Apache Airflow is the go-to platform for data orchestration, while dbt is widely recognized for analytical data transformations. Using astronomer-cosmos library, integrating dbt projects into Airflow becomes straightforward, allowing each dbt model to be transformed into individual tasks or task groups equipped with Airflow features like retries and callbacks. However, organizing dbt models into separate Airflow DAGs based on domain or user-defined filters presents challenges in maintaining dependencies across these distinct DAGs. Ensuring that downstream dbt tasks only execute after the corresponding upstream tasks in different DAGs have successfully completed is crucial for data consistency—yet this functionality is not supported by default. Join GetYourGuide as we explore our method for dynamically creating inter-DAG sensors in Airflow using Astronomer Cosmos for dbt. We will show how we maintained dbt model dependencies across multiple DAGs, making our pipeline modular, scalable, and robust.