Review this Apache Airflow DAG as if it operates a tier-1 production pipeline. Analyze the code; do not execute it.
Airflow version and executor: [version, Celery/Kubernetes/etc.]
SLA and schedule: [details]
Expected data volume and task duration: [details]
Backfill/catchup requirements: [details]
Known incidents or pain points: [details]
DAG code:
[Paste DAG and custom operator code]
Evaluate:
1. Idempotency and atomicity of every task write
2. Logical-date/data-interval correctness and avoidance of wall-clock coupling
3. Retries, exponential backoff, timeouts, and non-retryable failures
4. Catchup, backfill, max_active_runs, pools, and concurrency behavior
5. Sensor mode, deferrable operators, dynamic mapping, and scheduler load
6. XCom size, secret handling, templating, and serialization risks
7. Partial-failure recovery and safe manual reruns
8. Data-quality gates, lineage, metrics, and alert routing
9. Separation of orchestration from business logic
10. Unit, DAG-parse, integration, and idempotency tests
Return findings as Blocker/High/Medium/Low with line references, corrected snippets for the top issues, and a go/no-go recommendation.