Senior Product Manager, User Data Platform

Iterable Iterable · Enterprise · United States · Remote · Product

Product Manager for Iterable's User Data Platform, focusing on the end-to-end lifecycle of user and event data, from ingestion to intelligent storage and access. The role will shape systems powering messaging and AI features, ensuring data is fast, reliable, and ready for real-time decisioning and agentic journeys.

What you'd actually do

  1. Own the end-to-end user data platform charter: define how user and event data moves from APIs and pipelines into storage, through schema and lifecycle management, and out through query surfaces used by sends, journeys, and AI features—staying focused on core user data infrastructure, not analytics UI or external integrations.
  2. Make ingestion and identity reliable at scale: evolve ingestion APIs and pipelines with clear SLOs, prioritization, and fair-usage limits, and drive a clear identity model so we can safely move users from anonymous to known without duplicates or data loss.
  3. Modernize storage, schemas, and guardrails: lead ES8 and user-store roadmaps, retention and cost work, and turn schema management into a first-class product (field removal, usage heatmaps, structural guardrails) so customers can keep only the data that matters.
  4. Enable Nova and product teams with better data: partner with AI/ML and application teams to define what “AI-ready” means, evolve a centralized query layer with guardrails, and own platform metrics (SLOs, incidents, ES cost, schema health, query coverage) that show the impact of your roadmap.

Skills

Required

  • 5+ years of product management experience in B2B SaaS, focused on backend, platform, or data infrastructure products.
  • Hands-on experience with user and event data systems—for example: ingestion pipelines, identity/alias models, event stores, or large search/document databases (e.g., Elasticsearch).
  • Strong instincts around APIs and schemas: you’re comfortable reading JSON payloads, thinking through nesting vs. flattening, and managing breaking changes over time.
  • Familiarity with distributed systems and data infra (Kafka/Pulsar, Redis, relational or NoSQL stores, or search engines) and the tradeoffs around performance, reliability, and cost.
  • Experience collaborating with AI/ML or data science teams on the data side—feature availability, data quality, semantics—enough to know what really blocks good models and agent behavior.
  • Strong analytical skills (including SQL) and a habit of using data to drive prioritization and validate impact.
  • Clear, direct communication

What the JD emphasized

  • AI-native system
  • agentic journeys
  • AI features
  • AI-ready

Other signals

  • AI-native system
  • agentic journeys
  • intelligence-ready data systems
  • Nova AI experiences
  • AI-ready data