Lead Technical Enablement Specialist, Field Capability & Readiness

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 that enables expertise to grow across the organization without creating unnecessary central processes.

• Demonstrated measurable progress in adoption, capability coverage, field confidence, or other outcomes that matter to the business.

What We're Looking For

• 6+ years of experience in technical enablement, product management, learning and development, consulting, field engineering, or program leadership within a high-growth technology company.

• A track record of building and scaling something new from ambiguity. You have shaped the strategy, aligned stakeholders, designed the experience, launched it, measured it, and improved it over time.

• Strong strategic judgment and executive presence. You can partner with Directors and VPs to shape priorities, make tradeoffs, challenge assumptions constructively, and turn a broad vision into clear decisions.

• A product mindset. You start with the user and the problem, test assumptions, design for adoption, and continuously improve the experience based on evidence.

• The ability to balance rigor with practicality. You know how to create credible standards and meaningful signals without adding unnecessary friction for the field.

• Strong data fluency. You can define useful measures, interpret dashboards, identify patterns, and use data and field feedback to drive decisions.

• Experience partnering with internal product, engineering, or platform teams to turn program requirements into scalable tools and workflows.

• Genuine curiosity about AI and its impact on technical work, customer engagement, and organizational capability. Hands-on experience using AI to improve assessment, personalization, content, or operations is a plus.

• Enough technical fluency in data and AI to work credibly with technical experts and evaluate whether readiness standards are relevant and meaningful - or the curiosity and learning agility to build that fluency quickly.

• Excellent communication skills. You can translate between technical practitioners, SME communities, enablement teams, regional leaders, and executive stakeholders.

• A strong ownership mindset, with the pace, creativity, and judgment to operate effectively in a distributed, cross-functional environment.

Nice to Have

• Experience designing skills taxonomies, competency frameworks, technical certifications, assessment models, or role-based capability systems.

• Experience building community-driven models for sharing and validating expertise, such as peer review, SME networks, contribution-based credentials, or practitioner communities.

• Familiarity with learning science, adult learning, assessment design, or evidence-based evaluation.

• Experience integrating capability or enablement programs with internal platforms, such as skills systems, LMS platforms, CRM systems, or custom enterprise tools.

• Experience with industry-specific enablement or vertical go-to-market strategies in enterprise software.

• Experience in a customer-facing technical role, such as solutions architecture, sales engineering, consulting, technical account management, or data and AI engineering.

Why This Role Matters

A global FE organization cannot scale its impact through content and training alone. It needs a shared view of what great looks like, practical ways for people to build and demonstrate expertise, strong communities that spread knowledge, and useful signals that help leaders invest in the right capabilities.

This role will help build that system for Databricks. You will shape how the FE organization responds to the AI era, make expertise more visible and transferable, and create the conditions for thousands of FEs to grow their impact with customers. The work will be visible, highly cross-functional, and consequential to how Field Engineering operates in the future.

Pay Range Transparency

Databricks is committed to fair and equitable compensation practices. The pay range(s) for this role is listed below and represents the expected base salary range for non-commissionable roles or on-target earnings for commissionable roles. Actual compensation packages are based on several factors that are unique to each candidate, including but not limited to job-related skills, depth of experience, relevant certifications and training, and specific work location. Based on the factors above, Databricks anticipated utilizing the full width of the range. The total compensation package for this position may also include eligibility for annual performance bonus, equity, and the benefits listed above. For more information regarding which range your location is in visit our page here.

Zone 1 Pay Range

$171,400—$235,750 USD

Zone 2 Pay Range

$154,300—$212,200 USD

Zone 3 Pay Range

$145,800—$200,400 USD

Zone 4 Pay Range

$137,100—$188,550 USD

About Databricks

Databricks is the Data and AI company. More than 20,000 organizations worldwide — including adidas, AT&T, Bayer, Block, Mastercard, Rivian, Unilever, and 70% of the Fortune 500 — rely on the Databricks Data + AI Platform to build and scale data and AI apps, analytics and agents. Headquartered in San Francisco with 30+ offices around the globe, Databricks offers a unified platform that includes Genie, Lakebase, Agent Bricks, Lakeflow, Lakehouse, and Unity Catalog. To learn more, follow Databricks on LinkedIn, X, YouTube, and Instagram.BenefitsAt Databricks, we strive to provide comprehensive benefits and perks that meet the needs of all of our employees. For specific details on the benefits offered in your region click here.

Our Commitment to Diversity and Inclusion

At Databricks, we are committed to fostering a diverse and inclusive culture where everyone can excel. We take great care to ensure that our hiring practices are inclusive and meet equal employment opportunity standards. Individuals looking for employment at Databricks are considered without regard to age, color, disability, ethnicity, family or marital status, gender identity or expression, language, national origin, physical and mental ability, political affiliation, race, religion, sexual orientation, socio-economic status, veteran status, and other protected characteristics.

Compliance

If access to export-controlled technology or source code is required for performance of job duties, it is within Employer's discretion whether to apply for a U.S. government license for such positions, and Employer may decline to proceed with an applicant on this basis alone.