Five dbt Mistakes That Slowly Break Startup Data Pipelines

This article outlines five recurring dbt mistakes that cause startup data projects to become unmaintainable and expensive over time. The author explains how running full dbt builds in CI wastes warehouse credits and slows development, why missing data contracts on mart models let silent schema changes break downstream dashboards, and how incremental models without on_schema_change accumulate hidden schema drift. Additional pitfalls include misusing ref() for raw sources. For each issue, the article provides concrete configuration examples and remediation strategies using modern dbt features like state comparison, deferred builds, and enforced model contracts.