About the Team

The Storage teams build and operate online stateful systems and abstractions that are reliable, efficient, secure and easy to use for DoorDash Engineering. The teams are responsible for understanding Product Engineering’s evolving needs and developing platform and infrastructure capabilities to serve them. The team currently supports CockroachDB, Cassandra, Kafka and Redis as well as data abstraction services to reduce the complexity of interacting with storage systems for Product Engineers.

About the Role

The Storage team is building and operating a high-performance, scalable, and reliable data abstraction layer that optimizes both efficiency and reliability. Our goal is to create a platform that manages itself and fades into the background—empowering engineers to focus on delivering product experiences our customers love.

This role is available across two teams within Storage, each solving unique and high-impact challenges:

One team owns DoorDash’s vector storage infrastructure—operating and evolving vector search systems and future storage indexing systems. You’ll tackle challenges in distributed systems, performance, and reliability, turning complex, stateful indexing workloads into scalable, developer-friendly services used across the company.
One team builds and operates the distributed data platform powering DoorDash's largest stateful workloads -- including Cassandra, which backs critical product surfaces across DoorDash, Wolt, and Roo. You'll design high-throughput data abstractions, smart clients, and platform services that make distributed data reliable and easy to work with at multi-petabyte, multi-million-QPS scale, with opportunities to go deep on distributed systems internals and contribute to the open-source Cassandra ecosystem.

If you're passionate about distributed systems, developer experience, and building foundational infrastructure at scale, we'd love to hear from you.

You must be located in San Francisco, Sunnyvale, Seattle, or the New York Metro Area for this hybrid position. You will report into the Engineering Manager on our Storage team within the Core Infrastructure Organization.

You’re excited about this opportunity because you will…

Build and optimize distributed storage systems that improve data reliability, consistency, and performance.
Own high-leverage control-plane services with clear customer impact, strong reliability requirements, and room for deep technical exploration.
Develop and maintain data abstraction services tailored for high-throughput, low-latency workloads.
Implement and refine caching strategies using Redis, Memcached, and similar technologies to enhance system efficiency.
Collaborate with teams across infrastructure, platform, and application layers to ensure seamless data access and storage performance.
Drive innovation in storage architecture by identifying and addressing scalability and efficiency challenges in distributed systems.

We’re excited about you because…

You have 2+ years of fulltime working experience in designing, building and maintaining scalable, distributed data infrastructure.
You are proficient in GoLang, or Java with a deeper expertise in multi-threading, distributed systems and databases.
You have deep expertise in NoSQL databases (Cassandra, DynamoDB, or similar) and distributed caching systems (Redis, Memcached).
You have built and optimized high-scale data storage systems with a focus on reliability, availability, and performance.
You have expertise in workflow/orchestration engines (Temporal/Cadence/Argo, job runners) and modeling long-running, stateful workflows.
You thrive in a fast-paced, execution-driven environment and have a proven track record of delivering impactful storage solutions.

Compensation

The successful candidate's starting pay will fall within the pay range listed below and is determined based on job-related factors including, but not limited to, skills, experience, qualifications, work location, and market conditions. Base salary is localized according to an employee’s work location. Ranges are market-dependent and may be modified in the future.