Craig Condit
Apache YuniKorn PMC at The Apache Software Foundation
Sioux Falls, South Dakota, United States
Summary
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Rockstar🎓
Top SchoolCraig Condit is a seasoned software engineer and architect with over 19 years of hands-on experience and a 25+ year career footprint across large-scale retail, internet, finance, and transportation systems. He is a driving force in the Apache YuniKorn community—PMC member and key contributor—where his backend work on scheduler refactoring, node sorting, and resource weighting has materially improved performance and reliability for Kubernetes workloads. At Cloudera and Target he designed and delivered production-grade, zero-downtime big data platforms at massive scale, with a pragmatic focus on stability, observability, and operational simplicity. Equally fluent in JVM and cloud-native stacks, Craig blends deep systems knowledge (Hadoop, Kafka, Spark) with practical engineering leadership and mentoring. Based in Sioux Falls, he brings a rare combination of long-term systems experience and current open-source influence that accelerates team productivity and platform resilience.
19 years of coding experience
24 years of employment as a software developer
BS Computer Science, BS Computer Science at North Dakota State University
Stackoverflow
1reputation
0reached
1answer
0questions
Github Skills (13)
kubernetes10
go10
kubernetes-pods10
api-design9
algorithms9
data-structure9
rest-api9
algorithm9
data-structures9
testing8
maven6
raspberry-pi6
java6
Programming languages (7)
Github contributions (5)
apache/yunikorn-core
Aug 2021 - Dec 2022
Apache YuniKorn Core
Role in this project:
Back-end Developer
Back-end DeveloperContributions:4 releases, 402 reviews, 42 commits in 1 year 4 months
Contributions summary:Craig's commits primarily focus on refactoring and improving the core functionality of the Apache YuniKorn scheduler. They refactored node sorting policies into separate implementations, enhancing the scheduling performance. The user implemented resource weighting for node sorting, which enabled more flexible and configurable node selection. Furthermore, they made improvements to the application state management and REST API to include application requests and improved the overall reliability and performance.
universal-resource-scheduleryunikornapacheapache-yarnkubernetes
craigcondit/yunikorn-scheduler-interface
Jan 2022 - Mar 2025
Apache Yunikorn Common scheduler interface - Incubating
Contributions:32 pushes, 47 branches, 1 tag in 3 years 2 months
incubatingscheduleryunikornapachejava
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