DBT Cloud Landing Zone Provisioning by Data Army

Almost nobody's dbt project fails at the start. It fails at model number forty.

The pattern is consistent. Someone capable installs dbt, builds a proof of concept, and it works. It gets used. More models get added. Then:

No consistent layering, so nobody can tell whether a model is a staging model, a mart, or something in between Tests exist on the first ten models and nowhere else No CI, so breakage is discovered by whoever opens the dashboard first Documentation was going to be a later task Model naming reflects four different people's preferences Rebuilding a downstream model means understanding a lineage graph nobody has looked at in months

Unpicking that later is significantly more expensive than structuring it correctly at the outset, and it usually happens at the exact moment the business has started depending on the output.

What we deliver

Repository and project structure Layered project architecture (staging, intermediate, marts) following current dbt Labs guidance, with naming conventions, folder structure, and a documented pattern your team extends rather than reinterprets.

CI/CD pipeline Automated build and test on pull request, so breakage is caught before merge rather than in production. Configured against your existing Git platform, whether GitLab, GitHub, or Azure DevOps.

Testing framework Generic and singular tests configured across delivered models, source freshness checks, and a documented standard for what gets tested and why, so coverage does not decay as the project grows.

Initial model set A working set of production models built against your source data, following the delivered patterns. These serve as both immediate value and the reference implementation your team copies.

Documentation and enablement Project documentation, contribution guidelines, and a working session with your team covering the patterns, the reasoning behind them, and how to extend them.

As a dbt Visionary Partner, we understand that a dbt project is only as good as its interaction with the warehouse underneath it. Materialisation strategy, incremental logic, and clustering decisions are warehouse decisions made in dbt, and getting them wrong is where dbt projects quietly become expensive.