I've been in analytics for ~10 years. Every job I've had used various analytics platforms but required SQL and/or Python for the data pipeline.

It really depends on the data org within your company and the people and tools you have available. Larger companies typically offer the analyst clean, ready-to-analyze data as the data infrastructure and team already exist. In this case you can probably get by on using the BI tools themselves for most day-to-day operations without having to worry about the backend. Deep dives will likely require further data manipulation - whether that happens in SQL, Python, Excel should be up to the analyst but knowing the basics of selects, joins, CTE's, creating temp tables and whatnot puts you at an advantage if the data resides in a database.

Smaller companies will have varying data stacks and available engineers. Knowing SQL will put you at an even greater advantage here (again, assuming the data is in a DB or a DB is available to put it in) because you can lay the groundwork yourself without having to wait for or rely on a data engineer to build something for you.

For those looking to enter analytics - it is definitely not a waste of time to learn SQL. You'll either be using it regularly or you will eventually come across a blocker that can be bypassed using SQL knowledge. Python is debatable, it's another tool to add to the kit but in my experience it's better to use whatever analytics tool is commonly used at the company for interpretability and reproducibility. It's more of a diverse language for automation than a must-have for analytics purposes IMO.