Databento, a Salt Lake City-based market data platform, closed a $97 million Series B led by New Enterprise Associates, with the round drawing more than $300 million in total investor demand. The financing included participation from strategic and existing investors, including DRW Venture Capital, Redpoint Ventures, and Tribe Capital, among others.

The raise puts fresh capital behind a company built to solve a problem every trading desk knows firsthand. “Every firm in finance depends on market data, but accessing it has been one of the industry’s longest-running bottlenecks. For decades, it’s meant months of procurement and integration work just to get to the starting line,” Christina Qi, co-founder and CEO of Databento, says. “Databento was built by practitioners across quant trading and high-frequency market making to eliminate this overhead, making institutional-grade market data available on demand.”

A three-year climb to fintech’s fast lane

Three years after launch, Databento has emerged as one of the fastest-growing companies in fintech, serving some of the world’s most demanding financial firms whose daily trading activity is measured in the trillions of dollars.

The company reached profitability with just 24 employees, more than doubled its active API users in recent months, and grew revenue 6.65 times year over year while maintaining over 97% enterprise logo retention since inception.

It’s Databento’s second major raise in roughly a year. In October 2024, the company closed $10 million in additional funding, increasing its total Series A round to $30 million, capping a year that saw a 985% surge in revenue and over 7,000 new customers. The jump from that raise to a $97 million Series B, oversubscribed more than threefold, signals how quickly institutional demand for the platform has scaled.

NEA partners join the board

NEA’s partners, Rick Yang and Danielle Lay, will join Databento’s board, with Yang serving as a director and Lay as a board observer.

“It’s rare to encounter a company where the product, the traction, and the founding team all point in the same direction with this much clarity,” Rick Yang, Partner and Head of Technology at NEA, says.

“Databento has built the modern foundation this industry has needed for a long time, and the fact that it’s earned trust everywhere from students running their first strategies to the world’s most sophisticated trading desks isn’t by design—it’s what best-in-class looks like in a category this fundamental.”

The company is headquartered at 26 S. Rio Grande St. in Salt Lake City, placing it squarely inside Utah’s expanding fintech corridor. Qi, an MIT graduate who previously ran the high-frequency trading fund Domeyard, has described growing up south of Salt Lake City before building her career in quantitative finance. That local footprint arrives as Utah’s broader fintech sector matures: a Kem C. Gardner Policy Institute and University of Utah Fintech Center report found the state’s fintech industry supported nearly 8,000 direct jobs and more than $1 billion in annual wages in 2023, with average salaries more than double the statewide figure.

Where the money goes

The new capital will broaden Databento’s coverage across asset classes and geographies while scaling the infrastructure that colocates its servers at exchanges to capture data directly from the source.

The company is set to expand to more than 20 data centers worldwide following this round and has secured an additional 100-plus petabytes of storage capacity, more than doubling its previous footprint.

NEA brings scale to match Databento’s ambitions. Founded in 1977, NEA has more than $28 billion in assets under management as of June 30, 2025, and has backed more than 280 portfolio company IPOs and more than 510 mergers and acquisitions. For a Utah-born data company now serving trillion-dollar trading desks, that backing signals the next phase isn’t just about growth at home, but about becoming infrastructure the global financial industry runs on.


This article was adapted from a press release by an automated tool and reviewed and edited by an editor for accuracy before publication.