Senior Software Engineer, Spark Platform

DoorDash USA•San Francisco, NY

2d•Hybrid

About The Position

The Spark Platform team owns and operates DoorDash's Apache Spark ecosystem — the execution runtime, remote shuffle service, cluster scheduler, and reliability tooling that powers the company's data, analytics, and ML workloads. We run Spark across the company at significant scale and continue to expand the workloads, capabilities, and consumer base we serve. Orchestrating and operating thousands of Spark cluster deployments is a complex distributed system problem which the team invests heavily in runtime optimization, systems architecture, multi-tenant scheduling, and end-user tooling. As a Senior Software Engineer on Spark Platform, you will set the technical direction for our in-house Spark deployment and shape the architecture that will run DoorDash's data, analytics, and ML compute for the next five years and beyond. You will own the deep, cross-cutting problems that span the runtime, the shuffle service, the scheduler, and the overall service reliability — making the architectural calls that compound across the platform's lifetime. You will partner with the Engineering Manager on technical roadmap, hiring, and team shape, and act as the senior technical voice in cross-team partnerships with Data Engineering, ML Platform, and product engineering teams that depend on the platform. You must be located in San Francisco, Sunnyvale, Seattle, or New York City for this hybrid position. You will report into the Engineering Manager on our Spark Platform team.

Requirements

B.S., M.S., or PhD in Computer Science or equivalent.
6+ years of industry experience designing and operating distributed systems at scale.
Deep, hands-on experience with Apache Spark — internals, query execution, shuffle, the executor/driver model — at platform scale on Amazon EMR, Databricks, or an in-house deployment, with a focus on platform operations (runtime upgrades, cluster lifecycle, shuffle, observability, multi-tenant scheduling) rather than authoring individual Spark jobs.
Production experience with one or more of: remote/external shuffle systems (Celeborn, Magnet, Cosco, or similar), batch/big-data schedulers (YuniKorn, Volcano, Kueue, or the Spark-on-Kubernetes operator), or the observability and SRE patterns that make distributed compute platforms operable.
Strong fluency operating workloads on Kubernetes in production — operator patterns, executor pod lifecycle, network topology, and the multi-tenant failure modes that show up at scale.
Familiarity with data lake table formats such as Apache Iceberg or Delta Lake, and with the query and SQL engines that read them.
Track record of acting as a technical leader on a platform team — setting direction, mentoring, and partnering with management on roadmap and hiring.
Professional experience with Scala, Java, Python, or Go; strong SQL.
You are located or willing to relocate to the Bay Area, Seattle, or NYC.

Responsibilities

Set the multi-year technical direction for an in-house Spark-on-Kubernetes platform — runtime, shuffle, scheduler, reliability — and make the architectural calls that compound for years.
Own the deepest distributed-systems problems on the team: shuffle architecture, multi-tenant scheduling, runtime performance, and the failure modes that only show up at scale.
Partner with the Engineering Manager on technical roadmap, hiring, interview design, and team shape as the team continues to grow.
Uplevel the rest of the team through design reviews, mentorship, and raising the bar on what we ship.
Represent Spark Platform in cross-team architecture forums and shape how data, analytics, and ML workloads land on the platform.