About this role
At Klaviyo, we value the unique backgrounds, experiences and perspectives each Klaviyo (we call ourselves Klaviyos) brings to our workplace each and every day. We believe everyone deserves a fair shot at success and appreciate the experiences each person brings beyond the traditional job requirements. If you’re a close but not exact match with the description, we hope you’ll still consider applying. Want to learn more about life at Klaviyo? Visit klaviyo.com/careers to see how we empower creators to own their own destiny.
Senior Lead Software Engineer - Data Platform
Team Overview
The Data Platform organization owns the foundational data systems that power analytics, AI/ML, and product use cases across Klaviyo. This Senior Lead role is anchored in the Data Lake domain, while working broadly across Data Platform to support and align with adjacent teams.
In practice, this role helps shape and connect the technical direction across lakehouse storage and compute, data onboarding and orchestration, modeled data layers, and the operational automation that keeps the platform reliable, observable, and usable at scale. The role requires strong judgment in connecting work across teams rather than optimizing only within a single technical boundary.
The scope is not just to improve one system in isolation, but to make the broader data platform more trustworthy, scalable, and easier to operate. That includes aligning roadmaps across teams, supporting high-impact and complex cross-team projects, and helping represent Data Platform clearly to partner teams and stakeholders.
How You’ll Make an Impact
As a Senior Lead Software Engineer in Data Platform, with primary focus on Data Lake, you will:
• Independently own and drive high-impact technical objectives that span multiple Data Platform teams, especially Data Lake while supporting work that intersects with Data Automation and Data Warehouse.
• Help align technical roadmaps across Data Platform so teams are making coherent investments against shared priorities, dependencies, and long-term platform direction.
• Lead large, complex, cross-team initiatives from discovery through rollout and long-term ownership, especially where success depends on coordination across organizational and technical boundaries.
• Make high-judgment architectural decisions across core platform systems including lakehouse storage and compute, orchestration, modeled data layers, and operational automation, and create reference patterns that other teams can reuse.
• Establish paved paths, standards, and shared abstractions that reduce repetitive manual work and make onboarding, operating, and evolving data systems dramatically faster and more reliable across the platform.
• Partner closely with engineering, product, analytics, AI/ML, and governance stakeholders, and represent Data Platform clearly in cross-functional discussions that require both technical depth and strong external communication.