When Snowflake Lies to You: Understanding False Failures in dbt Pipelines (opens in new tab)

The Problem Most Teams Get Wrong Every data engineer has lived this moment. A dbt model fails at 3 AM. You pull up the logs, see a type conversion error, and start digging through SQL. You check recent commits. Nothing changed. You inspect the upstream data. Nothing looks off. You rerun the job. It passes. You shrug, label it a transient issue, and go back to sleep.