Using a date parameter to control data volume across Dev, UAT, and Prod is a practical and reasonable approach, especially when dealing with large datasets. It allows you to limit the amount of data processed in non-production environments without affecting testing or development workflows.

Some considerations to make this approach effective:

1. Consistency: Ensure that the same date logic is applied across all environments to prevent discrepancies in testing outcomes.
2. Coverage: Pick date ranges that include representative samples of your data to test edge cases and typical scenarios.
3. Automation: Integrate the date parameter into ETL or CI/CD pipelines so that developers and testers don’t have to manually adjust it.
4. Documentation: Clearly communicate how the date parameter affects data volume to avoid confusion among team members.

Overall, using a date filter is a common best practice in enterprise data engineering to manage resource usage while maintaining meaningful test coverage.