Undocumented schemas, restricted access, no staging environment, and data that disagrees with what you were told.
4 questions in Forward Deployed Engineering.
Build the data profile and a validation report first, not the dashboard. Read every column as text, infer types from evidence, show the customer what you found within the first hour, then load accepted rows idempotently into a staging table with a rejected-rows file that explains every exclusion.
Pull a bounded, representative extract into an environment you control and develop against that, keeping production for read-only verification under agreed limits, and treat every write path as something proven against a restored copy before it runs anywhere near live.
Read the data rather than the DDL: profile every column for cardinality, null rate and value distribution, use foreign-key-shaped joins to test relationships you suspect, and confirm each conclusion against a report the business already trusts before you build on it.
Segment the nulls before drawing any conclusion — by date, source system, record type and creating process — because a null rate that clusters is a documented business path nobody mentioned, while one spread evenly is usually a genuine capture defect.