How would you scale your data transformation workload when your core team is overloaded, and what strategies would you use to isolate parts of the pipeline for other teams to handle?

When the core team is overloaded, the first step is to identify the transformation steps that are bottlenecks and can be decoupled. You would refactor the pipeline into independent modules or dbt packages, each with a clear input and output contract. By packaging the logic, you can hand off a module to another team (e.g., the DICE team) without exposing the entire codebase. You would also adopt a versioned, CI‑driven deployment process so that each team can test and release its module independently. To keep the overall pipeline coherent, you would use a central orchestrator (Airflow, Prefect) that calls the modules in sequence and passes metadata. Additionally, you can implement a shared data catalog and governance rules so that all teams understand the schema and data quality expectations. This modular approach reduces the bandwidth requirement on the core team, accelerates delivery, and improves maintainability.