Senior Staff Software Engineer, Data Platform

Our mission is to democratize finance for all. An estimated $124 trillion of assets will be inherited by younger generations in the next two decades. The largest transfer of wealth in human history. If you’re ready to be at the epicenter of this historic cultural and financial shift, keep reading.

We are building an elite team, applying frontier technologies to the world’s biggest financial problems. We’re looking for bold thinkers. Sharp problem-solvers. Builders who are wired to make an impact. Robinhood isn’t a place for complacency, it’s where ambitious people do the best work of their careers. We’re a high-performing, fast-moving team with ethics at the center of everything we do. Expectations are high, and so are the rewards.

The Data Platform organization builds and operates the systems that power how data is stored, moved, and consumed across Robinhood. This organization spans three core pillars: Storage (Postgres, DynamoDB, and caching systems), Streaming (real-time event infrastructure), and Data Lake (ingestion and compute systems built on Delta Lake). Together, these platforms support transactional workloads, real-time data processing, and large-scale analytics that are critical to Robinhood’s products and operations. The team owns the full lifecycle of data—from low-latency order path systems to near real-time and batch analytics—serving millions of users and internal teams across the company.

As a Senior Staff Software Engineer, you will serve as the technical lead across the Data Platform organization, shaping architecture and guiding execution across multiple teams. You’ll work on complex distributed systems challenges such as database sharding and proxy architectures, real-time streaming and CDC systems, and large-scale data ingestion and compute platforms. You’ll define and drive key technical bets, partner with engineering leaders to align platform capabilities with business needs, and lead 0→1 initiatives that introduce new capabilities across storage, streaming, and data systems. This is a rare opportunity to influence multiple critical systems at once while raising the technical bar across an entire organization!

Here are a few talks + blog posts from the team!

• Listen to our director speak to the work we are building!
• Read a blog post or listen to this chat from members of the team!
• Robinhood’s Use of WarpStream for Logging 
• Robinhood’s Kafka Journey from EC2 to Kubernetes
• Robinhood’s Kafka Proxy

This role is based in our Bellevue, WA office, with in-person attendance expected at least 3 days per week.

At Robinhood, we believe in the power of in-person work to accelerate progress, spark innovation, and strengthen community. Our office experience is intentional, energizing, and designed to fully support high-performing teams.

• →Lead architectural direction across storage, streaming, and data lake platforms, connecting systems that handle transactional, real-time, and analytical workloads
• →Design and guide implementation of distributed systems, including database sharding, proxy-based query routing, and real-time event processing pipelines
• →Improve data freshness and latency by evolving streaming and ingestion systems toward near real-time processing goals
• →Partner with engineering leaders and teams across Robinhood to define platform strategy, align roadmaps, and ensure systems meet reliability, scalability, and performance requirements
• →Drive 0→1 initiatives that introduce new platform capabilities, including next-generation streaming, CDC, and data processing systems

• Extensive experience building and scaling distributed systems, with deep expertise in at least two of the following areas: storage systems, streaming platforms, or data lake / large-scale data processing
• Strong understanding of database systems such as PostgreSQL and/or DynamoDB, including replication, sharding, and performance optimization
• Experience with streaming and event-driven architectures using technologies such as Kafka, Flink, or similar systems
• Familiarity with modern data platforms and compute engines such as Spark, Delta Lake, or equivalent large-scale data processing systems
• Proven ability to lead complex technical initiatives, define long-term architecture, and collaborate across multiple teams