There's typically a role (data engineer?) where one is essentially a fancy DB admin that extracts and centralizes all data into db tables as per how the analysts want. Sometimes this is local and sometimes a data pool of s3 buckets. This is harder than it sounds where depending on the business and how fast it needs to action data you are likely skilled on low latency networking and fast algorithms to transform and load data from all kinds of hard to extract places like other proprietary software. They also have you adding desired analytics like PostGIS for location-based aggregations, designing all the backups, and some companies have 100+ pipelines as many proprietary data sellers exist that give an end of day dump or real-time fire hose you have figure out how efficiently capture and store.

An analyst then loads this up with whatever visualization software or 3rd party BI tools and interprets the findings and I've found this varies from giving a simple presentation after AI tools do everything for them to doing full on R Studio analytics such as these models https://www.stat.cmu.edu/~cshalizi/ADAfaEPoV/

If the analyst then selects a feature set from the data and performs machine learning I've found they are called 'Data Scientists' otherwise they are analysts but these titles mean something different everywhere.