Senior Software Engineer, Capacity & Efficiency

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 Capacity & Efficiency Engineering team builds the software that manages, governs, and reduces Robinhood's AWS cloud spend across compute, databases, and storage, operating at the intersection of cloud infrastructure, data engineering, and FinOps. We own the full lifecycle of cloud cost, from attribution and forecasting to automation that rightsizes infrastructure at fleet scale and keeps a bursty, latency-sensitive trading platform both reliable and cost-effective. Our work carries CEO-level visibility, has already driven millions of dollars in annualized savings, and is delivered in close partnership with Infrastructure, Finance, and product engineering teams across Robinhood.

As a Senior Software Engineer on the team, you will own capacity and efficiency for Robinhood's storage and database fleet, from Aurora clusters to S3 and the caches and volumes in between. You will build the systems that size this fleet to demand: Aurora reader counts and writer sizes set from market hours, declared events, and live load instead of fixed schedules; databases, caches, and volumes rightsized to what they actually use; and S3 data kept in the right storage class, with lifecycle rules that keep growth in check. You will take each from recommendations the Storage team validates to automation that safely applies its own changes in production. This is a 0-to-1 build with measurable impact on reliability at market peaks and on the AWS bill.

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.

• →Build forecast-driven capacity planning for the storage and database fleet, turning traffic history, load tests, and known events (market open, earnings, IPOs, promotions) into plans that scale up ahead and back down after, with an expiry on every temporary capacity hold
• →Design and build a capacity planner for Aurora that sets reader counts and writer instance sizes from demand and load signals (IOPS, CPU, connections, replica lag), replacing fixed scaling schedules
• →Rightsize databases, caches, and block storage (RDS, ElastiCache, DynamoDB, EBS) on instance size, provisioned IOPS and throughput, and idle capacity, and keep them right-sized as load changes
• →Own S3 efficiency at scale: storage class and Intelligent-Tiering strategy, lifecycle and retention policies, noncurrent versions and incomplete uploads, and request costs, using Storage Lens and inventory data
• →Take every recommendation to closed-loop automation with guardrails, progressive rollout, and rollback, in partnership with the Storage infrastructure team, handling constraints such as AWS capacity shortages, fallback instance types, and connection pooling
• →Measure outcomes in cost and reliability (savings against baseline, forecast error, utilization, capacity left behind after events) and share our forecasts with the pod rightsizing system so compute and storage scale together

• Deep hands-on experience operating AWS storage and databases at scale, especially Aurora or RDS (MySQL or PostgreSQL) and S3, including replication, read scaling, storage classes, and lifecycle management
• Capacity planning and forecasting experience: modeling time-series demand, load testing, and translating traffic into instance counts, sizes, and storage growth
• A track record of rightsizing infrastructure from utilization data (CPU, memory, IOPS, throughput, bytes stored) without hurting latency or availability
• Strong Python or Go engineering, with experience building automation that safely modifies production infrastructure (guardrails, progressive rollout, and rollback built in), not just systems that observe and report
• Familiarity with AWS storage and database pricing (S3 storage classes and requests, Aurora I/O-Optimized vs. Standard, Reserved Instances) is a strong plus
• Experience with bursty, event-driven workloads (trading, payments, ticketing) or with Spark, Airflow, or Kubernetes is a plus