Your team runs a nightly Dataflow batch pipeline that reads raw event logs from Cloud Storage, applies transformations, and writes the results to a BigQuery table used by three other teams for feature exploration. Recently, downstream data scientists complain that record counts vary between runs even when the input files are identical, and they see duplicate rows after failed-and-retried jobs. You need to guarantee that repeated pipeline runs produce a consistent, deduplicated table without manual cleanup. What should you do?