A Data Parallelism developer designs and implements software solutions that allow tasks to be split and run simultaneously across multiple processors or computing nodes. They leverage data parallelism to speed up processing times for large datasets. This involves partitioning data into smaller chunks, distributing it across various computing resources, and running computations in parallel. They are also responsible for ensuring data consistency and managing communication between processors. Understanding of parallel computing architectures, multi-threading, and synchronization is crucial. They often work with big data technologies and need to be proficient in programming languages like Python, Java, or C++.