dbt work spans dbt projects, models, tests, runs, data-quality investigations, and analytics-engineering handoffs. In a signed-in dbt tab, Strawberry can help an analytics engineer make sense of the project context around models, tests, runs, and documentation, then prepare a careful review of what needs investigation next.
It supports the reasoning around data transformations without turning an unreviewed browser workflow into a production change.
01
See the model in the project around it.
A single transformation rarely explains its own importance; its upstream inputs, downstream consumers, tests, and documentation define the real risk. Strawberry can assemble the visible dbt project context into a model-review brief that lets an engineer start with dependencies instead of hunting through tabs.
02
Treat a failed test as an investigation, not a verdict.
A red test can indicate a meaningful data problem, an expected shift, or a condition that needs further evidence. Strawberry can organise selected dbt results and related material into an incident note that separates the observed failure from the conclusions someone is considering.
03
Make analytics-engineering handoffs traceable.
Data work loses time when the next owner inherits an alert but not the project context or previous reasoning. Strawberry can create a structured handoff from the selected dbt pages, preserving the model, test, constraint, and outstanding decision that matter.
04
Prepare a routine review without running production work.
A repeated quality review can help a team notice tests, documentation gaps, and unfamiliar changes early, as long as it does not take action based on a shallow interpretation. Save the dbt review as a skill and use a routine for a private preparation pass.
dbt Strawberry