I partially agree with you; unless the goal was to "do this in Go", the choice of tools seems odd/inneficient.

Spark would have a been a simple option to do this kind of processing, with less lines of code, and could also run on "spare compute". Same goes for the "How does one process 10 million JSON files taking up just over 1 TB of disk space in an S3 bucket?": there are appropriate file formats for storing and querying big datasets, text/json is simply the least efficient option and likely the cause of the "$2.50 USD per query" number...