Matillion Launches Maia Foundation on Google BigQuery

Automating the Work Around the Data Warehouse

Maia Foundation automates the construction and governance of the pipelines that feed BigQuery, so engineers review and approve the work rather than author it.

MANCHESTER, England, July 9, 2026 /PRNewswire/ -- Matillion today announced the general availability of Maia Foundation on Google BigQuery. Maia Foundation is the execution layer of Matillion's AI Data Automation platform; from today, BigQuery customers can run pipelines on Maia with transformations pushed down into Google BigQuery. The release brings BigQuery alongside Snowflake, Databricks, and Amazon Redshift as a generally available Maia execution target.

How It Works

Maia is built in three layers. Maia Team authors pipelines: autonomous AI agents that take business intent and the structure of the source data and produce orchestration and transformation logic. Maia Context Engine governs the work: it tracks schemas, lineage, and governance rules as the data environment changes, so automation stays within enterprise policy. Maia Foundation executes the result inside Google BigQuery. When a source column is renamed, Maia detects the change, rebinds downstream transforms against the governance rules in the Context Engine, regenerates lineage, and commits the change for engineer review.

The engineer reviews and approves; the engineer does not author from scratch.

Matillion also supports customers moving from existing Matillion ETL for BigQuery projects onto Maia Foundation. Migration is a guided process that begins with a diagnostic of the existing deployment. Matillion has been the ETL/ELT layer for BigQuery in enterprise production for years — Maia Foundation for BigQuery extends a long-running BigQuery practice onto the autonomous-automation platform, rather than entering BigQuery as a new vendor.

Most AI features landing on BigQuery in 2026 accelerate the engineer writing SQL inside the warehouse. Maia operates on a different part of the engineering surface: the construction and governance of the pipelines that feed and surround the warehouse, which is where most of the delivery time goes. The two are complementary — a team can adopt both, while a team using only the first still has engineers building pipelines by hand.

"Maia is now available for Google BigQuery, bringing the power of AI Data Automation to the Google Cloud ecosystem. We're seeing massive shifts in productivity for enterprise data teams moving manual work to automated for up to 80-90% of work, freeing up teams from the backlog to deliver data outcomes across their business. The platform constructs and governs the pipelines; the engineer reviews and approves. Bringing that to BigQuery is significant. It opens autonomous data engineering to one of the largest data communities in the world, and it's available today."

— Mark Johnston, Chief Marketing Officer, Matillion

Availability

Maia Foundation on Google BigQuery is generally available today. Customers can deploy Maia Foundation as a hybrid agent in their own environment or as a full SaaS service, and onboard via Matillion Hub.

About Maia

Maia is an AI Data Automation platform powered by autonomous AI agents that build, maintain, and evolve data products, thus eliminating manual data work. Maia empowers CDAOs and enterprise data teams to deliver data products at machine scale while maintaining governance, combining autonomous AI agents in Maia Team, grounded in the enterprise knowledge of the Maia Context Engine and executed through the governed data tools of Maia Foundation. Organizations like EDF, St. James's Place, and Nature's Touch use Maia to automate data work at scale, modernize platforms, and accelerate AI roadmaps without expanding headcount.

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