The modern discipline of data engineering considers ETL (extract, transform, load) one of the processes that must be done to manage and transform data effectively. This article explains how to create an ETL pipeline that can scale and uses dbt (Data Build Tool) for transformation, Snowflake as a data warehouse, and Apache Airflow for orchestration. The article will propose the architecture of the pipeline, provide the folder structure, and describe the deployment strategy that will help optimize data flows. In the end, you will have a clear roadmap on how to implement a scalable ETL solution with these powerful tools.