Dimensional Modeling and ETL in Redshift
You are definately on the right track with Kimball rather than inmon for Redshift.

There are a number of patterns for this, I have used them all in different use cases


"ELT" pattern - Load the source tables to redshift fully, do not do any significant
transformations until the data has been loaded. For this you can
either load to s3, then use redshift copy command or I would
recommend using "AWS data migration services", which can sync a
source (e.g.mysql or postgres) to a target (e.g. redshift) Then, on
a regular basis run sql processes within redshift to populate dims
then facts. You can use third part cloud based tools to "simplify"
this process if you want to - such as Matillion (i do not recommend
using a third party tool)
"ETL pattern" - Transform the data in flight, using apache spark. and load the dims and facts into redshift spark->s3->redshift. I have used EMR for this which is good. this is also the approach taken if you use AWS Glue
Do not transform ! - similar to 1) but just use the tables that have
been loaded.


Note that Redshift sometimes works BETTER if you have a wide table with repeated values rather than a fact and  dimensions. The reason for this is that the columnar approach lets Redshift compress the different values down to a level that is pretty efficient. I do not have a formula for when to use many Dimensions vs a flat wide table, the only way is to try it and see!

Some links

AWS DMS for Redshift taret

AWS Glue