Goal: inspect real BigQuery data, design tests that fit what each column means (not a generic null check on everything), deploy them as an Orchestra pipeline, run it, and report what's actually wrong. A test that fails because the data is bad is the skill working correctly — do not tune thresholds until everything is green.

Flow: profile → design tests → write pipeline YAML → branch (or create pipeline if no git) → register → run → report.

Read first

• ../../references/orchestra/dq-tests/bigquery.md — BigQuery profiling SQL, the test catalogue in GoogleSQL, the pipeline YAML, and BigQuery-specific error causes.
• ../../references/orchestra/dq-tests/workflow.md — the engine-agnostic rest of the workflow: thresholds, the matrix/gating pattern, branching, registering, triggering, and how to interpret results. Applies unchanged to BigQuery.

Workflow

1. Profile the data and design tests per bigquery.md §1–2.
2. Write the pipeline YAML per bigquery.md §3, following the gating pattern and threshold rules in workflow.md.
3. Branch the repo (or create the pipeline if there's no git), register, trigger, poll, and report — all per workflow.md, using the BigQuery-specific error causes and qualified-name format from bigquery.md §4 when interpreting results.