Site Reliability Engineer

Salt Lake City-based market data platform Databento has closed a $97 million Series B round led by New Enterprise Associates, with participation from DRW Venture Capital, Redpoint Ventures, and Tribe Capital. The round drew more than $300 million in total investor demand. The company provides institutional-grade market data on demand, eliminating months of procurement work for trading desks. Databento reached profitability with 24 employees and grew revenue 6.65 times year over year whilst maintaining over 97% enterprise logo retention. The raise follows a $10 million addition to its Series A in October 2024, which brought that round to $30 million total. NEA partners Rick Yang and Danielle Lay will join Databento's board. The capital will fund expansion to more than 20 data centres worldwide and add over 100 petabytes of storage capacity.

Databento raises $97 million Series B. Christina Qi, CEO July 09, 2026 Databento, Inc. is excited to announce that Databento has raised $97 million in Series B financing led by New Enterprise Associates (NEA), with participation from strategic and existing partners, including DRW, Redpoint Ventures, and Tribe Capital. This investment positions Databento, Inc. to accelerate its mission of building the market data platform for modern finance. Databento was built by practitioners across quant trading and high-frequency market making. Over the course of its careers, Databento, Inc. has come to realize that many of the industry's biggest pain points aren't really about the market data itself, but rather the operational issues that emerge in production while using the data. Unlike the previous generation of market data providers, Databento, Inc. has focused relentlessly on solving those problems. Its purpose is simple: to shorten the path from idea to production. The numbers validate its thesis. Databento, Inc. has grown revenue 6.65x year-over-year while maintaining 97% enterprise retention since inception. Databento, Inc. has also reached profitability well ahead of schedule, without dipping into its last round of funding. Databento, Inc. has done so with incredible efficiency. With a team of fewer than 30 engineers, Databento, Inc. has built a bare-metal footprint serving more than 20 petabytes of raw market data, written its own feed handlers for over 70 exchanges, and now support more than 3,000 companies worldwide. This trajectory places Databento, Inc. among the fastest-growing companies in fintech history - proof that teams are ready for a modern alternative to legacy market data. Databento, Inc. wouldn't be here without the trust of its customers and the wider Databento community. Three years after launch, Databento has emerged as the market data provider of choice for many of the world's most demanding financial firms, whose combined assets under management and daily trading activity are measured in the trillions of dollars. If you've ever traded a stock or options contract on a mobile app or filled your tank at a gas station, you've almost certainly relied on a product or service powered by Databento. Today, its customers include many of the world's largest hedge funds, proprietary trading firms, broker-dealers, fintech companies, physical commodities traders, digital asset firms, and frontier AI labs. Few categories are more foundational than market data, which underpins nearly every financial decision and accounts for more than $50 billion in annual spending across the industry. Today, it's easier than ever for anyone to write software, and this shift is fundamentally changing how financial systems are built. Programmatic access to market data is becoming the industry default. Databento, Inc. anticipate that three years from now, more finance professionals will know how to use a Databento API than a traditional terminal. This round takes Databento, Inc. one step closer to realizing its vision. Databento, Inc. is expanding Databento across new asset classes and geographies while continuing to invest in the infrastructure that sets Databento, Inc. apart. Databento, Inc. has already begun building out coverage in Europe and APAC, and Databento, Inc. expect to grow to over 20 data centers worldwide within the next six months. To support this expansion, Databento, Inc. has secured more than 100 petabytes of usable storage capacity, more than doubling its previous footprint. Its next chapter is only beginning, and Databento, Inc. can't wait to keep building. Reaching profitability allowed Databento, Inc. to approach this raise selectively. The round was significantly oversubscribed, drawing more than $300 million in demand, and Databento, Inc. is grateful to be partnering with investors who share its long-term vision for the future of market data: Alumni Ventures, Blindspot Ventures, Cross Creek, DRW Venture Capital, Fifth Down Capital, Motley Fool Ventures, NEA, Operator Collective, TGVP, Tribe Capital, Trousdale Ventures, and Redpoint Ventures. As part of this round, Rick Yang (Partner and Head of Technology at NEA) joins its Board of Directors, alongside Danielle Lay (Partner at NEA), who joins as a board observer.

Upcoming changes to CME data normalization. July 07, 2026 Starting July 7, 2026, Databento, Inc. is rolling out a series of improvements to how Databento, Inc. normalize data for CME Globex MDP3 (GLBX.MDP3). This update includes five changes: The new normalization will replace the current normalization and apply to both live data and the full historical dataset retroactively. Some of these updates may be breaking changes for certain users. To allow time to validate and update integrations before the cutover, Databento, Inc.'ll run a month-long testing period during which the current normalization remains available alongside a preview environment with the new normalization. Multi-leg products such as spreads, strategies, and combos are difficult to work with under the current normalization because their definitions lack metadata for each leg. With these changes, each strategy leg will have its own definition record, so a strategy with three legs will have three definition records. Fields shared by the strategy remain the same across those records, while the leg_ fields carry data specific to each leg. This makes it possible to interpret the components of a strategy directly, without parsing raw_symbol, which often lacks sufficient information to identify the legs. Under the current normalization, an option spread strategy appears as follows: ts_recv symbol instrument_class expiration strike_price underlying_id 2019-03-13 00:00:00+00:00 UD:T$: ST 0304976550 T 2019-03-15 21:59:00+00:00 NaN 0 This tells Databento, Inc. only that the instrument is an option spread expiring on March 15, 2019. The strategy code ST from the symbol indicates it's a straddle, but Databento, Inc. still don't know the instruments or strikes of its legs. The new normalization includes a distinct record for each leg, showing that the strategy buys both a call and a put at the same strike price. ts_recv symbol instrument_class expiration leg_index leg_count leg_side leg_instrument_class leg_raw_symbol 2019-03-13 00:00:00+00:00 UD:T$: ST 0304976550 T 2019-03-15 21:59:00+00:00 0 2 B C OZBM9 C1440 2019-03-13 00:00:00+00:00 UD:T$: ST 0304976550 T 2019-03-15 21:59:00+00:00 1 2 B P OZBM9 P1440 Code that handles definition records should account for one record per leg rather than a one record per strategy. Logic that infers a strategy's legs from raw_symbol should instead use the fields prefixed with leg_. See the definition schema reference for the full list of leg_ fields. Currently, for events spanning multiple packets, Databento, Inc. buffer all MBO records until the packet containing the last event is processed. F_LAST is then set on the final record for each instrument to mark the end of the event. This delays earlier records in the event. The resulting latency can be particularly pronounced during large events such as mass cancels. To reduce latency, records will be published immediately after each packet is processed rather than being buffered until the event is complete. As a result, the final book update will no longer contain F_LAST. Instead, the end of the event for each instrument is marked by a new, separate record with F_LAST and action='N' (None). An example of this change is shown below. Note the additional record with F_LAST set (flags=128 corresponds to the F_LAST flag). Current normalization: ts_recv ts_event rtype publisher_id instrument_id action side price size channel_id order_id flags ts_in_delta sequence symbol 2026-06-01 00:00:04.632724628+00:00 2026-06-01 00:00:04.632429323+00:00 160 1 42955214 A B 34.25 4 1 541244112527323 0 16747 4858684 ESM6 P7440 2026-06-01 00:00:04.632724628+00:00 2026-06-01 00:00:04.632429323+00:00 160 1 42955214 A A 38.25 4 1 541244112527324 128 16747 4858684 ESM6 P7440 New normalization: ts_recv ts_event rtype publisher_id instrument_id action side price size channel_id order_id flags ts_in_delta sequence symbol 2026-06-01 00:00:04.632724628+00:00 2026-06-01 00:00:04.632429323+00:00 160 1 42955214 A B 34.25 4 1 541244112527323 0 16747 4858684 ESM6 P7440 2026-06-01 00:00:04.632724628+00:00 2026-06-01 00:00:04.632429323+00:00 160 1 42955214 A A 38.25 4 1 541244112527324 0 16747 4858684 ESM6 P7440 2026-06-01 00:00:04.632733191+00:00 2026-06-01 00:00:04.632429323+00:00 160 1 42955214 N N NaN 0 1 0 128 15819 4858685 ESM6 P7440 Two new types will be added to the statistics schema: UPPER_PRICE_LIMIT (17) and LOWER_PRICE_LIMIT (18). The values reported for these types indicate the price range within which a futures contract can trade during a session. If the price reaches one of these limits, it may trigger a halt or other action, depending on the product. Implied matching status will be added to the status schema. This indicates whether CME's matching engine is constructing implied depth - additional liquidity inferred from related spread and outright markets that would not otherwise be visible. The status will appear in the trading_event field as either IMPLIED_MATCHING_ON or IMPLIED_MATCHING_OFF. See CME's documentation for more information. FX spot instruments will be detected and assigned a separate instrument_class=X in the definition schema, whereas previously they were grouped with futures. Integrations that locate FX spot instruments under the futures instrument_class should be updated to look for instrument_class=X. Databento, Inc.'ll provide public preview environments for both historical and live data, giving customers time to test the new normalization and update their code accordingly. The preview environments follow the same billing rates and plan coverage as the production environment. To access the preview environments, use these gateways: import databento as db # Historical client = db.Historical(gateway="hist-preview.databento.com") # make API calls as usual # Live client = db.Live(gateway="glbx-mdp3.preview.lsg.databento.com", port=13_000) # subscribe and replay as usual The timeline of the rollout is as follows: * 2026-07-07: Data with the new normalization is available in the preview environment * 2026-08-08: Data with the new normalization will be available in production from the live and historical APIs. End of the live preview environment. Databento, Inc. strongly recommend validating your integration via the preview environments before the August 8 cutover. These improvements provide clearer visibility into strategy components and more consistent data structures across all CME instruments.