Flying back to Detroit, the author reflects on the Gordie Howe International Bridge's construction, drawing parallels between its complex logistical coordination and the role of feature stores in machine learning (ML). Just as the bridge's successful construction depends on precise material logistics, feature stores ensure that transformed and enriched data, or features, are consistently delivered for training and inference in ML models. dbt, an enterprise standard for data transformation, enhances this process by preparing clean, reliable datasets crucial for feature development. In collaboration with Snowflake, dbt facilitates feature engineering and ML workload management through its transformation capabilities, supporting both SQL and Python. This integration allows data scientists, analysts, and engineers to collaborate effectively, ensuring feature consistency and visibility across ML projects. Snowflake's Feature Store, part of its ML suite, offers scalable feature management and model training, further streamlined by dbt's governance and transformation processes. The collaboration between Snowflake and dbt empowers organizations to efficiently scale their ML operations, fostering innovation and collaboration while maintaining data integrity and quality.