Job description

RDQ427R100 At Databricks, we are passionate about helping data teams 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 AI and data infrastructure platform so our customers can use deep data insights to improve their business. Founded by engineers — and customer obsessed — we leap at every opportunity to solve technical challenges, from designing next-gen UI/UX for interfacing with data to scaling our services and infrastructure across millions of virtual machines. And we're only getting started. The Serverless Compute Platform is the backbone of Databricks' fastest-growing products. It is powering massive growth in our existing product lines (e.g. Generic Compute, SQL) as well as new and emerging products (e.g. Lakewatch, interactive compute). Behind this hockey stick growth is a set of highly scalable, efficient, and intelligent services managing tens of millions of virtual machines daily across AWS, Azure, and GCP. As Engineering Manager for the Execution Sandbox team, you will own the end-to-end delivery of this new service and the engineers building it. You will inherit a team of strong senior ICs who have already delivered an initial preview. Your job is to build out the full vision, guide evolution, and scale the team. You will ensure strong execution health and that the service launches with production-grade reliability spanning a range of use cases, e.g. GPU onboarding, UDF generalization, and managed REPL. The impact you will have: Own a 0→1 service with platform-wide blast radius. Architect and launch the Execution Sandbox Service from inception to production scale. This greenfield provisioning layer will power all non-Spark compute workloads on Serverless (Notebooks, AI Agents, Remote UDFs). Unify a fragmented compute surface. Converge disparate CPU and GPU cluster management paths into a single provisioning service, eliminating parity bugs and enabling consistent product experiences. Collaborate across 5+ partner organizations. Drive alignment on API contracts and shared milestones across Serverless Platform, AI Runtime, Lakeguard, and product teams. Shape product strategy through deep technical understanding. Partner with Product Management to leverage this new sandbox primitive for future offerings like serverless command execution APIs and FaaS-style workloads. What we look for: 5+ years managing engineers building and operating distributed systems in production, ideally control-plane or orchestration services BS or higher in Computer Science or a related field. Equivalent practical experience is equally valued. Deep technical fluency in infrastructure systems. Ability to deeply review architecture docs, challenge design tradeoffs (e.g., state machine design, API boundaries), and coach senior ICs. Experience with multi-cloud or multi-region service deployment (AWS, Azure, GCP). Bias toward operational rigor. Deep commitment to observability, SLOs, pre-mortems, and healthy on-call cultures. Build and scale a high-caliber team. Manage and elevate a team of strong L3-L5 engineers, establishing clear ownership boundaries and architectural doctrine. You will also hire 2-3 additional engineers to support this expanded scope. 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 . Local Pay Range $180,500 — $225,600 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.

Requirements

Must-haves

As Engineering Manager for the Execution Sandbox team, you will own the end-to-end delivery of this new service and the engineers building it.
You will inherit a team of strong senior ICs who have already delivered an initial preview. Your job is to build out the full vision, guide evolution, and scale the team.
You will ensure strong execution health and that the service launches with production-grade reliability spanning a range of use cases, e.g. GPU onboarding, UDF generalization, and managed REPL.
Unify a fragmented compute surface. Converge disparate CPU and GPU cluster management paths into a single provisioning service, eliminating parity bugs and enabling consistent product experiences.
Collaborate across 5+ partner organizations. Drive alignment on API contracts and shared milestones across Serverless Platform, AI Runtime, Lakeguard, and product teams.
Shape product strategy through deep technical understanding. Partner with Product Management to leverage this new sandbox primitive for future offerings like serverless command execution APIs and FaaS-style workloads.
5+ years managing engineers building and operating distributed systems in production, ideally control-plane or orchestration services
BS or higher in Computer Science or a related field. Equivalent practical experience is equally valued.
Deep technical fluency in infrastructure systems. Ability to deeply review architecture docs, challenge design tradeoffs (e.g., state machine design, API boundaries), and coach senior ICs.
Experience with multi-cloud or multi-region service deployment (AWS, Azure, GCP).
Bias toward operational rigor. Deep commitment to observability, SLOs, pre-mortems, and healthy on-call cultures.
Our Commitment to Diversity and Inclusion

Nice-to-haves

None flagged.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aisqlawsazuregcpapi

Platform: greenhouse
Effort: quick

Benefits

Benefits detected in the listing.

Vision