There is the official answer and the realistic answer (from a business perspective):
Officially the greatest thing your Python skills will bring you is flexibility. If you are going to run some economical model where you want to show a gradient uncertainty or something else crazy, doing that manually in any Data Visualization/Business Intelligence software is going to be a pain. Or even simpler stuff like semi-complex aggregations will often be easier to do in a couple of lines of Python compared to the mess they can quickly become in BI software.
Business Intelligence software - which I will include Tableau into for this answer - can handle quite a lot of of the real life data analysis and data visualization steps. They are not particularly flexible compared to code, but day to day they are good enough. In general - given a typical business setting - I would even recommend them easily for most users. The greatest limiting factor with all of them is that the biggest job of a business data scientist is collecting and most importantly cleaning data, and that boils down to either manual labour... or coding. All BI software tries to help with pulling in data automatically and to a lesser extend help with cleaning it up, but often the real job boils down to 'connect to these databases, clean the data, combine the data and put them somewhere so you or someone else can visualize the data in BI software'.
And that's the thing, Google Data Studio is easily the least capable of all the different popular BI solutions, and yet it has become my go-to solution, because once I prepare the data in the right way I can give it to anything to explore the data and it has the easiest/best UX. And yes, any complex statistics will happen long before it gets into any BI software (in both Tableau and Microsoft PowerBI you can run Python also directly inside the product... personally I wouldn't recommend it, as it 1) just becomes messy and 2) pulls it out of source control), but those happen less often than one might expect.
If you are in the business of business intelligence then I would wholeheartedly recommend leaning on business intelligence software as much as possible. So my experience is that you have:
- What your job really is: the Data Warehouse side of things (extract your data, transform (clean it) and load (storing it somewhere you can access from both your BI software and Jupyter))
- What your end users will see: the BI software for the standard visualizations
- What you want it to be: the occasional Jupyter notebooks for specialized analyses
Of course your experience might be completely different, but this has been my personal experience having worked for a couple of years for a company who helped companies with their data driven business management (and thus I got to see how it worked in a whole bunch of companies). And yeah, often enough all a company will be using is Excel + Power Query.
PS. Tableau tries to be this all-in-one solution. Personally my experiences have not been positive with them, but for whatever it's worth, they are the oldest most traditional player on the market.