I doubt this is the case. If you're working on even a medium sized team, you'll see this level of data, whether it's internal server logs, publicly-sourced data, or a variety of other applications. Almost by definition, any company that runs a cluster is probably dealing with TBs of data (otherwise they could probably run it on one machine). I don't know the percentage of developers that deal with this, but I do know that it's pretty common.
If you're literally processing TB or PB of data, you'll want to parallelise. Though shell tools do this amazingly well.
This doesn't follow:
1) One reason to run a cluster is to use flaky commodity hardware instead of high-reliability specialized hardware. Note the transition from specialized hardware and computer rooms of the '80s and '90s to cloud computing on preemptible instances.
2) They might just be straight-up wrong / misguided. http://www.frankmcsherry.org/graph/scalability/cost/2015/01/... As a professional developer I've seen tons of distributed systems that could be replaced by a well-designed non-distributed system, but ELK and Mongo and etcd and Kafka and more generally cloud servers are easy off-the-shelf tools.
You can get a lot of milage out of parallel xargs and other of the shelf tools.