Engineering Manager owning end-to-end delivery and scaling of a new Execution Sandbox service powering non-Spark compute workloads across AWS, Azure, and GCP. Requires 5+ years managing engineers on distributed systems and deep infrastructure fluency.
181k – 226k/yr
On-site5+ YOEEngineering Management
About the role
Impact
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.
Responsibilities
Own the end-to-end delivery of the Execution Sandbox service and the engineers building it.
Build out the full vision, guide evolution, and scale the team.
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.
Manage and elevate a team of strong L3-L5 engineers, establishing clear ownership boundaries and architectural doctrine.
Hire 2-3 additional engineers to support this expanded scope.
Requirements
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.
Lead a high-performing engineering team focused on data visualization, leveraging AI/BI Dashboards. Oversee talent development, strategic execution, and operational excellence while managing technical debt and an ambitious product roadmap.
181k – 247k/yr
On-site5+ YOEEngineering Management
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