Staff Software Engineer, Event Streaming Systems

About the Team

The Storage organization builds and operates the online stateful systems and abstractions that DoorDash Engineering depends on: reliable, efficient, secure, and easy to use. Within Storage, the Event Streaming Systems team owns the event streaming platform that powers business-critical, event-driven workflows across DoorDash, Wolt, and Deliveroo, including Kafka, our event streaming abstraction layer (internally called Event Bus), and the next generation of streaming platform technologies.

These systems sit on the Tier-0 critical path. They move trillions of events per day across multiple regions and underpin how hundreds of engineering teams build reliable, event-driven products at massive scale.

About the Role

We're looking for a Staff Software Engineer to set the technical direction for our event streaming platform and lead its evolution over the next several years. This is a senior individual-contributor role: you'll operate as a force multiplier across teams, owning the hardest architectural problems, driving org-wide technical strategy, and raising the engineering bar for everyone who builds on and operates streaming infrastructure.

You'll go deep on the internals of modern streaming systems, from the Kafka protocol and storage engine to object-store-backed, diskless architectures like WarpStream and AutoMQ. You'll make the high-leverage bets that determine how DoorDash streams data for years to come, on a platform that serves hundreds of engineering teams and trillions of events per day.

You must be located in the New York Metro Area for this hybrid position. You will report to the Engineering Manager on our Event Streaming Systems team within the Storage organization.

You're excited about this opportunity because you will…

• Set the multi-year technical vision and architecture for DoorDash's event streaming platform, and drive alignment in that direction across Storage, SRE, Data Platform, and Product Engineering.

• Lead the largest and most ambiguous streaming initiatives end to end, such as re-architecting toward diskless, object-store-backed streaming to cut cost and operational overhead across the fleet.

• Go deep on the internals of Kafka and modern streaming systems (WarpStream, AutoMQ, and similar), and use that depth to solve problems that block the rest of the organization.

• Own architectural decisions with company-wide blast radius, from replication, consensus, and storage design to multi-region topology, capacity, and cost efficiency.

• Debug and resolve the hardest distributed systems problems in production, including subtle correctness, performance, and reliability issues under Tier-0 constraints.

• Raise the technical bar through design reviews, mentorship of senior engineers, and by establishing the standards and patterns others build on.

• Partner with engineering leadership to shape roadmaps, make build-versus-buy and open-source-versus-managed decisions, and translate business needs into platform strategy.

We're excited about you because…

• You have deep 8+ years of hands-on experience designing, building, and operating large-scale distributed systems in production, with a track record of technical leadership across multiple teams or an organization.

• You have operated event streaming systems such as Kafka at scale, and you understand their internals, including the log storage engine, the ISR replication model, metadata consensus (KRaft/ZooKeeper), the consumer/producer protocol, partitioning, and rebalancing.

• You are a strong engineer in Java, Go, or a similar language, with deep expertise in concurrency, distributed systems, and building reliable, maintainable production infrastructure.

• You have led high-impact, cross-team initiatives from ambiguous problems to shipped outcomes, and you can influence technical direction across an organization without relying on authority.

• You have strong fundamentals in Linux, networking, storage, and JVM-based systems, and you use them to reason about performance and failure modes from first principles.

• You understand the trade-offs of object-store-backed streaming, including write-path latency versus cost, cross-AZ network cost avoidance, the separation of compute and storage, and object metadata and caching design.

• You communicate with clarity and can bring engineers, managers, and leaders along on complex technical decisions and trade-offs.

• You have proficiency in using AI coding tools (e.g., Claude Code, Codex, Cursor) in the full software development lifecycle, including designing, generating code, testing, monitoring and releasing software

Preferred

• Experience with the internals of object-store-backed or diskless streaming architectures such as WarpStream or AutoMQ, or with tiered storage in Kafka.

• Contributions to Kafka or related open-source distributed systems (Pulsar, Redpanda, or similar).

• Experience with broker and JVM tuning, partition management, or Cruise Control at large scale.

• Experience designing multi-region streaming platforms and reasoning about their consistency, durability, and cost trade-offs.

• Experience leading migrations of Tier-0 infrastructure with zero customer-visible impact.

• Experience driving significant efficiency or cost wins on large streaming or storage fleets.

Notice Regarding Use of AI and Automated Tools: To streamline our hiring process, DoorDash utilizes an automated recruitment tool called Gem.

How it works: Gem assists our recruiting team by evaluating job related qualifications and characteristics in connection with hiring. The tool is designed and used to support - rather than replace - human decision-making; trained personnel make final decisions with meaningful human review and oversight, and DoorDash does not use Gem or other AI-enabled tool in a manner that has the effect of subjecting applicants or employees to discrimination based on any protected characteristic or proxy or for engaging in any protected activity under applicable law.

Data Retention, Privacy & Bias Audit: Data collected during this process is retained in accordance with our Candidate Privacy Policy and applicable state laws. In compliance with New York City Local Law 144, the independent bias audit summary for Gem is publicly available for review at our Careers Page .

Notice to Applicants for Jobs Located in NYC or Remote Jobs Associated With Office in NYC Only

We use Covey as part of our hiring and/or promotional process for jobs in NYC and certain features may qualify it as an AEDT in NYC. As part of the hiring and/or promotion process, we provide Covey with job requirements and candidate submitted applications. We began using Covey Scout for Inbound from August 21, 2023, through December 21, 2023, and resumed using Covey Scout for Inbound again on June 29, 2024.

The Covey tool has been reviewed by an independent auditor. Results of the audit may be viewed here: Covey

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.

In addition to base salary, the compensation for this role includes opportunities for equity grants. Talk to your recruiter for more information.

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