Staff Software Engineer, Product Data Platform

Engineering at Brex

Engineering at Brex is about building systems that scale with speed and intention. Our teams span Software, Data, Security, and IT, and operate with high autonomy and deep collaboration. We tackle hard technical problems, own our outcomes, and push for excellence at every level — from architecture to deployment. It’s an environment where engineering is a craft, and builders become leaders.

What you’ll do

This opportunity is on our Product Data Platform (PDP) team, which builds and operates the high-performance data, query, and search infrastructure that powers critical Brex product experiences, including AI-powered data products like our agentic reporting experience. PDP's mission is to make Brex customizable, scalable, and reliable for finance teams, and that requires deeply optimized, production-grade distributed systems.

As a Staff Software Engineer, you'll shape the technical direction of the platform across multiple teams. Your mandate is to identify the highest-impact architectural problems across the data products group, including the cross-cutting ones that fall between teams, and drive them to resolution. You'll be accountable for the group's technical quality, move between long-horizon strategy and hands-on execution, and lead through influence rather than authority.

Where you’ll work

This role will be based in our San Francisco office. We are a hybrid environment that combines the energy and connections of being in the office with the benefits and flexibility of working from home. We currently require a minimum of three coordinated days in the office per week, Monday, Wednesday and Thursday.

As a perk, we also have up to four weeks per year of fully remote work!

Responsibilities

• Lead technical strategy for critical PDP initiatives, balancing long-term architecture with near-term delivery across multiple teams.
• Identify and resolve systemic performance, consistency, and scalability problems, including cross-team gaps no single team can own.
• Own architecture direction for query performance: query plans, execution paths, cost models, indexing strategies, and schema and access-pattern design.
• Design and evolve distributed systems with thoughtful tradeoffs around consistency, latency, caching, cost, and failure modes.
• Raise the technical bar across the group through design reviews and architecture decisions, and dive in hands-on where it matters most, including P95/P99 latency issues in production.
• Proactively identify scaling risks and reliability gaps, and turn findings into plans the group aligns behind.
• Mentor senior engineers and grow the next generation of technical leaders within PDP.
• Contribute directly on high-stakes initiatives and critical path decisions, primarily in the JVM ecosystem (Kotlin/Java).

Requirements

• 8+ years of backend engineering experience building and operating production systems at scale, including as a technical leader on multi-team initiatives.
• Track record of leading architecture and delivery for large-scale distributed systems in a platform or infrastructure environment.
• Deep hands-on expertise with relational databases (Postgres or Aurora strongly preferred), including query plan analysis, indexing strategies, and real-world optimization.
• Ability to drive ambiguous, cross-cutting problems to resolution without a defined charter.
• Strong written and verbal communication, with the ability to influence stakeholders and align teams without direct authority.
• Experience mentoring senior engineers.

Bonus

• Hands-on experience with OpenSearch / Elasticsearch or similar systems in production.
• Experience with streaming data platforms (Flink, Kafka) or semantic/analytics layers in production.
• Experience with multi-tenant or data-heavy platforms in regulated environments.
• Experience building platform foundations for AI or agentic product experiences.

Compensation

The expected salary range for this role is $240,000 - $300,000. However, the starting base pay will depend on a number of factors including the candidate’s location, skills, experience, market demands, and internal pay parity.