FEQ227R209

About the Role

Databricks needs to understand what technical capability looks like across several large, fast-moving populations: employees, customers, and partners. Today that happens through role-based learning pathways and product-aligned enablement, an approach that can't keep pace with how quickly the platform, technology, and roles change. AI has also collapsed the half-life of technical proficiency, and for the first time, it makes a living, self-maintaining model of capability possible.

In this builder role, you'll own systems for defining, measuring, and developing technical capability across Databricks learner audiences. You'll build a capability model that ties roles, skills, content, and credentials together and the instrumentation that shows where capability actually stands.

What You'll Own

Skills taxonomies, capability models, and learning context

• Define the skills taxonomies that make up technical capability and shape how they’re organized; what skills exist, which are adjacent, which are prerequisites, how quickly they go stale, and what sources are available to learners for developing and maintaining them.

Observability and measurement

• Stand up instrumentation and AI-informed signals that show where capability stands and where it's drifting. Develop live signals, not a quarterly or monthly health index, to show how skills are moving and evolving.
• Anticipate where capability demand is heading. Product releases, market shifts, and role evolution are constant; track changes closely so emerging skills surface early and content and programs stay ahead of change.

AI-native tooling

• Build the software that maintains the skills taxonomy and capability model, including LLM-driven skill extraction and organization, agentic pipelines that keep them current, automated drift and gap detection, and APIs that expose it all.
• Make the model reusable enterprise context that other systems, teams, and products build on vs. a training-only asset.

Impact You'll Have

You will sit upstream of and across several teams and functions:

• Anywhere skills show up in products: You define what technical capability means and what evidence counts, so wherever skills are inferred, captured, or recognized, it reflects real technical work and skills & abilities.
• Content and curriculum. The skills taxonomy and capability model influence what gets built next and why. Learning context is a critical input to generative content.
• Learning architecture and in-product training: Your work informs what learning belongs where and how it’s presented. Pathways are assembled with the model and taxonomy instead of mapped by hand; in-product training surfaces them to learners.
• Certification & accreditation. The capability model grounds skills assessment to guide and accelerate exam developers.
• Learning & enablement. You give the organization a current view of capability across every audience, and a shared model to build and plan against.

What We're Looking For

• Experience in technical training, learning, enablement, or product education in data & AI, cloud, or comparable product categories.
• Experience designing capability or skills models and the systems around them, spanning modeling, measurement, and instrumentation.
• A builder mindset. Ability to use Python and SQL and build apps, with AI assistance, and wire up pipelines and stand up tooling yourself.
• AI-native. AI tooling is how you build and reason, from extraction and assessment to agentic workflows and evaluation. You understand when to reach for AI and when not.
• A track record of moving strategy as an IC through analysis and clear writing.

Nice to Have

• Familiarity with off-the-shelf skills-intelligence tooling and build-vs-buy tradeoffs.
• Experience instrumenting capability data across several audiences.

What Success Looks Like in Year One

• A live skills taxonomy, capability model, and learning context resources are published, and content, learning, and certification teams plan against them.
• Decisions about what to build, refresh, or retire leverage the skills taxonomy and capability model, so content better keeps pace with the product, and investment goes where capability demand and skill gaps are real.
• In-product training uses the model to target and measure learning, so learners can see what moves them forward, and stakeholders get insight into skills attainment within their audiences.
• Teams that bring skills into products work from the same model, so a skill means the same thing wherever someone encounters it.