About The Position

Klaviyo is seeking a senior technical leader to own its approach to enterprise scalability, performance, reliability, and multi-region readiness. This is a senior individual-contributor role with no direct reports, reporting to a VP of Engineering. The Principal Engineer will lead through technical expertise, hands-on engineering, and influence, partnering across teams to improve the platform for large customers and demanding workloads. The role spans system design, profiling, implementation, and production rollout. It includes identifying and removing major bottlenecks, driving platform-wide architectural changes, and establishing measurable readiness standards for large tenants. The engineer will help teams adopt practical tools and repeatable patterns so performance and reliability become part of everyday engineering, while communicating technical priorities and tradeoffs to engineering teams and executives. Success includes adoption of a company-wide scale scorecard; removal of high-impact bottlenecks with reproducible evidence of sustained improvement; measurable improvements in tail latency, throughput, and error rates for the largest customer workloads; actionable multi-region and migration plans; earlier detection of regressions; fewer recurring P0 incidents; and demonstrated operational value from AI-assisted tools.

Requirements

  • 12 or more years of relevant engineering experience and a track record of removing significant bottlenecks with measurable production impact.
  • Deep experience scaling multi-tenant SaaS.
  • Hands-on expertise in performance engineering, capacity planning, sharding and partitioning, caching, back-pressure, tenant isolation, multi-region architectures, and large-scale migrations.
  • Ability to investigate unfamiliar systems, test hypotheses, write production code, and work with service owners to roll out changes safely.
  • Sound technical judgment in evaluating evidence and architectural tradeoffs, including when to address an immediate constraint versus make a broader platform investment.
  • Ability to align teams around shared outcomes, clarify ownership, and establish practical standards.
  • Clear communication of complex technical decisions and their business implications to engineering and executive audiences.
  • Practical experience applying AI tools or automation to engineering work, with the ability to evaluate usefulness, limitations, and operational risks.

Responsibilities

  • Define scale-readiness measures, including targets for latency, throughput, error rates, and capacity; establish a shared scorecard and align teams on service-level objectives, error budgets, and performance expectations.
  • Use production data, profiling, and reproducible benchmarks to identify important constraints, partner with service owners on fixes, and verify results under representative workloads.
  • Design and implement architectural strategies such as sharding, partitioning, caching, back-pressure, workload isolation, multi-region readiness, and safe high-volume data migrations.
  • Create lightweight benchmarks, profiling harnesses, and test environments that teams can use independently, and turn successful fixes into reusable patterns.
  • Lead scalability reviews and establish proportionate release gates that surface risks early and help teams ship confidently.
  • Lead technical incident deep dives, identify systemic causes, and work with teams to deliver and validate fixes that reduce recurring incidents.
  • Apply AI-assisted profiling, workload modeling, synthetic traffic, anomaly detection, and guided runbooks where they improve outcomes; establish guardrails and evaluate operational impact.
  • Communicate technical priorities, tradeoffs, and results to engineers and executives, connecting engineering investments to customer experience, enterprise growth, and operating efficiency.
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