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The AI Wake-Up Call for Data Engineers: Why LLMs + MCP Matter Now
From Skeptic to Builder: Why AI Is Now Critical for Data Engineers
I wrote a blog post a few months ago — How to Build an AI Agent for Data Analytics Without Writing SQL. One comment I received was, “This is mostly a toy example; my production jobs are way more complex than this; it won’t be applicable to my job.” — but it misses a bigger point: AI is evolving fast, and it’s reshaping how we build pipelines, write SQL, and even debug. In many companies, AI skills are no longer optional for data engineers. We cannot overlook the fact that AI proficiency is now mandatory.
Let us look at how modern AI, particularly LLMs, RAG, and MCP, is becoming not only relevant, but essential for production-grade data engineering.
Can AI resolve the intricate SQL and data pipeline issues that data engineers face? Please put your skepticism of the potential of AI away, and I can show you its potential and how AI can enhance data engineering.
Let's examine why there is widespread skepticism about AI's capacity to handle complex data engineering logic and what has changed.
