SLSQ227R703
About the Role
The Field Engineering role is changing quickly. AI is changing how technical work gets done, products and customer needs are evolving faster, and the capabilities that distinguish an exceptional FE today will not be exactly the same ones that matter tomorrow.
Databricks is looking for a Lead Technical Enablement Specialist to help define and build the capability engine for our global Field Engineering organization. You will shape how FEs build expertise, demonstrate readiness, share what they know, and apply their capabilities with customers.
This role owns a portfolio, not a single program. That portfolio may include skills verification, technical validation, role-based readiness paths, practical challenges, and new approaches we have not yet imagined. Your job is to understand where the field is headed, identify the capabilities that will matter most, and turn that insight into experiences and systems that help FEs grow and perform.
This is a high-visibility, high-autonomy role for someone who wants to build. You will work with FE leaders, product and technical communities, regional teams, enablement partners, HR, and internal platform teams to define what “field-ready” means and make it useful in the flow of work. You will have the opportunity to influence how a global technical organization develops - and to create programs that become part of how Databricks operates.
The best person for this role is equal parts strategist, product thinker, builder, and operator. You can see around corners, create structure from ambiguity, earn trust with senior stakeholders, and turn an idea into something the field actually uses. You are energized by the possibility of using AI not only as a topic to teach, but as a catalyst to redesign how capability is built, measured, and scaled.
What You'll Do
• Set the vision and roadmap for FE capability. Develop a clear point of view on how Databricks should build, demonstrate, recognize, and continuously evolve field expertise. Translate FE strategy, product priorities, customer needs, and the changing nature of technical work into a focused portfolio of investments.
• Own and evolve a portfolio of readiness experiences. Bring together existing programs and new opportunities across skills verification, technical demonstrations, role-based pathways, credentials, and practical application. Decide what to scale, redesign, connect, or retire to keep the portfolio coherent and valuable.
• Invent new ways for FEs to build and prove expertise. When the field needs a new capability, design the right experience - not automatically another course. This could be a customer scenario, a live technical demonstration, a peer review, a contribution model, an AI-enabled assessment, or something entirely new. Prototype quickly, learn from the field, and scale the strongest ideas.
• Make readiness relevant to the work FEs actually do. Partner with practitioners, managers, product teams, and SME communities to define credible standards and evidence of capability. Embed development and validation into existing FE workflows wherever possible, making readiness a pull-based experience rather than an additional layer of process.
• Help the organization adapt to an AI-enabled future. Stay close to how AI is changing FE workflows, technical depth, customer engagement, and delivery models. Ensure our capability models measure judgment, problem-solving, architecture, collaboration, and customer impact - not just static product knowledge. Experiment with AI to improve assessment quality, personalize development, surface expertise, and scale program operations responsibly.
• Create the systems and signals that make capability visible. Partner with internal platform and engineering teams to turn program concepts into simple, scalable experiences. Ensure that skills, evidence, readiness milestones, and development recommendations are useful to FEs, managers, and leaders in the systems they already use.
• Build the operating model for a federated technical organization. Establish clear ownership, governance, quality standards, content and assessment lifecycles, and feedback loops across global programs, product SMEs, regional leaders, and enablement partners. Create mechanisms that let expertise scale without centralizing every decision.
• Drive adoption through influence and strong field experience. Build the narrative, stakeholder plan, manager engagement, and operating rhythm needed to make programs matter. Listen carefully to the field, remove friction, and use adoption data and qualitative feedback to improve the experience.
• Give leaders a clear view of capability and opportunity. Define meaningful measures of participation, proficiency, capability coverage, readiness, and emerging gaps. Use data to help leaders decide where to invest, where to raise the bar, and how the FE organization is evolving.
• Connect capability to growth and career opportunities. Partner with FE leadership, HR, and career architecture owners to make demonstrated capability useful in development conversations, specialization paths, succession planning, and career progression where appropriate.
What Success Looks Like
In your first year, you will have:
• Established a compelling, shared point of view on what FE capability and readiness need to look like in an AI-enabled Databricks.
• Created a clear roadmap for the field capability portfolio, with explicit priorities and decisions about what to scale, improve, or stop.
• Launched or materially evolved high-value readiness experiences that FEs and managers see as relevant to their work - not administrative requirements.
• Built strong partnerships across FE leadership, product and technical communities, regional teams, HR, and internal platform teams.
• Given leaders and managers better visibility into capability, gaps, and trends - and helped them act on those signals.
• Established a scalable operating model