Michael Matz
Senior Software Engineer at General Atomics Integrated Intelligence
Charlottesville, Virginia, United States
Summary
👤
Senior🎓
Top SchoolMichael Matz is a Senior Software Engineer with over a decade building enterprise-class systems for clients ranging from commercial finance to the federal intelligence community. He combines deep backend and data-engineering skills—Java, Scala, C#, SQL—and hands-on experience with big-data and streaming stacks like Hadoop, Accumulo, Kafka, and GeoMesa to solve large-scale geospatial and telemetry problems. At General Atomics Integrated Intelligence he drives product vision and translates complex requirements into prioritized roadmaps while collaborating across engineering, architecture, and executive teams. His open-source contributions to GeoMesa show an attention to efficient serialization, attribute-aware query planning, and Kafka datastore replay—practical work that improves real-time spatial data pipelines. A Penn State computer science graduate with a minor in math, he pairs rigorous technical depth with a knack for turning research-grade technologies into production-ready solutions.
11 years of coding experience
11 years of employment as a software developer
Bachelor's degree, Computer Science, Bachelor's degree, Computer Science at Pennsylvania State University - Main Campus
English
Github Skills (8)
data-storage10
javas10
kafka10
geotools10
java10
scala10
data-pipelines9
data-pipeline9
Programming languages (2)
Github contributions (5)
locationtech/geomesa
Apr 2015 - Jun 2015
GeoMesa is a suite of tools for working with big geo-spatial data in a distributed fashion.
Role in this project:
Back-end Developer & Data Engineer
Back-end Developer & Data EngineerContributions:7 commits, 21 PRs, 90 comments in 1 month
Contributions summary:Michael contributed to the GeoMesa project by implementing features related to propagating user data during serialization of SimpleFeatures and adding retyping support to the KafkaConsumerFeatureSource. They also worked on improving query planning for multiple attribute queries, specifically optimizing the selection of attribute filters. The user's contributions demonstrate a focus on data handling, feature encoding, and data store integration within the GeoMesa ecosystem, including replay capabilities for the Kafka data store.
geomesageo-spatialgeospatialkuduaccumulo
mmatz-ccri/geomesa
Mar 2015 - May 2015
Contributions:2 PRs, 46 pushes, 17 branches in 1 month
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