Most data teams hit the same wall. The pipelines run, the warehouse fills up, and yet nobody trusts the numbers. Two dashboards report different revenue. An analyst spends a morning rebuilding a definition of “active user” that someone already wrote last quarter. The data exists, but the data nobody argues about does not.

Analytics engineering is the work that closes that gap. It sits between the people who move data and the people who interpret it, and it owns the messy middle where raw tables become datasets you can trust.