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Harmonic is a startup discovery engine that maps the entire startup landscape and provides tools for investors, corporate development, and GTM teams to identify breakout companies before they become obvious. The company is growing revenue at 100%+ annually with $30M in funding from Craft, Floodgate, and Sozo Ventures.
The Data team builds the foundational data that powers Harmonic's platform. Every company, person, and investor lookup, and everything the Scout AI agent reasons over, passes through this team first. The team combines internet-scale data collection with information shared directly by founders, partner data from across the innovation ecosystem, and internally derived insights. These sources are incomplete, frequently conflict, and constantly change—the team's job is to reconcile them into a knowledge graph capturing who people are, what companies do, how they connect, and how relationships evolve.
As Harmonic's first Data PM, you will report to the Head of Product and work closely with engineering and data operations. You will own which data gaps to close, which new information to pursue, and how to ensure results are reliable enough for customers to act on. Your decisions will directly shape what customers can discover in Harmonic and what the AI products can reliably do.
Key responsibilities include:
- Owning the data roadmap and its impact by identifying improvements that would most change customer discovery, evaluation, and action capabilities. Use customer research, usage patterns, commercial opportunities, and quality issues to prioritize investments.
- Developing strategy for acquiring data: deciding when to collect, partner, request from users, or derive information internally. Evaluate sources for incremental value, reliability, cost, and freshness.
- Working with engineering to represent the startup ecosystem: reconcile conflicting evidence, resolve company and person identities, model relationships and changes over time, and decide how uncertainty and provenance should inform customer and AI capabilities.
- Defining measurable quality evaluations for accuracy, coverage, and freshness that reflect actual customer tasks. Establish baselines, set launch criteria, and catch regressions.
- Leading new data capability projects from idea to adoption, such as mapping company-investor relationships over time, adding cap table and revenue data, and building AI agents to extract structured information. Scope useful first releases and work with product and GTM to drive adoption.
- Establishing systematic responses to data problems by investigating customer and internal issues, distinguishing isolated errors from systemic failures, and prioritizing fixes that improve the dataset broadly.
REQUIREMENTS:
- 5+ years in product management with recent responsibility for data products. Must be able to explain how your decisions improved customer capabilities alongside underlying data metrics.
- Experience reasoning through messy real-world data, particularly company, people, or financial data. Ability to investigate conflicting information and understand what fixing problems across millions of records requires.
- Technical fluency and hands-on capability. Can work through ingestion, extraction, entity resolution, and classification with engineers. Can independently investigate data to test assumptions and make informed tradeoffs among accuracy, coverage, freshness, latency, and cost.
- Experience building or owning evaluation frameworks with understanding of their limitations. Can explain how representative samples were chosen, ground truth established, and readiness to ship determined.
- Ability to make clear decisions under uncertainty, turn competing requests into focused roadmaps, explain investment priorities, and define useful first releases before all questions are answered.
- Comfort investigating customer workflows and disputed records directly. Follow-through to verify whether shipped solutions actually solved problems.
- Plus: Experience at a private-market data provider or building data-intensive workflows for investors. Early-stage B2B experience is helpful.
