A data engineering team runs sporadic Apache Spark batch jobs to transform data landing in Cloud Storage. Job volumes are unpredictable, and the team currently spends significant effort sizing, patching, and tearing down Dataproc clusters. They want to eliminate cluster lifecycle management entirely while still running their existing PySpark code, and pay only for the resources each job consumes. Which approach best meets these requirements?