Loading...
LeadFull-timeGoNode.jsAWSGCPAzureMongoDBRESTAPIMicroservicesAI
About the Role
We are hiring a Staff Engineer for the Workflows Engine to own and evolve the orchestration layer that powers automation at scale. This role involves rebuilding the engine that drives over 80 million workflow enrollments and nearly 1 billion action executions daily, focusing on reliability, performance, and clarity. You will operate as a hands-on individual contributor, designing core engine logic in Go and Node.js while defining contracts, reliability guarantees, and scaling patterns.
Key Responsibilities
- →Rebuild the Workflow Engine from Node.js to Go, creating a modular, high-performance foundation
- →Design core abstractions for orchestration, state, retries, and execution guarantees with clear contracts
- →Optimize for throughput-first execution while maintaining strict ordering within workflow contexts
- →Define interfaces and schemas between Engine, Triggers, and Actions for reliable communication
- →Partner with SRE to instrument metrics and build replay and diagnostics tooling
- →Own the engine’s runtime, including incidents, root cause analysis, and prevention
- →Drive dual-run migration with progressive rollout and auto-rollback safety
- →Set technical benchmarks for clarity, testability, and performance within the team
Requirements
- ✓10+ years of backend engineering experience in distributed systems, job schedulers, or orchestration engines
- ✓Advanced proficiency in Go and Node.js, with experience in low-latency, high-throughput microservices
- ✓Strong understanding of testability and isolation principles for easy-to-test systems
- ✓Production-grade database experience with MongoDB, Firestore, or equivalent and sound data modeling
- ✓Cloud experience with GCP, AWS, or Azure, especially event-driven services like Pub/Sub or SQS
- ✓Proven record of measurable performance wins in latency, throughput, or reliability
- ✓Strong fundamentals in concurrency, idempotency, ordering guarantees, and fault tolerance
- ✓Pragmatic engineering mindset prioritizing simplicity and clarity over unnecessary abstraction
- ✓Applied understanding of design patterns and system architecture principles for scalable modeling
