Zita Dombi is a Senior Software Engineer with five years of experience specializing in distributed storage systems, currently contributing to Apache Ozone at Cloudera. With a Bachelor's and Master's in Computer Science from the University of Szeged and three years in a research group on linear algebra and network optimization, she blends academic rigor with production-grade engineering. Her work on integrating Hadoop HttpFS into Ozone, removing unnecessary dependencies and hardening secure integration tests, reflects a focus on pragmatic back-end improvements that enable scalable analytics and cloud-native workloads. A long-time intern-turned-committer at Cloudera, she’s comfortable navigating both low-level debugging (e.g., read-replicas tooling) and broader system integration. Based in Szeged, Hungary, she brings a thoughtful, research-informed approach to solving real-world distributed systems challenges.
4 years of coding experience
3 years of employment as a software developer
High School Diploma, Mathematics, High School Diploma, Mathematics at SZTE Gyakorló Gimnázium és Általános Iskola
Master's degree, Computer Science, Master's degree, Computer Science at University of Szeged
Scalable, reliable, distributed storage system optimized for data analytics and object store workloads.
Role in this project:
Back-end Developer
Contributions:371 reviews, 30 commits, 77 PRs in 1 year 5 months
Contributions summary:Zita's commits focus on integrating the Hadoop HttpFS module into the Ozone project, including copying the code, modifying code and removing unnecessary dependencies. The user also added a simple test suite for the HTTPFS gateway, and replaced the Hadoop shaded guava dependencies. Further commits involved moving code to the org.apache.ozone package, removing non-server side code, and fixing secure integration tests. These actions indicate a focus on enhancing the HttpFS gateway for Ozone.
Scalable, redundant, and distributed object store for Apache Hadoop
Contributions:220 pushes, 75 branches in 3 years 8 months
scalableredundantapachebig-dataspark
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