dbt Model Development

dbt models are how we transform raw data into trusted metrics your teams can rely on. Our dbt projects include testing, documentation, and CI/CD that your team can maintain.

Our Approach

We structure dbt projects with staging, intermediate, and marts layers. Our models include tests, documentation, and lineage that make your data trustworthy and maintainable.

Capabilities

dbt Project Build

Complete dbt projects with proper structure and conventions.

dbt Rescue

Fixing existing dbt projects with build time optimization.

Testing & Documentation

Comprehensive tests and docs for trustworthy models.

CI/CD for dbt

Automated testing and deployment pipelines for dbt.

How It Comes Together

A typical data platform architecture

In Practice

A dbt project without tests and documentation is just SQL with extra steps. We structure every project around the standard staging, intermediate, and marts layering, so raw source data is cleaned once in staging, business logic lives in intermediate models where it can be reused, and marts expose clean, well-named tables that analysts and BI tools can query directly without needing to understand the transformation history behind them. Every model ships with tests, not_null, unique, referential integrity, and business-logic-specific tests where relevant, because a broken model that fails silently costs far more trust than one that fails loudly in CI before it reaches production. We also take on dbt rescue work: inheriting projects with years of undocumented models, circular dependencies, and multi-hour build times, and restructuring them for maintainability and speed, where the levers that matter most are materialization strategy and incremental models. Documentation is generated from the dbt project itself rather than maintained separately, so it can't silently go stale the way hand-written wikis inevitably do.

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