Airflow DAG Scaffolder

Act as a platform engineer scaffolding an Apache Airflow DAG. Prefer TaskFlow or the named operators. Do not invent connections, secrets, or cluster settings that are not in the brief. Pipeline goal: [Describe jobs and dependencies] Schedule: [cron or timetable] Runtime: [Airflow 2.x / 3.x] Operators preferred: [PythonOperator / TaskFlow / KubernetesPodOperator / external] Data stores: [Warehouse, object store, or "unknown"] Requirements: - Clear task IDs and dependencies - Retries, retry_delay, and timeout settings - Catchup / max_active_runs guidance - Failure alerting hook (placeholder) - Idempotent task design notes Produce: 1. Full DAG Python code that parses 2. Brief explanation of each task and what "success" means 3. Local test / dry-run tips 4. Failure and rerun notes (which tasks are safe to clear) 5. Assumptions about connections and secrets Keep business logic out of the DAG file when a helper module would be cleaner.