The honest picture for learners
If you are learning data engineering, the risk with AI tools is using them as a shortcut rather than as a learning aid. There is a real difference between pasting a problem into ChatGPT to understand the concept better, versus pasting it to get an answer you copy without understanding. The first helps you learn faster; the second fills your portfolio with code you cannot explain in interviews.
Interviewers have noticed this pattern. A candidate who can explain the code they wrote and adapt it in real time stands out clearly from one who memorised AI-generated output. The underlying knowledge of SQL, Python, and distributed systems has to be in your head — AI tools help you express and apply that knowledge faster, they do not substitute for having it.
Use AI tools to: understand error messages, generate starter code that you then modify and own, check your understanding of concepts, and speed up repetitive tasks. Do not use them to: complete assignments you have not tried yourself, write code you cannot explain, or replace the practice of building things from scratch during your learning phase.