Staff Full Stack Engineer (Hybrid)

Job description

Job responsibilities

• Architect and build across distributed systems, APIs, integrations, and infra shipping your own code with performance, fault-tolerance, and horizontal scale as first-class design concerns.
• Own the platform's non-functional bar by setting explicit targets for scalability, reliability, availability, latency, performance, security, and observability, and holding the full stack to them.
• Partner with team members on multiple projects like MHEV, Drone and 3DCC to elevate and deliver solutions in technical design and take new requirements to production at high SDLC standards (testable, observable,…
• Decompose overloaded APIs into well-bounded services and introduce data-access abstractions that control DB connections and improve scalability.
• Retire reliability and compliance debt (e.g., PostgreSQL upgrades), stand up production observability metrics and alerts, and reduce delivery friction with CI/CD and AI-assisted delivery.
• Serve as the US-based single point of contact for fullstack work, partnering with Customer Success to turn customer requirements into clear, well-scoped engineering.
• Mentor senior engineers and raise the bar on engineering practices across backend and infrastructure.

Job requirements

• 8+ years building and operating production software, with a track record of taking large, stateful systems to enterprise-grade scale and reliability
• Deep hands-on experience with Python, Go and Node.js, PostgreSQL at production scale, and Kubernetes on Azure, from architecture through independent production debugging
• Depth in APIs and integrations across internal service-to-service and external partner integrations: REST, protobuf, messaging, and OAuth, plus secure integration protocols across HTTP, SFTP, email, and IPSec, designed…
• Deep grasp of non-functional design: scalability, availability and failure modes, latency budgets, caching, and security as first-class concerns, not afterthoughts
• Solid, hands-on CI/CD: building and owning automated build, test, and deployment pipelines that make delivery fast and safe
• Production-grade observability: instrumenting logs, metrics, and traces, and using them (with SLIs/SLOs and incident response) to keep systems healthy
• Practical AI-assisted engineering, using tools such as Claude across the SDLC with sensible guardrails
• Establishing best practices that raise team's engineering standard