Principal Engineer, Enterprise Scalability
Core
Lead platform-wide initiatives to strengthen scalability, performance, reliability, and multi-region readiness for a high-growth, multi-tenant SaaS environment.
Role type
Principal, hands-on IC
Builds
High-growth, multi-tenant SaaS platform
Domain
Software Engineering / Distributed Systems
Deliverable
production ML models
Required skills
Distributed systems architecture, Performance engineering, Capacity planning, Sharding, Partitioning, Caching, Back-pressure, Multi-region design, Large-scale data migrations, AI-assisted engineering tools, System profiling, Incident investigation, Technical strategy
Preferred skills
None stated
Technologies
None stated
Responsibilities
Define enterprise scale-readiness standards by establishing measurable targets for latency, throughput, error rates, and capacity across critical workloads. Develop and maintain a shared scalability scorecard, aligning engineering teams around service-level objectives, error budgets, and performance expectations. Identify and eliminate major system bottlenecks using production data, profiling tools, reproducible benchmarks, and representative workload testing. Partner with service owners to implement performance improvements and validate their impact under realistic production conditions. Lead architectural initiatives involving sharding, partitioning, caching, back-pressure, workload isolation, and multi-region readiness. Design safe and reliable approaches for high-volume data migrations and evolving enterprise infrastructure requirements. Build reusable engineering tools, including lightweight benchmarks, profiling frameworks, and test environments that enable teams to assess performance independently. Introduce scalability reviews and proportionate release-readiness checks that identify risks early and support confident delivery. Lead technical investigations following major incidents, identify systemic causes, and coordinate lasting improvements to reduce recurring reliability issues. Apply AI-assisted tools and automation to engineering workflows, including profiling, workload modeling, synthetic traffic generation, anomaly detection, and guided incident response. Establish appropriate guardrails for AI-enabled engineering practices, evaluate their effectiveness, and measure their operational impact.
Seniority
Principal, hands-on IC
