Product Manager, Data
Listed on 2026-09-02
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IT/Tech
Data Engineering
Title
Product Manager, Data
Salary$120,000 – $150,000
Remote (US) East-Coast Hours About Castellum.AI
Castellum.
AI is an AI layer for compliance infrastructure. We provide intelligence data, AI-driven screening across Sanctions, PEPs, and Adverse Media, and AI Agents to resolve alerts. Our customers have seen results like reducing their false positive rate by 94% via our screening engine and reducing the amount of time analysts spend on alerts by 80% by using our AI Agents. Our customers include a Top Four US Bank, a Top Three Global Payments Company and a Top Five Digital Asset Company, and we're scaling to meet demand.
you’ll own
You’ll own the product execution for our core product: our data platform and pipelines. This covers our sanctions lists, watchlists, PEP data, and regulatory sources that feed everything else we sell, and the ingestion pipeline behind them.
- The list source roadmap. Which lists, watchlists, PEP sources, and regulatory feeds we add next, in what order, and why.
- The definition of data quality. You set the targets for coverage, accuracy, deduplication, and freshness. Engineering builds to them, and you hold the line on them.
- Match quality as a customer outcome. False positive rate and true positive recall are how customers judge our data. You own the requirements behind both.
- New sources, end to end. Scope the source, define the schema and the edge cases, write the spec, lead acceptance testing, and confirm what landed is what customers needed.
- Third-party data partners. Evaluate vendors, scope integrations, and pressure test what they claim their data covers
- The bridge to sales and customers. Prospect calls, product trials, implementation scoping, onboarding, and turning what you hear into the next thing on the roadmap.
- More surface area over time. The screening engine, Adverse Media, or Arbiter's data layer, depending on where you're strongest and where we need you.
- ~4 to 6 hours in ceremonies. Standup, sprint planning, grooming, retro. We keep it light.
- ~5 hours with customers and prospects. Sales calls, technical discovery, troubleshooting, onboarding, feedback.
- The rest is product work. Writing specs precise enough to build from, grooming the backlog, QA'ing data output, enabling the go-to-market team, digging into a match failure in postman yourself before escalating it, and deciding what work comes next.
- You’ll report to the Head of Product, who sets strategic direction while you own execution and delivery.
- Your primary counterpart is the data engineer who owns our scrapers, our Python ETL pipelines, and the implementation of everything you scope.
- The split we run: you define what good means for our data and what order we build it in. Engineering owns how it gets built and how it stays running.
- You’ll partner and collaborate across our go-to-market team to ensure sales, client success, and solutions engineering all have the right context and share back meaningful insights to keep our product feedback loop strong.
- 3 to 5 years of product management experience in B2B SaaS, fintech, compliance tech, or on a data or platform product.
- Are comfortable getting hands‑on in an early‑stage startup environment and figuring things out yourself
- Excel at execution and delivery—you figure out how to work with engineering to ship products on time that add meaningful value.
- Fierce commitment to identifying and delivering the minimum viable product. You treat sequencing as the real decision, not scope.
- You get real value out of AI tools, and you notice when the model makes the wrong tradeoff. You ship work you understand and stand behind.
- Have excellent analytical and problem‑solving skills with ability to make data‑driven decisions.
- Can translate technical concepts for non‑technical stakeholders and vice versa.
- Can communicate quickly, respectfully and concisely.
- Working knowledge of sanctions and watchlist data: OFAC, EU, UN, PEPs, how the lists are structured, and why entity matching on them is hard.
- Experience with data ingestion, ETL, or products built on scraped sources.
- Experience owning a precision…
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