How do you handle schema evolution in a data pipeline or lakehouse without breaking consumers?
A new column is safe; a rename silently corrupts every dashboard downstream. The signal is knowing which changes are compatible, enforcing data contracts in CI, and running expand-contract migrations for the breaking ones.
Updated Aug 2026 · Grounded in real Applied AI Engineer interview loops and written to a senior-engineer editorial bar.
A new column is safe; a rename silently corrupts every dashboard downstream. The signal is knowing which changes are compatible, enforcing data contracts in CI, and running expand-contract migrations for the breaking ones.
Lead with where the obvious approach breaks, because that is the judgment they are screening for — most candidates jump straight to the happy path and lose the room.
Then walk the failure back through the pipeline in order, naming the one metric the customer's exec sponsor actually cares about before you propose the fix.