When executing a Firetron job, if it fails due to a platform issue, what steps would you consider to recover the data?

First, isolate the failure by reviewing the job logs and platform metrics to determine whether the issue is transient (e.g., network hiccup) or persistent (e.g., resource exhaustion). If the job was idempotent, simply restart it from the last successful checkpoint; many ETL frameworks support checkpointing that records the last processed record. If checkpoints are not available, use the platform’s undo or rollback features (e.g., Snowflake’s UNDO or a transactional staging table) to revert any partial writes. Next, validate data integrity by comparing source and target row counts or checksums. If the failure caused data loss, re‑run the ingestion for the affected partitions only, using a “resume” flag or a time‑window filter. Finally, implement monitoring and alerting to detect similar failures early, and consider adding retry logic with exponential back‑off to handle transient platform issues automatically. These steps ensure minimal downtime and data consistency after a failure.