Michael Wallman
Technical Lead - Compute at Google
Berkeley, California, United States
Summary
🤩
Rockstar🎓
Top SchoolMichael Wallman is a technical lead specializing in compute and cloud infrastructure with 10+ years driving reliability and supportability at Google from Berkeley, CA. He combines hands-on engineering (early AWS consultant, SRE work, and tooling like a GCE "labelmaker" integrated with Terraform) with product-facing leadership to improve customer empathy across product and engineering teams. His background spans startups to enterprise—building production systems for media, telecom, and large-scale sensor networks—so he brings practical operational know-how to architectural decisions. At Google he progressed from hands-on TPM and consulting roles into technical leadership, focusing on making complex systems simpler and more robust. He has a knack for turning operational pain into automation and deployable tools, evidenced by contributions to GoogleCloudPlatform/professional-services. Trained at UC Berkeley in computer architecture and EECS, he pairs deep systems knowledge with a pragmatic, customer-first engineering mindset.
10 years of coding experience
14 years of employment as a software developer
CS61C - Great Ideas in Computer Architecture, CS61C - Great Ideas in Computer Architecture at UC Berkeley College of Engineering
English, Swedish
Github Skills (8)
infrastructure-as-code10
google-cloud-platform10
python10
gcp10
terraform9
terraformer9
json8
tooling7
Programming languages (6)
Github contributions (5)
GoogleCloudPlatform/professional-services
May 2017 - Mar 2019
Common solutions and tools developed by Google Cloud's Professional Services team. This repository and its contents are not an officially supported Google product.
Role in this project:
Cloud Engineer / Infrastructure Engineer
Cloud Engineer / Infrastructure EngineerContributions:22 commits, 11 PRs, 17 pushes in 1 year 10 months
Contributions summary:Michael primarily contributed to the "labelmaker" tool, a Python script designed to label Google Compute Engine instances and attached disks based on a JSON configuration file. Their work included implementing the core functionality of label application using the Google Compute Engine API and integrating the tool into a startup script, as evidenced by modifications to a startup script template. The user also updated the supporting infrastructure by creating Terraform configuration files to deploy the "labelmaker" tool and test instances.
gcpprofessionalgoogle-cloud-mlgoogledata-stream
mwallman/aws-big-data-blog
Dec 2015 - Sep 2016
Contributions:7 pushes in 8 months
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