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Staff Engineer – Workflows Engine
Location
India
Posted
167 days ago
Salary
0
Seniority
Lead
Job Description
Staff Engineer – Workflows Engine
HighLevel
• Re-architecture: Rebuild the Workflow Engine from Node.js to Go, creating a modular, high-performance foundation for billions of executions • Core abstractions: Design orchestration, state, retries, and execution guarantees with clear contracts and isolation boundaries • Performance model: Optimise for throughput-first execution while maintaining strict ordering within each workflow execution context • APIs & contracts: Define interfaces and schemas between Engine, Triggers, and Actions. Ensure consistent, reliable, and versioned communication • Reliability & observability: Partner with SRE to instrument metrics (latency, throughput, failure rate) and build replay and diagnostics tooling • Operational ownership: Own the engine’s runtime — incidents, RCA, and prevention. Deliver measurable reliability improvements (<1% failures/day) • Migration & rollout: Drive dual-run migration with progressive rollout and auto-rollback safety • Engineering culture: Set the technical benchmark for clarity, testability, and performance within Workflows and beyond
Job Requirements
- 10+ years of backend engineering experience with deep hands-on work in distributed systems, job schedulers, or orchestration engines
- Advanced proficiency in Go (preferred) and Node.js, with experience writing low-latency, high-throughput microservices
- Strong understanding of testability and isolation principles; you design systems that are easy to test, reason about, and extend
- Production-grade database experience (MongoDB, Firestore, or equivalent) with sound data modelling
- Cloud experience (GCP, AWS, or Azure), especially event-driven services like Pub/Sub, SQS, or Cloud Tasks
- Proven record of measurable performance wins — reduced p95/p99 latency, improved throughput, or increased reliability
- Strong fundamentals in concurrency, idempotency, ordering guarantees, and fault tolerance
- Pragmatic engineering mindset — simplicity and clarity over abstraction for abstraction’s sake
- Strong applied understanding of design patterns and system architecture principles — able to model orchestration, state, and retries using proven, scalable patterns
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