Andrew Kuhnhausen Interview
summary
Describes concrete engineering choices at Domino Data Lab: balancing rapid feature work with enterprise-grade stability, supporting small-to-medium data science teams across bare metal, AWS and Azure, and integrating with Spark and SAS Studio. Describes building a caching service using Play and Akka with a functional wrapper, introducing type classes and Either for error handling instead of exceptions, and the org growth from under 20 to about 80 people that forces new tooling and process decisions for multi-tenant and multi-region needs.
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