1. A compiler. The actual algorithms and datastructures might not be all that interesting (or they might be if you're really interested in that sort of thing), but the kinds of transformations you're doing from stage to stage are sophisticated.
2. An analytics pipeline. If you're working in the Spark/Scala world, you're writing high-level functional code that represents the transformation of data from input to output, and the framework is compiling it into a distributed program that loads your data across a cluster of nodes, executes the necessary transformations, and assembles the results. In this case there is a ton of stateful I/O involved, all interleaved with your code, but the framework abstracts it away from you.