Data warehouseWe set up BigQuery as your warehouse, bring in data from every tool you use, model it with dbt, and put it to work in dashboards, AI agents, attribution and the tools your team already uses.
Your data is spread across payments, CRM, ad platforms and your product database, and you want it in one place. But it isn't clear how to structure BigQuery, what to bring in first, or how to keep the bill low.
The GA4 export is switched on and someone loaded a few tables once. The rest of your data isn't there, nothing updates on its own, and nobody trusts the tables that exist.
Meta, Google Ads, your store and your CRM each claim the same sale, and revenue never matches. You want one set of numbers, calculated one way, that the whole team uses.
Answers still take a data request and a few days. You want dashboards people open, an AI agent that answers questions, and modelled data sent back to the tools your team works in.
We set up BigQuery, so it stays organised, secure and cheap as it grows.
We write Python scripts that pull data from every tool you use into BigQuery on a schedule, so the warehouse becomes your source of truth.
We use dbt to turn raw tables into clean models your whole team works from, with one definition for every metric.
We put the models to work: dashboards your team opens every day, and AI agents that answer questions in plain English.
We send modelled data from BigQuery to the tools your team works in, in the exact format each one expects.
Buildern's product data sat in BigQuery as raw app tables, and almost every visit landed in Direct. We modelled the warehouse with dbt at the account level, rebuilt attribution, and added a health score that flags accounts drifting toward churn.
Read the case studyAnyIP couldn't see which marketing channels worked, because its raw data was messy and inconsistent. We cleaned and structured it, built reporting on top, and the team moved budget to the channels that convert.
Read the case studyThese are real results from teams like yours.
Book a callMost setups take four to six weeks. Week 1 is the plan and the warehouse setup, the next few weeks bring in your data and build the dbt models, and dashboards and syncs to your other tools go live at the end. Finishing an existing setup is usually quicker.
Yes. We audit what's already there, keep what works, fix what's broken and bring in the data that's missing. Rebuilding from zero is rarely needed.
BigQuery charges for storage and for the data each query scans. The first 10 GB of storage and 1 TB of queries each month are free, and most startups pay little beyond that. We partition tables and set query limits, so costs stay low as your data grows.
If you use GA4, Firebase or Google Ads, BigQuery is usually the easiest start: those tools export to it for free, there are no servers to manage, and you only pay for what you use. We also work with Snowflake and Postgres, and will tell you if one of them fits you better.
We use both. Connectors like Fivetran and Airbyte are quick for popular tools, but they charge by volume and don't cover every tool. Where a connector is missing or gets expensive, we write Python scripts that you own and that cost a few dollars a month to run.
dbt is a way to write data models in SQL, with version control and tests. Every metric is defined once, changes are reviewed before they go live, and broken data is caught before it reaches a dashboard. For anything beyond a handful of tables, it's worth it.
Yes. Each ad platform counts its own conversions, so together they claim more sales than you made. We build attribution in BigQuery from your own data, joining every touchpoint to real orders or sign-ups, so there's one number the whole team uses.
Yes. We send audiences and offline conversions to Meta and Google Ads, and scores like LTV and churn risk to your CRM or product analytics tool, formatted the way each tool expects.
No. We build and document everything, and the pipelines run on their own. Someone on your team who can read SQL can make small changes, and we can stay on to maintain it if you'd rather not.
Yes. Everything lives in your own Google Cloud project and your own Git repository, so your team keeps the warehouse, the scripts, the models and the documentation after we're done.