Snowflake engineering: modeled warehouses with dbt, cost-governed compute, and data sharing, built by a team that ships marketing data platforms on it.
The warehouse bill nobody can explain
Snowflake credits burn silently: warehouses left running, oversized compute for trivial queries, and dashboards hammering unmodeled raw tables. Meanwhile metric definitions drift between dashboards until no two reports agree. Both problems have the same fix: a modeled, governed warehouse.
What we build with Snowflake
- Warehouse architecture with dbt models that make metrics mean one thing
- Compute sizing, auto-suspend, and resource monitors that cap burn
- Ingestion from ad platforms, marketplaces, and product databases
- Data sharing and secure views for clients and partner teams
How we work
Model with dbt from day one; raw-table dashboards are debt
Size warehouses per workload and let auto-suspend do its job
Set resource monitors before the first surprise invoice
Document models so analysts self-serve without guessing
Typical stack
Frequently asked questions
Both are excellent; the decision usually follows your cloud, your team, and your pricing shape. Snowflake favors multi-cloud flexibility and granular compute control; BigQuery favors GCP-native stacks. We build on both weekly.
Yes. Most savings come from auto-suspend, warehouse rightsizing, and moving hot dashboards onto modeled marts, none of which change what users see except the speed.