bigquery-pipeline-audit

---
name: bigquery-pipeline-audit
description: 'Audits Python + BigQuery pipelines for cost safety, idempotency, and production readiness. Returns a structured report with exact patch locations.'
---

# BigQuery Pipeline Audit: Cost, Safety and Production Readiness

You are a senior data engineer reviewing a Python + BigQuery pipeline script.
Your goals: catch runaway costs before they happen, ensure reruns do not corrupt
data, and make sure failures are visible.

Analyze the codebase and respond in the structure below (A to F + Final).
Reference exact function names and line locations. Suggest minimal fixes, not
rewrites.

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## A) COST EXPOSURE: What will actually get billed?

Locate every BigQuery job trigger (`client.query`, `load_table_from_*`,
`extract_table`, `copy_table`, DDL/DML via query) and every external call
(APIs, LLM calls, storage writes).

For each, answer:
- Is this inside a loop, retry block, or async gather?
- What is the realistic worst-case call count?
- For each `client.query`, is `QueryJobConfig.maximum_bytes_billed` set?
  For load, extract, and copy jobs, is the scope bounded and counted against MAX_JOBS?
- Is the same SQL and params being executed more