If you’re considering a career move into tech, it’s important to understand the impact of AI and LLMs (Large Language Models, the tech behind tools like ChatGPT). AI is now everywhere. ChatGPT, Claude, Gemini… It feels like a new model launches every week, with new features. But something we often don’t stop to think about is that none of it works without data engineers. AI models may be getting better, but they’re not replacing data engineering. In fact, they’ve made it more important than ever.
AI is only as good as the data behind it
LLMs are impressive, but they’re not magic. They’re trained on enormous amounts of data, and when businesses plug them into their own systems, they need that data to be clean, accessible, and up to date.
That’s where data engineers come in. It’s their job to build the pipelines that collect data, clean it up, and get it flowing to wherever it needs to go, including into AI models. Without a solid data pipeline, there’s no reliable AI.
So while everyone’s excited about what AI can do, someone still needs to build the plumbing underneath it. This is why data engineering is a growing, in-demand job.
AI is a data engineer’s toolkit, not a replacement
A common worry is “Won’t AI just do this job for me?” It’s a fair question, but the honest answer is no, not on its own.
LLMs are brilliant at speeding up parts of the job. They can help you write code faster, explain an error message, or suggest a fix. But they can’t decide what data a business actually needs, spot when something’s gone wrong in a pipeline, or design a system that scales. That still takes a human who understands what’s going on underneath.
In fact, one of the most valuable skills right now is knowing how to use AI well as a tool, while still understanding the fundamentals yourself. Employers don’t want people who can only prompt a chatbot. They want people who understand data, and who can use AI to work faster and smarter.
Why data skills and AI skills now overlap
Some of the best applications of AI right now are, at their core, data engineering problems. For example, RAG (Retrieval-Augmented Generation), where a model pulls in real, up-to-date information rather than relying only on what it was trained on.
To build something like that, you need to know how to store data, search it efficiently, and structure it so an AI model can use it properly. That’s data engineering with an AI layer on top. The two skill sets have basically merged.
This is exactly why the smartest move right now isn’t choosing between data and AI as separate careers. It’s learning both together.
Why now is the time to learn this
A few years ago, data engineering and AI were seen as separate, specialist fields. Not any more. Businesses across every industry, from retail to healthcare or finance, are trying to work out how to use their data with AI, and they need people who can build that infrastructure.
That means demand for people with these combined skills is growing fast. This is good news if you’re just starting out, because you don’t need to unlearn old habits or catch up on a decade of legacy tools. You can learn the modern stack from day one.
Getting started in data and AI
You don’t need a computer science degree, to be a math genius, or to already understand machine learning to get started. What you need is curiosity, a willingness to problem-solve, and the right support to guide you through it.
That’s exactly what Northcoders’ Data Engineering, AI & Machine Learning Bootcamp is built for. Over 13 weeks, you’ll go from Python fundamentals to building real data pipelines, working with cloud platforms like AWS, and getting hands-on with neural networks, embeddings, and LLMs, including building your own RAG-powered AI system. No prior tech experience required, whatever your background.
AI isn’t replacing data engineers. It’s creating more demand for them, and reshaping the role into something even more exciting. If you’ve been curious about tech but didn’t know where to start, this combination of data engineering, AI, and machine learning is one of the most future-proof directions you could pick.
The people who understand both data and AI will build the next generation of tools and applications. If you want to be one of them, explore the Data Engineering, AI & Machine Learning Bootcamp for your next step into tech.