A data engineering team runs a nightly AWS Glue ETL job that reads raw clickstream files landing in an S3 bucket, transforms them, and writes the results to a curated S3 location that feeds a daily model retraining pipeline. New files arrive throughout each day, and the team notices that every nightly run reprocesses the entire history of files, causing job duration and cost to grow continuously. They need the job to process only newly arrived data on each run without manually tracking which files were already handled. What is the most operationally efficient way to achieve this?