Being compute-bound is pretty standard for analytic queries (i.e. computing aggregate things over larger data sets). A lot of workloads do have high reuse rates of data so you'll get a lot of data cached in memory, and a lot of the processing is pretty CPU-intensive. Columnar data formats can also achieve very high compression rates, so a relatively small amount of data read off disk turns into a large number of rows. Plus, real queries often have insanely complex expressions (giant case statements, for example), that can burn a lot of compute.

It's very different from a OLTP workload where a query will read 10s or 100s of rows via a btree index.