Marketing data warehouse consultants who build dbt + Snowflake warehouses for agencies and marketing teams: modeled, tested, documented, and built for ad data.
The spreadsheet ceiling
Most marketing teams hit the same wall: platform dashboards disagree with each other, the 'source of truth' is a spreadsheet only one person understands, and every new channel adds another manual export. A warehouse fixes this only if it's modeled properly a raw data dump in Snowflake is just a more expensive spreadsheet.
What we build
- Warehouse architecture on Snowflake or BigQuery sized for your data volume and budget
- Ingestion from ad platforms, CRM, web analytics, and commerce systems
- dbt models with tests and documentation staging, intermediate, and reporting marts
- Conformed metric definitions so spend, revenue, and ROAS reconcile across sources
- Incremental models and warehouse cost controls that keep compute bills predictable
- BI layer wiring into Looker, Power BI, or Tableau
How we work
Start from the questions the business asks weekly, and model backwards from those
Ingest raw, model in dbt never hand-transform data on the way in
Test every metric that appears in a client-facing or executive report
Document as we build so the warehouse survives team changes
Typical stack
Frequently asked questions
Because platform dashboards each report their own attributed version of reality. A marketing data warehouse pulls raw data from every platform into one place, applies a single set of metric definitions, and lets you compare channels honestly which is impossible when Meta, Google, and Amazon each grade their own homework.
dbt brings software engineering practice version control, tests, documentation to the transformation layer, which is where most marketing data quality problems live. Snowflake separates storage from compute, so agency-scale workloads stay affordable. We also build on BigQuery when a team is already on Google Cloud; the modeling discipline matters more than the vendor.
A focused build a handful of sources, modeled marts, and dashboards typically lands in the low-to-mid five figures over 2–3 months. Warehouse compute itself often runs a few hundred to a few thousand dollars a month at agency scale. The main cost driver is source count and how messy the historical data is.
Yes a large share of our warehouse work is rescue: auditing an existing Snowflake/dbt project, adding tests where numbers have drifted, and refactoring models so the warehouse can be extended safely. You keep everything that works; we fix what does not.
Go deeper
Get one source of truth
Send us your source list ad platforms, CRM, commerce and we'll propose the warehouse architecture and modeling plan.
Start a project