Skills Systems Architect

FEQ
227R209

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.

• 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.

• 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.

• 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.

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.

• 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…