Verify the dbt project

Reference project layout

The reference project ends up looking like this. The context/ directory is what this guide produces - everything else is dbt prerequisites:

contoso-dbt/
├── ingest/pipeline.py         # dlt - loads raw tables to BigQuery
├── models/                    # dbt - staging + reports
│   ├── sources.yml
│   ├── staging/stg_*.sql      (10)
│   └── reports/rpt_*.sql      (7)
├── context/                   # ← the Bruin context layer (this guide)
│   ├── .bruin.yml
│   ├── pipeline.yml
│   └── assets/
│       ├── contoso_dbt_raw/*.asset.yml      (23)
│       ├── contoso_dbt_staging/*.asset.yml  (10)
│       └── contoso_dbt_reports/*.asset.yml  (7)
├── AGENTS.md                  # how agents should use the context
└── run_pipeline.sh            # dlt ingest + dbt build

The end state is 40 YAMLs - one per materialized table - covering raw ingest, staging, and reports.

What the dbt half looks like

The contoso-dbt project uses a fairly standard dbt-on-BigQuery setup. If yours looks similar, you're ready:

• A dbt_project.yml and a profiles.yml (the reference uses OAuth via gcloud auth application-default login, location EU)
• A models/sources.yml declaring raw tables loaded by dlt into a *_raw schema
• Staging models (stg_*.sql) materialized into a *_staging schema
• Report / mart models (rpt_*.sql) materialized into a *_reports schema
• A generate_schema_name.sql macro so a +schema: staging config lands as contoso_dbt_staging, not contoso_dbt_contoso_dbt_staging
• dbt build runs cleanly and the tables exist in the warehouse

Heads up on schema naming. dbt's default generate_schema_name macro concatenates the target schema with the +schema config, which is rarely what you want. The reference project overrides it to use the +schema value directly. If your final schema names look doubled-up, this is usually why.

Verify the warehouse has data

Before moving to the Bruin steps, make sure your dbt-built tables actually exist. From your dbt project root:

dbt build

Then spot-check the warehouse. For BigQuery:

bq ls bruin-playground-arsalan:contoso_dbt_staging
bq ls bruin-playground-arsalan:contoso_dbt_reports

For Postgres / Redshift:

SELECT table_schema, table_name
FROM information_schema.tables
WHERE table_schema IN ('contoso_dbt_staging', 'contoso_dbt_reports')
ORDER BY 1, 2;

You should see one row per materialized model. If a model is missing, fix dbt before continuing - Bruin will simply skip what isn't there.