So have I (not FAANG, though). I've been working in scientific python for over 20 years and long before notebooks came to the python world. (Yes, pre-numpy) And I've seen notebooks abused far more than used well. The same general thing was heavily abused in Matlab/etc too. It's not only Jupyter and it's not new.
It's not that they're not a good tool, it's that they're the wrong tool for a lot of what they're used for. They're an excellent teaching and documentation tool. However, they're remarkably bad for actually doing any type of analysis or exploration. They're a great way of presenting your analysis after you complete it, but they're bad for actually doing that analysis. Write analysis code and data exploration code using the same tools you would for production code. There's nothing wrong with an edit + run + explore loop. Do that with a standard REPL like ipython + your preferred editor.
But there's a lot wrong with trying to use a notebook to write and execute code, as it:
1) encourages writing code that won't actually run later (dependent on non-linear code execution and unsaved changes made to earlier cells), and
2) is a poor environment for writing code compared to more full featured editors or IDEs, and
3) discourages interactivity and data exploration due to being browser-based (poor performance compared to running directly limits interactivity during data exploration).
Your data analysis work needs to actually run independently later. Notebooks rarely do unless they're re-made from scratch. At that point, you're better off working in a more full-featured environment for the exploration phase and then using the notebook as later documentation / communication.
I've taught and worked with some of the biggest companies in the world. They were using Jupyter and sound EDA to great effect.