Lead Product Manager, Core Data

We are seeking a highly technical Lead Product Manager to set the vision, strategy, and execution for our Data Ingestion Platform. This is a platform PM role, ideal for someone who operates comfortably at the intersection of deep architectural discussions and long-range product strategy — with a track record of driving measurable outcomes across complex, distributed systems.

You will define the vision and roadmap for how large volumes of data from diverse sources are ingested, transformed, and made reliable within Klaviyo’s core systems. You’ll work closely with engineering, integration teams, and downstream stakeholders to modernize legacy pipelines and build a scalable, unified ingestion framework. At the Lead level, you’re not only executing the roadmap — you’re setting the direction, influencing engineering leadership, and driving org-wide alignment on data platform strategy.

Data is foundational to everything Klaviyo delivers. This role sits at the heart of how that data enters and powers the platform. You’ll shape the architecture that enables reliable, scalable data ingestion for years to come — driving platform health, reducing complexity, and unlocking innovation across the company.

If you’re energized by building structure where it doesn’t yet exist, modernizing complex systems, and operating at the intersection of data architecture and product strategy, this is a high-impact opportunity to define a critical platform capability at scale.

• →Set the Vision: Define the long-term mission, charter, and ownership boundaries for a newly formed team — establishing clarity where ambiguity currently exists.
• →Own the Roadmap: Develop and maintain a coherent, high-impact roadmap that balances foundational investments with active initiatives; refine prioritization and sequencing across concurrent projects.
• →Drive Ingestion Strategy: Define the strategy for ingesting large volumes of data from numerous sources; think holistically about data movement, transformation, and reliability at scale.
• →Partner with Engineering: Partner with engineering to design ingestion systems that are scalable, resilient, and maintainable; navigate tradeoffs across performance, latency, cost, and reliability.
• →Accelerate with AI: Institutionalize AI and automation as force multipliers — identify opportunities to reduce toil, improve pipeline observability, and accelerate the roadmap through intelligent tooling.
• →Deliver Measurable Outcomes: Establish instrumentation and success metrics (e.g., ingestion reliability, pipeline latency, data freshness, change failure rate) to validate impact and adjust priorities based on outcomes.
• →Champion Quality & Reliability: Drive improvements in observability, monitoring, and operational excellence for ingestion systems; partner with SRE and platform teams to ensure reliability is built into every component.
• →Scale for the Future: Identify and drive architectural simplifications that improve platform health over time; guide tooling choices that will keep the data ingestion platform sustainable and scalable.
• →Build for Collaboration: Create transparency and open feedback loops with engineers, engineering managers, and senior leadership to ensure alignment and adoption of platform direction.

• Strategic Builder: Proven experience as a Lead or Senior Platform Product Manager in data infrastructure, distributed systems, or developer platform environments — with a track record of driving velocity and reliability at scale.
• Data Infrastructure Depth: Deep understanding of data architecture and large-scale systems; experience with event-driven systems, high-volume ingestion pipelines, or complex data movement patterns.
• Technical Fluency: Comfort engaging in highly technical discussions around pipelines, infrastructure, and system evolution; ability to operate at both strategic and highly detailed technical levels.
• AI-Forward Mindset: Understanding of the transformative potential of AI in platform engineering; ability to separate hype from practical value and drive adoption of AI-forward practices.
• Metrics-Oriented: Fluency in measuring success via engineering productivity, quality, and reliability metrics; data-driven approach to prioritization and investment decisions.
• Influential Collaborator: Proven ability to define structure and clarity in ambiguous, newly forming teams; skilled at stakeholder management across multiple technical organizations.
• Bias for Action: Thrives in fast-moving environments; balances short-term wins with long-term strategy and biases toward automation, simplicity, and self- service solutions.

• Engineering teams feel they can deliver data-dependent features faster, safer, and with higher confidence than ever before.
• The data ingestion platform is recognized internally as reliable, observable, and a competitive advantage for product teams building on top of Klaviyo’s core data objects.
• Pipeline reliability improves, incidents decrease, and data freshness and quality metrics trend in the right direction — even as ingestion volume grows.
• The roadmap and platform strategy are clear, measurable, and directly tied to Klaviyo’s ability to deliver customer value quickly and reliably.
• AI and automation reduce operational toil, with measurable gains in engineering productivity across ingestion workflows.