Skills Systems Architect

Databricks•Bellevue, WA

34d•$93,900 - $161,350

About The Position

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.

Requirements

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 Haves

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

Responsibilities

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.