David Ohana
Senior Software Engineer Technical Lead at NVIDIA
Israel
Summary
👤
Senior🎓
Top SchoolDavid Ohana is a Senior Software Engineer and technical lead with nine years of post-academic experience (and two decades of hands-on development) building scalable, production-grade systems across cloud, search, and defense domains. Currently at NVIDIA after leading search-growth projects and Kotlin mentorship at Google, he combines deep backend expertise with a proven track record in AIOps, log-driven failure detection, and full-stack feature delivery. At IBM he designed from-scratch systems that detect failures in large cloud deployments, and his open-source contributions include significant refactors to the popular Drain3 streaming log template miner to improve robustness, persistence, and delimiter handling. Comfortable both leading teams and shipping code, he has a history of introducing modern practices—CI/CD, pub/sub, DevOps tooling—and mentoring engineers to raise craft and reliability.
9 years of coding experience
18 years of employment as a software developer
Bachelor of Science (BSc), Computer Science, 85.1, Bachelor of Science (BSc), Computer Science, 85.1 at Technion - Israel Institute of Technology
English, Hebrew
Stackoverflow
1,840reputation
825kreached
33answers
23questions
Github Skills (18)
python10
machine-learning10
log-parser10
parse10
clustering10
anomaly-detection9
configuration-management9
persistence9
kafka8
logging6
dockerfile6
python-logging6
grafana6
docker6
debian6
Programming languages (9)
Github contributions (5)
logpai/Drain3
Mar 2020 - Sep 2022
A robust streaming log template miner based on the Drain algorithm
Role in this project:
Back-end Developer
Back-end DeveloperContributions:23 releases, 57 reviews, 154 commits in 2 years 6 months
Contributions summary:David primarily refactored and improved the core Drain3 log parsing algorithm. Their contributions involved modifications to core files, including `drain3/template_miner.py`, `drain3/drain.py`, and `drain3/kafka_persistence.py`. This work included state saving/loading, configuration management, and adding support for extra delimiters, indicating a focus on improving the robustness and functionality of the log parsing process. Furthermore, the user worked on adding file and match-only processing examples, showcasing expertise in leveraging Drain3 for practical log analysis tasks.
minertemplate-miningdrainaiopsanomaly-detection
IBM/HBPE
May 2020 - Sep 2020
Contributions:35 commits, 16 pushes, 1 issue in 4 months
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