My team has found this drastically easier than Quilt, and we do a ton of stuff with reproducible environments in Docker, creating Makefiles to reproduce exact model training with the exact same data, etc. We probably hit just about every case there is (huge models, small models, models where we'd like to train separately or collectively on a bunch of different benchmark data sets, in-house data sets, models that need to be refreshed with new data in pipelines, etc.) So far, Quilt has not been competitive with a simple repo of shell scripts for us, in terms of ease of use or effectiveness in maintaining different packages of data.
The other super nice thing is that when people start out on new models or experiments, we already have our in-house maintained copies of a bunch of academic data sets, private data sets, etc., and you can throw together an incredibly simple Dockerfile or Makefile that uses the appropriate script. It's just one or two lines of shell code and voila, you have an environment with the dataset you want. Check that into git and now your experiment is immediately reproducible from day one. We've found this to dramatically increase the amount of code review that researchers engage in for checking their statistical methodology and sanity checking their intended models or experiments. With Quilt, you have the extra issue of versioning (rather than harshly enforcing all data sets to be immutable ... even just adding one more training example to the data set means you must provide a new shell script that downloads the old data, injects your lone additional sample, and has a documentation entry about exactly what it is doing), as well as the overhead of using yet another tool instead of super standard shell scripts.
For me, any of the tools that pop up attempting to be like conda-forge but for data packages is sort of like taking a gatling gun to a problem that can be solved with a hammer.