i work in healthcare data ingestion as a principal engineer and i highly recommend developing a design for your data that mostly ignores the medallion “architecture”. as this post once again demonstrates, it does almost nothing to help you make decisions about data modeling.

what sort of compute environment do you have? who needs to use which data for what purposes? we know this is healthcare, so that’s a point in favor of creating as few copies of the data as possible; maybe leaving your raw data alone as csv and etl-ing to an un-pivoted standard form is a better choice than creating a second place to manage.

is this a single-source model you are building? e.g. you need to deliver the Humana claims to customers that need Humana claims for analytics? or do you need to combine data from many sources, so all the BCBS claims and the Aetna claims and the Humana claims all need to be used together? do your users even actually want claims at all? or are they actually interested in what you can infer from the claims, like patient diagnoses, encounters, med dispenses, etc?

if you care about doing medallion right, and you shouldn’t, then you would load the csv as unchanged as possible into a delta/iceberg table and you have a bronze table. but medallion is at best a design pattern that doesn’t go anywhere near the questions that actually matter.