Strategic Overview
Skills Systems Architect at Databricks is a uiux opportunity centered on the specialist responsibilities and outcomes described in the employer's listing.
The published role emphasizes work such as 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…
The role is listed for United States with the working arrangement published with the role, giving candidates the practical context needed to assess fit before applying.
Key information at a glance
About This Role
FEQ227R209
Key Responsibilities & Objectives
- 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.
- 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.
Required Qualifications & Candidate Blueprint
- 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.
Salary & Benefits
Published compensation: Zone 1: $139,000 – $191,050 USD · Zone 2: $125,000 – $171,950 USD · Zone 3: $118,100 – $162,350 USD · Zone 4: $111,200 – $152,900 USD
- 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.
Full Job Description
The following is the complete job description supplied by the source. We sanitize unsafe markup and remove external links, but do not shorten or rewrite the employer's source content.
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.
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.
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.
Editor’s Market Perspective (Our Custom Analysis)
Why This Position Matters
This uiux opening is a useful opportunity for candidates whose experience matches the role-specific capabilities named in the listing. Its strongest career signal is the scope of the work itself — particularly 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… — rather than a generic title alone.
Resume & Application Strategy
For the application, mirror the employer's language where it truthfully matches your background: foreground the role-specific capabilities named in the listing, quantify outcomes from comparable work, and place the most relevant experience near the top of the resume. If the listing specifies tools, platforms, leadership scope, or domain experience, use those exact terms in context instead of presenting an unrelated keyword list.
We organize source-provided information into this independent job-seeker overview. Compensation is shown only when the source provides it; the official listing remains the authority for current requirements and application instructions.