At a glance
Build Databricks’ AI-native skills intelligence systems: define capability models, instrument live skill signals, and develop LLM-driven pipelines and APIs that keep technical learning aligned with fast-changing data and AI products.
Summarized by AI from the original posting
What you'll do
- Define skills taxonomies, capability models, and learning context
- Build instrumentation and AI-informed signals to measure technical capability
- Track emerging skill demand and capability gaps
- Build AI-native tooling including LLM-driven skill extraction, agentic pipelines, drift detection, and APIs
- Make the capability model reusable across enterprise systems and products
- Support content, curriculum, learning architecture, certification, and enablement teams
- Publish a live skills taxonomy, capability model, and learning context resources
Requirements
- 01Experience in technical training, learning, enablement, or product education in data and AI, cloud, or comparable product categories
- 02Experience designing capability or skills models and related systems
- 03Ability to use Python and SQL, build applications, wire up pipelines, and stand up tooling
- 04AI-native approach to extraction, assessment, agentic workflows, and evaluation
- 05Track record of moving strategy as an individual contributor through analysis and clear writing
- 06Familiarity with skills-intelligence tooling and build-vs-buy tradeoffs preferred
- 07Experience instrumenting capability data across several audiences preferred
Perks
Skills
Full description
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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.
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$117,400—$161,350 USDZone 2 Pay Range$105,600—$145,200 USDZone 3 Pay Range$99,800—$137,150 USDZone 4 Pay Range$93,900—$129,150 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.
Benefits
At 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.