Generally I don’t view a “feature store” as being a simple lake of raw data. To me a feature store is a place where features known to have signal in a ML model are stored. It has structure, “clean” data (to the extent possible) and some pre-computed elements (e.g. aggregates over some commonly used time windows) to facilitate efficiency in the end to end ML pipeline.

I’d also argue a feature store is not complete unless there are tools and infrastructure to make it relatively easy to provision and access new features: that is, it should cover the issues you raise.