Marion Lang
Principal Software Engineer at GitHub
San Francisco, California, United States
Summary
👤
Senior🎓
Top SchoolMarion Lang is a Principal Software Engineer in San Francisco with 13 years of experience building high-throughput distributed systems and production-grade developer tooling. She brings a rare blend of academic rigor and industry impact—former tenure-track CS professor who redesigned curricula and led DEI efforts, and a Google engineering lead/visiting researcher who optimized the Dataflow stream processing engine. Marion has practical open-source chops, having improved the Google Cloud Dataflow Java SDK (gzip/bzip2 support, custom Sink API, and robust GCS/Datastore handling) and has worked at Mozilla and GitHub on backend reliability and scalability. Comfortable translating research into production, she focuses on low-latency, fault-tolerant architectures and mentoring engineering teams.
12 years of coding experience
15 years of employment as a software developer
Bachelor of Science (BS), Computer Science, Bachelor of Science (BS), Computer Science at Indiana University of PennsylvaniaDoctor of Philosophy (Ph.D.), Computer Science, Doctor of Philosophy (Ph.D.), Computer Science at The Ohio State University
Github Skills (13)
google-cloud-storage10
javas10
storage10
google-cloud-platform10
dataflow-programming10
dataflow10
cloud-object-storage10
google-cloud10
java10
gcp10
avro9
api8
apidoc8
Programming languages (8)
Github contributions (5)
GoogleCloudPlatform/DataflowJavaSDK
Jan 2015 - Oct 2015
Google Cloud Dataflow provides a simple, powerful model for building both batch and streaming parallel data processing pipelines.
Role in this project:
Back-end Developer
Back-end DeveloperContributions:27 commits in 8 months
Contributions summary:Marion's contributions focused on enhancing the functionality and reliability of the Google Cloud Dataflow Java SDK. They implemented support for compressed text sources (gzip and bzip2), fixed a NullPointerException in the CoGroupByKey transform, and optimized GCS file reading by caching Content-Encoding and always requesting full files when handling gzip Content-Encoding. They also added a custom Sink API and corresponding Write transform and improved the Datastore Sink, including exponential backoff for write retries and ensuring at least one split.
stream-processingbeambatchdata-processingparallel
Rhodes-CS-Department/jupyterhub-deployment
Dec 2020 - Jan 2023
Configs and READMEs for Rhodes CS JupyterHub deployment
Contributions:1 review, 118 commits, 23 PRs in 2 years 1 month
jupyterlab-extensiondeploymentconfigsjupyterlabjupyter
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