Min Xia is a senior software engineer with 11 years of experience building cloud-native observability and backend systems, currently at AWS in Seattle. He has deep hands-on expertise in OpenTelemetry—contributing to both the core collector and the widely used collector-contrib repo—where he improved AWS EMF exporting, metric batching, timestamp accuracy, and resource-to-telemetry conversions to optimize performance and cost. Previously he delivered scalable services at Audible and other firms, and he combines backend development with DevOps skills such as build automation and cross-platform packaging (notably for the AWS Distro for OpenTelemetry). Min pairs pragmatic engineering with a focus on telemetry correctness and memory/resource management, making him adept at hardening production observability pipelines.
11 years of coding experience
12 years of employment as a software developer
Master's degree, Computer Science, Master's degree, Computer Science at Kennesaw State University
AWS Distro for OpenTelemetry Collector (see ADOT Roadmap at https://github.com/orgs/aws-observability/projects/4)
Role in this project:
Back-end & DevOps Engineer
Contributions:9 releases, 296 reviews, 137 commits in 1 year 1 month
Contributions summary:Min's initial contributions focused on setting up the AWS Distro for OpenTelemetry (ADOT) Collector. Their work included integrating OTel Collector v0.7.0, creating build scripts for various platforms, and providing a starter script. They added default support for AWS endpoints, indicating a focus on cloud integration. Further commits addressed issues related to Windows MSI, and various enhancements and fixes to deployment configuration files.
Contrib repository for the OpenTelemetry Collector
Role in this project:
Back-end Developer
Contributions:230 reviews, 14 commits, 31 PRs in 11 months
Contributions summary:Min primarily worked on improving the AWS EMF exporter within the OpenTelemetry Collector Contrib repository, focusing on handling metrics and translating them into a format compatible with AWS CloudWatch Logs. They implemented code changes to handle instrumentation library dimensions, ensuring correct metric dimensioning. Additionally, the user refactored the EMF exporter to support metric batching, improving performance and reducing costs, and corrected the timestamp handling to ensure accurate metric data representation. Furthermore, they improved the support for aws.local.service and aws.remote.service.
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