assess AI readiness, prioritize opportunities, and define adoption roadmaps with clear ownership and measurable outcomes.
• Establish program objectives, delivery approaches, reusable assets, and feedback mechanisms that support consistent execution across teams.
• Measure success through customer progression, validated use cases, implementation milestones, and sustained adoption.

Build and Demonstrate Practical AI Solutions

• Architect and build working solutions using generative AI, AI agents, retrieval‑augmented generation, vector search, and agentic workflows.
• Connect AI capabilities to enterprise data, applications, APIs, and business processes.
• Develop compelling demonstrations that communicate business value while standing up to technical scrutiny.
• Evaluate solution quality, reliability, security, latency, and cost, and explain the requirements for moving from prototype to production.
• Turn successful concepts into reusable reference implementations, demonstrations, and hands‑on learning experiences.

Shape Executive Conversations and AI Thought Leadership

• Serve as a trusted advisor to executives, technical leaders, and practitioners on enterprise AI strategy and adoption.
• Translate rapidly evolving AI capabilities into clear business implications, architectural choices, and actionable next steps.
• Lead executive briefings, customer workshops, conference presentations, webinars, and strategic technical discussions.
• Develop substantive technical content and practical guidance grounded in direct experimentation and customer experience.
• Articulate Oracle’s approach to enterprise AI with technical credibility and a clear understanding of the broader ecosystem.

Scale AI Capability Across Oracle and Its Ecosystem

• Equip evangelists, architects, sales teams, and partners to deliver effective AI engagements and adoption programs.
• Build facilitator guides, workshop curricula, labs, solution patterns, and implementation guidance that others can use and extend.
• Mentor technical professionals in AI architecture, hands‑on development, customer discovery, and executive communication.
• Partner with Product Management, Engineering, Marketing, and field leadership to align programs with customer needs and technical capabilities.
• Translate recurring customer challenges into actionable feedback that informs product direction and adoption strategy.

Establish the Data Foundation for Enterprise AI

• Help customers understand how data quality, access, integration, security, and governance affect AI outcomes.
• Design approaches that connect models and agents to relevant enterprise information with appropriate controls.
• Apply Oracle’s data and cloud capabilities to support retrieval, contextual grounding, analytics, and enterprise integration.
• Guide architectural tradeoffs across existing customer environments, including multicloud and hybrid deployments.

Core Focus Areas

Candidates should bring deep practical expertise in enterprise AI and sufficient data and cloud architecture knowledge to deliver integrated solutions.

• Enterprise AI:Generative AI, foundation models, AI agents, agentic workflows, tool integration, retrieval‑augmented generation, embeddings, vector search, and evaluation.
• AI adoption and programs:Use‑case discovery, readiness assessment, executive engagement, innovation workshops, hands‑on learning, adoption roadmaps, and outcome measurement.
• Responsible implementation:AI security, governance, access controls, human oversight, observability, reliability, and cost management.
• Supporting data and cloud platforms:Oracle Database, Autonomous Database, AI Vector Search, OCI, data integration, analytics, APIs, and multicloud architectures.