How would you manage having two separate dbt packages in different repos for a Redshift serverless warehouse, and what steps would you take to standardize and integrate them using Airflow?

When you have two dbt packages in separate repos, the first step is to define a clear boundary for each package: one for core transformations (e.g., raw ingestion) and one for downstream analytics. You would then create a shared dbt project that imports both packages as dependencies, using the packages.yml file. Each package would expose its models as a set of materialized tables or views. In Airflow, you would create a single DAG that triggers the dbt run for each package in the correct order, passing the appropriate environment variables. To standardize, you would enforce a common naming convention, use the same schema naming (e.g., stg_, int_, fct_), and share a common dbt_project.yml that sets the target database to Redshift serverless. You would also set up CI/CD to run dbt test on every commit, ensuring that both packages adhere to the same quality standards. Finally, you would document the data lineage and maintain a data catalog so that downstream teams understand which package owns which tables.