About the Role

The Engineering Platform organization owns the systems that engineers at Compass depend on to build, test, deploy, and operate software. Our mission is to reduce friction across the SDLC, improve engineering effectiveness, and build the foundation for AI-enabled software development.

This role is centered on maximizing engineering velocity and velocity at scale by architecting fast feedback loops, optimizing system efficiency, and building seamless, intuitive workflows that empower developers across the entire software development lifecycle. As a Staff Engineer, you will work across observability, engineering telemetry, developer tooling, software catalog systems, organizational metadata, and AI-assisted workflows to turn these gaps into durable platform capabilities.

Applying AI across the software development lifecycle is a core responsibility of this role. You will build systems that use engineering signals to improve feedback loops, automate operational decision-making, and reduce manual engineering work. This includes capabilities such as intelligent testing frameworks, AI-assisted production troubleshooting, automated change analysis, workflow orchestration, and secure execution environments for AI-generated code integrated throughout the SDLC.

The ideal candidate sees the engineering ecosystem as an interconnected system of services, workflows, operational signals, and human behavior - and knows how to simplify, standardize, and scale it.

Responsibilities

• Define the technical direction for Developer Intelligence systems across the SDLC, including engineering telemetry, observability, and AI-assisted developer tooling.
• Identify systemic engineering inefficiencies by analyzing developer workflows, operational signals, and SDLC feedback loops, then translate those findings into scalable platform capabilities.
• Architect systems that collect, correlate, and operationalize data from CI/CD pipelines, source control, testing infrastructure, production systems, and developer workflows.
• Build AI-assisted capabilities across the SDLC, including code generation, code review, software validation, production troubleshooting, and workflow automation.
• Lead large cross-functional initiatives by aligning teams, driving architectural direction, and decomposing ambiguous problems into scalable execution plans.
• Identify and eliminate systemic bottlenecks impacting engineering velocity, reliability, operational efficiency, and developer experience.
• Mentor engineers, raise technical standards, and contribute to a strong engineering culture through design leadership and high-quality code review practices.

Basic Qualifications

• BS or higher in Computer Science, Software Engineering, or equivalent practical experience.
• 10+ years of professional software development experience, with a proven track record of leading large-scale technical initiatives.
• Strong experience designing, scaling, or operating data pipelines, telemetry systems, large-scale observability infrastructure, or developer metrics frameworks (e.g., tracking DORA or SPACE metrics).
• Hands-on professional experience integrating AI tools & capabilities into software engineering, automated workflows, or system triaging tools.
• Experience designing and operating platform systems on major cloud infrastructures (AWS, GCP, or Azure).
• Experience modernizing legacy systems, consolidating fragmented tooling, and driving platform standardization across multiple engineering teams.
• Strong track record of technical leadership, organizational influence, and cross-functional execution.
• Proficiency in Go and professional experience in one or more of the following: Java, Python, or TypeScript.

Preferred Qualifications

• Deep familiarity with core SDLC infrastructure - including practical experience with or exposure to build systems, local development environments, testing infrastructure, or CI/CD pipelines.
• Experience driving platform adoption or data consolidation across an engineering organization of 1,000+ developers.
• A strong track record of building tools for developers - intimately understanding internal customer pain points and translating feedback into reliable platform capabilities.