dbt (data build tool) is an open source tool for transforming data directly inside a data warehouse by writing SQL, with software engineering practices: Git versioning, tests, documentation and automated deployment.
A dbt project is a set of models, SQL queries that each build a table or view from others. dbt works out the execution order from the dependencies between models, compiles everything (with Jinja macros) and runs it in the warehouse: Snowflake, BigQuery, Databricks, Redshift, PostgreSQL… Tests check the data (uniqueness, null values, relationships) and documentation is generated from the project.
It is developed by dbt Labs, with an open source version (dbt Core) and a cloud offering.
dbt sits at the heart of what is called the modern data stack and gave rise to the analytics engineer role, between the data engineer and the data analyst.
Why it matters when hiring
dbt has become a baseline skill for advanced data analysts, analytics engineers and data engineers. The level shows in how the project is organized: models split into layers, tests, handling of incremental models, run times. Have the candidate walk through a real project. Writing SQL queries in dbt does not make someone a data engineer able to build ingestion pipelines.