Case study
Migration of Analytics Workload from Snowflake to BigQuery
How SquareShift migrated a recruiting SaaS platform's analytics workload from Snowflake to BigQuery.
A warehouse migration succeeds or fails on what happens to the dashboards built on top of it. Re-pointing Looker to BigQuery mattered as much as moving the data itself.
Data & Analytics Practice Lead, SquareShift
A SaaS-based applicant tracking and recruiting platform that helps thousands of companies source, hire, and onboard top talent.
Its analytics ran on Snowflake and Looker, but slow queries, ETL lag, and high storage costs were holding the reporting back.
- <10s — Dashboard refresh on BigQuery, down from hours of ETL lag.
- 70+TB — Snowflake analytics workload migrated to BigQuery.
- 4B–5B events/week — Handled on the rebuilt BigQuery pipeline.
How SquareShift delivered it.
The challenge
The client’s analytics ran on Snowflake and Looker, processing 4B–5B events a week across more than 70TB of data. Query performance had slipped, ETL runs lagged 4–6 hours behind, and storage costs kept climbing.
What we delivered
SquareShift migrated the full analytics workload from Snowflake to BigQuery and re-pointed the existing Looker dashboards to the new backend, so reporting kept working through the cutover. The rebuilt pipelines simplified the data architecture end to end.
The payoff
Dashboard refreshes now complete in under 10 seconds, data arrives fresher, and the simplified BigQuery architecture costs less to run than the Snowflake setup it replaced.
Where this work sits