Marketing Intelligence Lead

About AngelList Private markets are the new public markets. The most important companies of this era are private, and almost no one can own them. What's missing is the financial system public markets spent a century building: exchanges, custodians, brokers, and ways for ordinary people to buy in. Private markets, now $28T, never had the equivalent. AngelList is building it. The last decade of structural innovation in venture happened here: syndicates, SPVs at scale, Rolling Funds, scout programs. Today we have $200B+ in assets and 30,000+ startups funded on our platform. The rest of private markets is next. Funds is the software and services behind private funds, Nova is the banking network, Meridian is the wealth platform for accredited investors, and USVC is the brand behind a registered fund that lets anyone invest in venture. Working here means an unusual amount of agency on a problem that matters. Titles are closer to suggestions, and there are about 200 of us building what private markets never had. Come build it with us.

About the Role: This is a chance to rebuild marketing ops from first principles, designed around what AI makes possible. We need a clearer perspective on where to invest marketing dollars and why. This role exists to build that clarity. You'll own the marketing tech stack, full-funnel reporting, and market intelligence insights that guide where dollars go. You'll be the natural partner to marketers running experiments: the source of truth who provides data backing and builds the instrumentation (dashboards, signals, measurement systems) that tells us what's working and what we're missing. This role requires wide altitude coverage: executive-level strategy partnership and hands-on technical execution. You'll sit at the intersection of marketing, sales, and executive leadership. You’ll frame what the numbers mean and what we should do about them. Hands-on execution is required because the person making strategic recommendations is the same person who understands exactly how the data is built, where it breaks, and what it can't tell you. The right candidate is comfortable moving between these modes daily.