I did use BigQuery in the startup I was working for before, and it worked wonders for our 12Tb of data. I think it would be a bit overkill in our situation---even though not having to manage a DB is great.
We had Redshift for our 23TB+ dataset and it worked great. The downside is it can get pricy, so do a cost analysis before you commit. Also know that views in redshift are not materialized so it’s more efficient to create physical tables of the views - which then adds maintenance overhead. The last thing I’ll add is that you’ll need to experiment with compression settings for your data. For us, a combination of ZSTD and bytedict was all we needed
Another item to consider is that BQ now has ML (simpler) models built in, further reducing the complexity of your pipeline: https://cloud.google.com/bigquery/docs/bigqueryml-intro
If you are not on GCP, then I’d consider AWS Athena for querying the parquet files, but you still have to structure these efficiently beforehand.