For the past two decades, most enterprise data engineering systems have been built on one default assumption: People understand the system. The system executes the pipeline.Engineers understand the business context, break a requirement into steps, write SQL, Spark jobs, shell scripts, synchronization tasks, and scheduling workflows, and then let the system run them. The scheduler does not need to understand the business. The sync engine does not need to understand the metric. It only needs to execute the predefined flow reliably.