Xuewei Zhang is a software engineering manager with 10 years of experience building observability and APM backends, currently leading the APM product backend after Observe was acquired by Snowflake. He has a strong track record delivering high-impact features—service explorer, monitors, Kubernetes explorer, and native OTel histogram support—while running small, fast teams that stay tightly customer-focused. Technically grounded in distributed systems and kernel-level debugging from his time at Google, he’s known for resolving deep production incidents and designing performant metric and query systems. His open-source contributions include improving Kubernetes node-problem-detector with Prometheus and system metrics support and dynamic daemon registration, reflecting hands-on DevOps and backend expertise. Xuewei combines product-facing leadership with low-level troubleshooting instincts and a penchant for ambitious architecture changes that de-risk future platform work. Based in Sunnyvale, he mentors engineers across domains and continually pushes cross-cutting improvements in observability and monitoring.
10 years of coding experience
8 years of employment as a software developer
High School Diploma, High School Diploma at Tianjin No.1 High School
Bachelor of Science - BS Physics, Bachelor of Science - BS Physics at Peking University
studied towards M.S. Electrical and Computer Engineering, studied towards M.S. Electrical and Computer Engineering at University of Illinois Urbana-Champaign
This is a place for various problem detectors running on the Kubernetes nodes.
Role in this project:
Back-end Developer & DevOps Engineer
Contributions:4 reviews, 52 commits, 36 PRs in 9 months
Contributions summary:Xuewei primarily focused on updating dependencies within the `kubernetes/node-problem-detector` repository, specifically upgrading `golang.org/x/sys/unix` and `github.com/golang/protobuf/proto`. The user also integrated new dependencies such as `go.opencensus.io` and `github.com/shirou/gopsutil` to enable Prometheus and system stats metrics. Furthermore, the user implemented dynamic registration and initialization for problem daemons and integrated new exporters, which involved modifying CLI options and core problem detector logic.
This is a place for various problem detectors running on the Kubernetes nodes.
Contributions:215 pushes, 25 branches in 11 months
placepythonnodesmachine-learningdetectors
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Xuewei Zhang - Software Engineering Manager at Snowflake