Jobs / United States / Databricks
Skills Systems Architect
Databricks · 🇺🇸 United States
Sponsorship verdict
Sponsorship possible
One solid signal, not two — worth applying, and worth asking about sponsorship early.
- Employer is on a government sponsor recordThe US Department of Labor certified 445 H-1B/E-3 labor condition applications for this employer between Oct 2025 and Jun 2026 (latest Jun 2026) — the step every H-1B hire needs first. USCIS also records 78 H-1B approvals in FY2023. Source: LCA disclosure data (US Department of Labor (OFLC)).
- The posting doesn’t mention sponsorshipSilence isn’t a refusal — ask the recruiter before investing much time.
- No salary bar for this routeH-1B has no fixed salary bar: the employer must pay at least the prevailing wage for the role and area. Cap-subject employers enter a lottery weighted by wage level. Source: https://www.federalregister.gov/documents/2025/12/29/2025-23853/weighted-selection-process-for-registrants-and-petitioners-seeking-to-file-cap-subject-h-1b, rules effective 2026-02-27.
- What Databricks paid sponsored hires in similar roles3 certified filings for “Senior Solutions Architect” (Computer Systems Analysts) in CA: $132k–$175k, median $175k. Most were filed at wage level IV (67%) — 4 lottery entries, ≈61% projected selection odds for cap-subject employers. Source: US Department of Labor LCA disclosure data (Oct 2025 – Jun 2026).
- Confirmed live todayWhen a source last listed this job as open.
US H-1B: cap-subject employers enter a lottery weighted by wage level — Level I gets 1 entry, Level IV gets 4 (DHS projected selection odds ≈15% at Level I to ≈61% at Level IV). Universities and non-profit research employers are cap-exempt. The $100,000 fee for new petitions from abroad is currently blocked by a court order (appeal pending).
A verdict summarises public evidence; it is not legal advice and never a guarantee — the employer and the immigration authority decide. Sign in to factor in where you can already work.
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Sponsor Radar — Databricks
The US Department of Labor certified 445 H-1B/E-3 labor condition applications for this employer between Oct 2025 and Jun 2026 (latest Jun 2026) — the step every H-1B hire needs first. USCIS also records 78 H-1B approvals in FY2023. Source: LCA disclosure data (US Department of Labor (OFLC)).
Past sponsorship or register membership never guarantees sponsorship for this vacancy or for you. Full Sponsor Radar for Databricks →
About the role
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. •