Ready to see what Maia Foundation means for your BigQuery environment?
Enjoy the freedom to do more with Maia on your side.
Now available:
Maia for
Google BigQuery
Your environment is supported. Your existing pipelines keep running. When you choose to move, migration is a guided process: we start with a diagnostic of your project, then convert it with our engineering team working alongside yours.
Book your guided Matillion ETL to Maia migration analysis
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SEE IT IN ACTION
How Maia Foundation works on BigQuery
Maia operates as a unified platform built on three integrated layers, all running pushdown into BigQuery.
The pipeline maintenance tax, eliminated
Moving from Matillion ETL to Maia Foundation isn't a rip-and-replace. It's a platform upgrade on top of the execution model you already use.
Maia automates the work that's been consuming your engineering bandwidth: the schema drift, the governance gaps, the review cycles, and replaces it with an operating model where they inspect and approve, rather than build and fix.
- Before Maia FoundationAfter Maia Foundation
Manual schema drift investigation and repair
Schema drift detected, rebound, and committed for engineer review automatically- Pipeline governance added after the fact, if at allLineage and governance emitted from the first pipeline run
- Sprint capacity consumed by maintenance, not deliveryEngineers review and approve changes rather than authoring from scratch
- Migration from METL means a rebuild with no clear end dateGuided migration from METL-BQ: diagnostic first, then conversion with engineering support
- Lineage documented when someone has time to do itA live model of your schemas, lineage, and governance rules, maintained continuously
Why Maia Foundation works for your BigQuery practice
Whether you're migrating it, extending it, or both, Maia replaces the guesswork with a process that actually ships.
1
Data engineers
Engineers shift from building pipelines to reviewing them. Maia Team's agents construct and maintain the work. When an upstream source changes, Maia detects it, rebinds the affected transforms, and commits the update through Git. Your engineer reviews. Authorship is off the plate.
2
Data architects
Governed pipelines from day one, not retrofitted later. The Context Engine maintains a live model of your schemas, lineage, and governance rules across your entire BigQuery estate, keeping automation inside the lines your governance team has drawn.
3
Data leaders
Platform consolidation without a multi-year programme. Maia Foundation joins Snowflake, Databricks, Redshift, and Azure Synapse as a supported execution target. One platform, one operating model, one operational view across your full stack.
The results speak for themselves
Outcomes from Maia customers running enterprise data migrations. BigQuery-specific customer references in progress, these reflect the broader Maia platform.
2/3 reduction
in legacy ETL migration effort per pipeline
Days to hours
turnaround on individual pipeline conversions
Engineering capacity
freed and reinvested into data product delivery
Built for environments where getting it wrong isn't an option
Migrating from Matillion ETL in financial services or any regulated environment isn't just a technology project. Every pipeline carries business logic that needs to be traceable, documented, and auditable before it goes anywhere near production. Maia is built for that.
Maia is running enterprise migrations in production environments. Enterprise customers get a dedicated support team engaged from day one of the migration, not day one of the contract renewal
Governance without guesswork
Maia enforces role-based access controls on every AI action, runs Git-based workflows your compliance team can sign off on, and logs every pipeline change from the first migration run to the last production deploy.
Security your InfoSec team can approve
SOC 2, HIPAA, and GDPR compliance standards, verifiable through documentation for your InfoSec team. Data encrypted at rest and in transit. AI operations governed and logged, processed under strict access controls.
Featured Customer Story
From days to hours: How St. James’s Place cut ETL migration effort by two-thirds with Maia
Platform modernization stalls when legacy ETL migrations consume the engineering capacity needed to drive it forward. For St. James's Place (SJP), one of the UK's leading wealth management businesses, manually rewriting and validating pipelines was taking
days per job. In a proof of concept with Maia, the AI Data Automation platform, SJP cut that effort by roughly two-thirds - freeing engineers to focus on higher-value work and accelerating their path to a consolidated, modern data platform.
“Maia reduced ETL migration effort by around two-thirds, taking work from days to hours… Platform consolidation will help us to reinvest in the team, to enable us to build out more on the SAP and AI roadmap of tomorrow”
KM
Kelly Maggs
Director for Data Architecture Platform and Engineering.
2/3 reduction
in legacy ETL migration effort per pipeline
Days → Hours
turnaround on individual pipeline conversions
Engineering capacity freed
and reinvested into SAP modernization and AI roadmap - without additional headcount
