Stephen Haberman is an experienced engineer with 18 years building and scaling full-stack systems across startups and large enterprises, currently based in the Omaha metro area. He has led engineering teams and rewrites of revenue-driving front-end infrastructure at LinkedIn, and later continued hands-on engineering at companies like Remind and Homebound. His work spans backend data platforms (Hadoop, Kafka, Pinot) to front-end engineering, and he’s comfortable owning the full stack from batch jobs to JS UIs. An active open-source contributor, he’s implemented core features in projects ranging from a TypeScript Protobuf generator (ts-proto) to improvements in the React virtual list library and contributions to Apache Spark’s RDD APIs. Known for mentoring and fostering a culture of quality-first engineering, he combines management experience with deep technical craftsmanship. He holds degrees from the University of Nebraska at Omaha and Carnegie Mellon, blending practical delivery with formal software-engineering training.
17 years of coding experience
14 years of employment as a software developer
Bachelors, Computer Science, Bachelors, Computer Science at University of Nebraska at Omaha
Masters, Information Technology, emphasis in Software Engineering, Masters, Information Technology, emphasis in Software Engineering at Carnegie Mellon
Contributions:246 releases, 351 reviews, 564 commits in 4 years
Contributions summary:Stephen appears to be focused on developing the back-end functionality for a Protobuf generator for TypeScript. They implemented core features like prefixing nested messages, creating decode and encode functions, and handling lists of messages. Their work extended to supporting various Protobuf features, including handling string enums, and the implementation of a client-side API with features like deep partial types.
Lightning-fast cluster computing in Java, Scala and Python.
Role in this project:
Back-end Developer
Contributions:107 commits, 1 PR in 8 months
Contributions summary:Stephen contributed to core functionality within the Spark project, focusing on the PairRDDFunctions and RDD classes. Their commits added new methods for common operations like keys, values, collect, and keyBy to enhance the RDD API. They also performed refactoring and bug fixes, improving the reliability and usability of the Spark codebase, in addition to merging and refactoring code. These contributions centered on expanding and refining core data processing capabilities.
pythoncluster-computinglightningsparkscala
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