Senior Engineering Manager for Self-Serve (Learning)
DatabricksMountain View, California, United States · Posted 5 days agoDescription
RDQ426R220
At Databricks, we are passionate about enabling data teams to solve the world's toughest problems — from making the next mode of transportation a reality to accelerating the development of medical breakthroughs. We do this by building and running the world's best data and AI infrastructure platform so our customers can use deep data insights to improve their business.
The Self-Serve team owns Databricks' product-led growth motion — the experience that takes someone from "I just heard about Databricks" to succeeding on the platform entirely on their own. Our most ambitious bet is Learning: making Databricks the place where anyone interested in data + AI comes to learn, and building the largest community of active, capable learners in the world. It's a long game with a simple thesis — if people learn data + AI on Databricks, it becomes ubiquitous with the field itself, driving adoption and revenue.
As a Senior Engineering Manager on the Self-Serve team, you will lead the Learning bet end to end across two sides: self-paced learning — a place to learn any Databricks skill, hands-on labs that spin up inside a real workspace, and a durable skill profile a learner carries across jobs — and enterprise-managed learning, giving admins the tools to assign, track, and grow learning inside their orgs. AI is central to both: a content-generation agent that scales the catalog far past what we could author by hand, and an AI tutor that guides learners hands-on inside the product. This is a genuine 0→1 product with real systems depth — on-demand provisioning, identity, sandboxing, and an interactive learning engine that must scale to millions of learners — and you'll grow and lead a team of ~12 engineers (planned to roughly double) to build it.
The impact you will have:
- Strategy & Vision: Define and drive the technical and product strategy for Learning, and tie it into the broader self-serve growth motion.
- Execution Ownership: Own the roadmap, execution, and delivery — taking a 0→1 product from early signal to millions of learners at the highest standards of quality.
- Engineering Excellence: Establish team best practices — design reviews, code quality, testing, and performance for high-scale, interactive systems.
- Cross-Functional Collaboration: Partner closely across R&D, the Learning & Enablement org, Marketing (university and online channels), and Field Engineering to align the product with how learners actually reach and adopt Databricks.
What we look for:
- Experience:
- 15+ years of software engineering experience with a strong track record of technical leadership and impact.
- 5+ years of engineering management experience, including 2+ years managing other managers (or clear readiness to).
- Technical Depth: A Staff engineer caliber IC background before pivoting to management, with full-stack experience (including back-end, not purely front-end/UI); comfort leading a mix of front-end and full-stack engineers.
- Scaling: Proven experience scaling engineering teams from 10 to 30+ engineers.
- Product & Domain Fit:
- A track record building and scaling consumer-facing products, ideally taking early-stage products from 0→1 through scale. Scope- and impact-driven over team-size-driven.
- Genuine excitement for product-led growth and putting AI to work in a real product.
- Systems at scale: Experience designing scalable, distributed, customer-facing systems, ideally in a SaaS environment.
- Collaboration: Strong ability to align technical strategy with company growth objectives across product, engineering, and go-to-market partners.
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 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 anticipates 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.
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