Lead Software Engineer, Platform Engineering

Klaviyo•Boston, MA

15h•Onsite

About The Position

The mission of the Platform Engineering team is to provide the infrastructure primitives, platforms, tooling, and guidance that let every engineering team at Klaviyo build, ship, and operate with confidence. We own the paved path for how product teams manage their services, applications, and data — spanning core infrastructure, observability, databases, and queuing. As a Lead Software Engineer on the Platform Engineering team, you will set technical direction for our core platform services, ensuring our platform primitives scale with Klaviyo’s growth while remaining simple, reliable, and cost-efficient for the teams that depend on them. Your work will be highly visible and will directly shape how thousands of Klaviyo engineers build and ship products every day.

Requirements

BA or BS degree in Computer Science, related field, or equivalent experience.
10+ years of experience designing, building, and operating large-scale distributed systems, with a track record of technical leadership.
Deep, hands-on experience developing production software in Python and/or Go.
Extensive experience working in cloud-native environments, with hands-on ownership of infrastructure as code (e.g. Terraform) and containerized platforms (e.g. Kubernetes).
Fundamental understanding of Linux and all layers of the networking stack; comfortable administering and debugging production systems.
Experience across a range of platform-related domains such as API gateways & traffic management, observability, asynchronous processing, or database/storage platforms.
Proven ability to independently own the full lifecycle of complex, cross-team platform initiatives — from discovery and technical design through rollout and long-term maintenance.
Strong track record of mentoring engineers and influencing technical direction beyond your immediate team.
Excellent communicator who writes clear technical design docs and RFCs, and keeps stakeholders aligned on progress, risk, and trade-offs.
Comfortable handling complex systems in outage situations and driving failures to root cause and resolution.

Nice To Haves

Familiarity with high-volume data processing and storage systems, including OLTP and OLAP databases.
Experience with large-scale data ecosystems — e.g., ClickHouse, DynamoDB, MySQL, Kafka, Spark, Flink, Iceberg, or Airflow.
Background in internal tooling or paved-path platform design.

Responsibilities

Provide technical leadership across the Platform Engineering org while remaining hands-on with the systems you build.
Build a deep understanding of engineering needs across the organization, guiding the design and development of the right platform primitives (infrastructure, databases, queuing, and more) and balancing the platform's vision with the practical needs of product teams.
Set the technical vision and long-term roadmap for core platform services, translating organizational priorities into a clear, multi-year plan.
Own the design, development, and evolution of foundational platform primitives that product teams rely on to ship quickly and safely.
Lead cross-team initiatives that improve the availability, scalability, latency, and cost-efficiency of Klaviyo’s core platforms, and drive adoption of the resulting paved paths.
Identify systemic gaps and architectural bottlenecks, and lead the design and delivery of solutions.
Leverage technologies such as Python, Go, AWS, and Kubernetes to advance the platform, partnering closely with security, and product engineering leaders.
Champion best practices across the organization, including technical design review, configuration as code, and defensive programming.
Drive cost-optimization efforts across the platform — right-sizing infrastructure, improving resource utilization, and building the tooling and guardrails that make cost-efficiency the default.
Mentor senior and mid-level engineers, raising the bar for technical quality and engineering craft across the Platform Engineering org.
Participate in and help evolve on-call practices, with a focus on solving issues at the root, preventing recurrence, and minimizing alert fatigue.
Experiment with AI in work or personal projects, and dive in and learn fast.
Responsibly explore new AI tools and workflows, finding ways to make work smarter and more efficient.