AI-assisted coding has transformed software engineering by reducing implementation effort, but enterprise data platforms span multiple technologies, upstream and downstream systems, and teams with different responsibilities, creating fragmentation that code generation alone cannot solve. The article introduces Spec-Driven Data Engineering (SDDE), an architectural approach treating executable specifications as operational contracts for AI-assisted development. By moving critical system knowledge from temporary prompts into versioned specifications, organizations can build AI-powered data platforms that are easier to understand, evolve, validate, and maintain. In the AI era, executable specifications are becoming the new source code—the persistent system memory coordinating human engineers and AI agents. Modern enterprise data platforms already span transactional systems, SaaS applications, APIs, streaming platforms, NoSQL databases, warehouses, semantic layers, dashboards, and machine learning and AI agent systems owned by different teams. A single upstream source often feeds multiple downstream systems, making change impact hard to predict. Organizations have struggled with schema drift, difficult debugging, weak lineage, and downstream compatibility problems. A small upstream schema modification could silently break ingestion jobs, transformations, dashboards, APIs, or ML feature pipelines. AI-assisted vibe coding amplifies these issues because engineers generate far more pipelines than they can validate. As logic becomes AI-generated, architectural decisions and transformation logic become embedded in prompts rather than explicit contracts. Prompts are temporary, so reasoning is lost once pipelines reach production, creating operational fragmentation. To improve coding agents from temporary chat assistants to permanent production support, context and logic captured in prompts must become