I've worked with many data scientists whose typical SQL usage was to load entire rows (or with simple 'with' filtering) into Pandas / R dataframes and then do all their work there.
I think it's a combination of Pandas and R having much simpler APIs and a ton of documentation on Stack Overflow, and modern hardware just being so good that you can load a big enough chunk of your dataset in-memory on a laptop.
I mostly use SAS, I tend to prefer using plain sql queries where I typically depart SQL and jump into code is doing what SAS calls "By Group processing" (example https://support.sas.com/kb/26/013.html#)
I am not as familiar with R. Last time I worked in R (some years ago) equivalent R code was something like this caution I'm no expert in writing R so might be a better /more intuitive way...
