Many recent Text-to-SQL solutions claim to replace Data Engineers, but they often lead to unreliable results with inaccurate queries on complex Datasets. In reality, Data Engineering remains essential: pipelines, transformation, and data modeling must be in place for AI to function effectively. LLMs, trained primarily on language, can help interpret queries, but deterministic systems are needed to convert them to SQL using well-defined data models built by Data Engineers. This talk will explore why Data Engineering remains essential to the success of AI implementations.