Stop and consider that there are different tools for different purposes. Exactly the same way Word, Publisher, Photoshop, and Sublime or Vim are different from each other, so are things like PyCharm, VS Code, Jupyter, and Spyder.
A "full" IDE like PyCharm is designed for application development, including deep introspection of your code, deployment automation, tool integration for things like preprocessors (such as sass/less), and integrated support for VMs and Docker. This is overkill if you're writing a simple script, but helps for larger projects.
Something like VS Code is closer to PyCharm, but might not be as deeply integrated for any given language. It does provide a more consistent environment if you tend to work in multiple languages, which can help you avoid some of the context switching between langs.
Jupyter and Spyder serve a different purpose. They aren't the kind of thing you'd (usually) write a full application in, but it's exactly the kind of thing that would be useful to data scientist, or someone trying to explore a library. The re-executable cells in Jupyter are quite useful for quickly iterating on data cleaning code, since you can re-write and rerun a series of cells without rerunning expensive calculations or data loading.
I haven't used spyder in years (2008/9?), but it was a great way to put my fledgling python skills to use back then. I was performing a lot of data analysis on radio astro data in Matlab, but we only had so many 'roaming' licenses for our lab. Using numpy and spyder allowed me to practice my python and get my data analysis done outside of the lab.
So consider the fact that not all tools are suitable for the same thing, and that a 'senior' dev considers what tool is right for the job at hand.
I use more advanced IDE when integrating the code I developed in Spyder into a larger code base. I’m sure many will criticize my workflow but I’ve tried doing other ways and this works best for me and I write less buggy code this way.
Variable explorers and integrated charting/plotting can be fantastic for visualizing and understanding your data.
Cells in Jupyter are great for quickly iterating on data cleaning code.
When I was part of a scientific applications team at a past employer (state agency), we used Jupyter heavily while developing the core code for the data processing pipelines.
We were reading and processing data from instruments based on (bad) docs and notes from a former employee who reverse-engineered the format, so there was a lot of edge-case finding and testing. The notebooks fleshed out with comments and notes in Markdown upon completion and were saved within our repo as interactive documentation, demonstrating the more complicated processing steps in a far more approachable fashion.
I used Eclipse years ago and am neutral on it. I can use it if I need to. Just wouldn’t be my go to. I can say same for PyCharm; it’s very powerful but me and PyCharm never really jelled (lol!).