Klaviyo Principal Engineer at Klaviyo focused on enterprise scalability, performance, reliability, and multi-region readiness. Leads through technical expertise and hands-on engineering to solve bottlenecks and drive architectural improvements.
Responsibilities
We are seeking a senior technical leader to own Klaviyo’s approach to enterprise scalability, performance, reliability, and multi-region readiness. Reporting to a VP of Engineering, you will be a senior individual contributor with no direct reports. You will lead through technical expertise, hands-on engineering, and influence, partnering across teams to deliver improvements that matter to our customers.
This role suits an experienced engineer who can move between deep technical investigation and clear executive communication, make and defend architectural decisions, and build alignment across teams.
Reports to: VP of Engineering
Direct reports: None
Define what scale readiness means. Establish measurable targets for latency, throughput, error rates, and capacity. Build a shared scorecard and align teams on service-level objectives, error budgets, and performance expectations for critical enterprise workloads.
Remove the bottlenecks that matter most. Use production data, profiling, and reproducible benchmarks to identify constraints. Partner with service owners to implement fixes and verify improvements under representative workloads.
Drive architectural change. Design and implement sharding, partitioning, caching, back-pressure, and workload isolation strategies. Advance multi-region readiness and build safe paths for high-volume data migrations.
Make scale engineering repeatable. Create lightweight benchmarks, profiling harnesses, and test environments that teams can use independently. Pair with engineers to turn successful fixes into reusable patterns.
Build readiness into delivery. Lead scalability reviews and establish clear, proportionate release gates that surface risks early and help teams ship confidently.
Turn incidents into lasting improvements. Lead technical deep dives, identify systemic causes, and work with teams to deliver and validate fixes that reduce recurring incidents.
Apply AI where it improves outcomes. Use AI-assisted profiling, workload modeling, synthetic traffic, anomaly detection, and guided runbooks to improve investigation and response. Establish explicit guardrails, evaluate results, and measure operational impact.
Connect engineering decisions to customer needs. Communicate priorities, tradeoffs, and results clearly to engineers and executives, tying technical investments to customer experience, enterprise growth, and operating efficiency.
