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Marketing data warehouse
One warehouse for every ad platform, in your own cloud.
We build marketing data warehouses on Snowflake, BigQuery or Redshift: ad platform, CRM and commerce data ingested, every metric modeled and tested in dbt, and every table documented, so ROAS means one thing across the business.
What you get
One number for ROAS, and a warehouse you can extend.
Answers without an export
Analysts query one place instead of stitching platform downloads together.
New channels plug in
Adding a source extends the model instead of adding another manual export.
Yours to extend
Your analysts can build on it without calling us.
The problem
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.
Before and after
What changes.
The spreadsheet ceiling
- Platform dashboards disagree with each other
- The source of truth is a spreadsheet one person understands
- Every new channel adds another manual export
- A raw dump in Snowflake is just a pricier spreadsheet
A modeled warehouse
- Channel comparisons you can defend
- Weekly business questions answered from one place
- Client and executive reports that agree with each other
- New analysts get up to speed from the docs
What we build
What’s included.
- Warehouse architecture on Snowflake, BigQuery or Redshift 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
How we run it.
Step 1
Start from the questions the business asks weekly, and model backwards from those
Step 2
Ingest raw, model in dbt; never hand-transform data on the way in
Step 3
Test every metric that appears in a client-facing or executive report
Step 4
Document as we build so the warehouse survives team changes
Typical stack
What it runs on.
- dbt
- Snowflake
- BigQuery
- Airflow
- Fivetran / custom ingestion
- Looker / Power BI / Tableau
Results
Teams we've built this for.
120+ automations live
Tinuiti
We rebuilt the ETL, warehouse and automation layer across Amazon, Walmart, TikTok, Instacart and Criteo.
Read the case study
50+ campaigns, one dashboard
BTR Media
We built ETL pipelines into one data model and one dashboard for Amazon, Walmart and Instacart campaigns.
Read the case studyFAQ
Common questions.
Why do marketing teams need a dedicated data warehouse?
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.
Why dbt and Snowflake specifically?
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.
How much does a marketing data warehouse cost to build?
It depends on scope, so we quote after a free 30-minute call. The main cost drivers are how many sources you connect, how messy the historical data is, and how much custom modeling you need. Warehouse compute is billed by your cloud provider and often runs a few hundred to a few thousand dollars a month at agency scale.
Can you take over a half-built or inherited warehouse?
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.
Talk to an engineer.
Tell us what you run and where it hurts. We'll tell you what we'd build first.