The Empirico company, which specializes in drug discovery and development using human genetics and data-centric approaches, has implemented Dagster as part of their data infrastructure to support large-scale, multi-cloud computing. With a team consisting of data engineers, data scientists, bioinformaticians, and biologists, they aimed to improve the pace of drug discovery by speeding up the analysis of genetic data. Empirico built a bespoke interface to integrate with cloud compute layers, using standard big data tools like Spark, Pandas, Databricks, and NumPy. The team evaluated various orchestration solutions before adopting Dagster, which provided dynamic capabilities for branching and conditional execution. By leveraging Dagster's StepLauncher abstraction, they were able to transparently move op code to remote execution environments and trigger it from a Dagster step worker. This allowed them to manage compute environments dynamically, combining datasets and returning results based on user selections. The adoption of Dagster has transformed how Empirico can support big data analyses, enabling the team to introduce new compute providers without interrupting workflows and improving observability across the entire platform.