Principal Software Engineer, Enterprise Scalability

## Responsibilities - Define enterprise scalability fitness functions (latency/throughput/error rates) and a scorecard; align teams to SLOs and budgets. - Design/implement sharding and partitioning strategies, caching/back-pressure, multi-region readiness, and high-volume migration paths. - Build lightweight enablement: benchmarks, profiling harnesses, reproducible testbeds; pair with teams to land fixes. - Lead scalability reviews and readiness gates that accelerate—not block—delivery; drive incident deep dives tied to systemic fixes. - Communicate clearly to execs and engineers, tying technical work to business impact and customer outcomes. - Integrate AI into scale and resiliency work—from proactive anomaly detection to synthetic load and guided runbooks—so performance improvements stick and incidents don’t repeat. ## Requirements - Experience: 12+ years scaling multi-tenant SaaS with a reputation for removing major bottlenecks and proving impact with data. - Technical expertise: Performance engineering, capacity planning, sharding/partitioning, caching/back-pressure, multi-region readiness, and high-volume migrations; you turn hotspots into robust patterns. - AI tools & automation: You apply AI to scale work—profiling assistance, workload modeling, synthetic traffic generation, anomaly detection, and runbook copilots—always with explicit guardrails and observability. - Cross-org influence: You align teams through fitness functions, scorecards, and readiness gates that accelerate—not block—delivery; you communicate tradeoffs crisply to execs and engineers. - AI fluency: Curious, adaptable, and proactive in exploring AI that responsibly improves scale outcomes. ## Nice to Have - Scale scorecard: Company-wide fitness functions (latency/throughput/error rates) are adopted and reviewed regularly. - High-impact wins: 2–3 bottlenecks removed with documented, reproducible testbeds; pXX latencies and error rates improve on top enterprise workloads; repeat P0s trend down. - AI-assisted scale engineering: AI-driven anomaly detection reduces alert noise while improving signal; generative load testing and copilot runbooks are used in release/readiness checks for the top critical services; time-to-isolate regressions drops 20–30%. ## Success in 6–12 Months - Company-wide scale scorecard in place; 2–3 high-impact bottlenecks removed; top enterprise workloads show improved pXX latencies and error rates; fewer repeat P0s.