ETL Error Diagnosis with Claude Code: Human Review Queue
A production playbook for ETL error diagnosis in cross-industry operations using Claude Code: human review queue, run-scoped inputs, logs, typed results, and artifacts.
Audience: Data platform teams
The problem
Data platform teams need ETL error diagnosis to run repeatedly against pipeline logs, schemas, samples, and freshness checks. In cross-industry operations, the pain is not one good answer; it is repeatability, auditability, exception handling, and evidence that survives handoff.
Implementation path
Split the ETL error diagnosis result into automatable fields and review-only exceptions, then send low-confidence cases to a human queue with evidence artifacts attached.
Tradeoffs and failure modes
Human review slows a subset of runs, but it lets the workflow ship before every edge case is fully automated. For ETL error diagnosis, the practical test is whether a second run can be debugged, retried, and consumed by a product without reading the raw agent transcript.
Review handoff
review_status: needs_review | approved | rejected
review_reason: string
source_evidence: artifact_url[]
agent: Claude Code
workflow: etl-error-diagnosis
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