Many recent text-to-SQL solutions claim to replace data analysts, but they result in untrustworthy data and inaccurate queries on anything but the most simple datasets. In reality, data engineering is needed to define the semantics of the data and provide a way for LLMs to naively request what they want without needing to generate complex code. Deterministic systems are needed to convert these requests into accurate SQL using the well-defined data models built by engineers. This talk will explore why data engineering remains critical for successful AI implementations.