Jordan Tigani

Jordan Tigani is an American software engineer and entrepreneur who spent over a decade at Google as a founding engineer on BigQuery, the company's serverless cloud data warehouse. His work at Google put him at the centre of building hyperscale data infrastructure capable of processing petabytes of data for customers ranging from Spotify to Twitter and major financial institutions, helping define what a modern managed data warehouse looks like.

After leaving Google, Tigani served as Chief Product Officer at SingleStore, an experience that sharpened a contrarian view he had been forming: that most organisations don't actually have "big data" problems, and that the industry's infrastructure had become oversized for the workloads most companies actually run. That thesis, later popularised in his widely read essay and talks titled "Big Data is Dead", became the founding premise of his next venture.

In 2022, Tigani co-founded MotherDuck, a company built around DuckDB, the fast, embeddable, open-source analytical database. Rather than scaling out across large distributed clusters, MotherDuck's approach combines local, in-process query execution with a lightweight cloud layer for collaboration and scale-when-needed, aiming to make most analytical workloads dramatically cheaper and faster to run.

MotherDuck assembled its core team in Seattle in mid-2022 and launched publicly from stealth in November 2022, backed by more than $47.5 million in seed and Series A funding led by Redpoint Ventures and Andreessen Horowitz. As CEO, Tigani has continued to argue publicly that the data industry over-invested in distributed "big data" systems and that a new generation of tools built on fast single-node engines like DuckDB will reset expectations for cost and simplicity.

Tigani's journey from building one of the industry's most successful large-scale cloud data warehouses to championing a leaner, DuckDB-based alternative has made him one of the most closely watched and provocative voices in contemporary data infrastructure, influencing how vendors and practitioners think about the true scale of most real-world data problems.