Choosing an Architecture for a Growing Data Platform

A 40-person startup currently dumps all event data as raw JSON files into an S3 bucket ("just in case we need it"), and analysts run ad hoc Python scripts against exports when they need numbers — there's no warehouse or lakehouse yet. The company is about to: (a) hire its first two data analysts who need to self-serve SQL dashboards, (b) start training a recommendation model that needs to read the same historical event data as the analysts, and (c) is cost-conscious as a startup but expects data volume to grow 5-10x over the next two years.

1. Would you recommend a data warehouse, a lakehouse, or continuing with the current raw data lake approach (with some improvements)? Justify the choice against the specific constraints given, not in the abstract.
2. What would change your recommendation if the ML use case (part b) didn't exist and it were purely the two analysts?
3. Name one concrete risk of your recommended architecture and how you'd mitigate it given the team's small size.