Daniel Janicek
QA Engineer at Weaviate
Greater Seattle Area United States
Summary
🤩
Rockstar🎓
Top SchoolDaniel Janicek is a QA Engineer with three years of focused experience building test automation and infrastructure for cloud-native, containerized systems in the Greater Seattle Area. He has hands-on expertise in Kubernetes, CI/CD optimization, and end-to-end test frameworks—having cut Pachyderm’s Kubernetes test runtime from 2.5 hours to 45 minutes and created Playwright frameworks from scratch. His background spans SDET and delivery engineering roles at companies like Pachyderm (now HPE), NGINX, and Allstate, where he integrated observability (DataDog), performance testing (Locust, Gatling), and API/messaging test frameworks. Daniel contributes to open-source data-centric pipeline work (pachyderm), demonstrating backend, DevOps, and authentication testing chops in real-world distributed systems. Trained in both mathematics and philosophy, he brings analytical rigor and a systems-thinking approach to solving complex technical and non-technical problems. Colleagues rely on him to shrink feedback loops and make test infrastructure a scalable part of delivery.
3 years of coding experience
10 years of employment as a software developer
Bachelor of Arts (B.A.) Philosophy, Bachelor of Arts (B.A.) Philosophy at James Madison University
German
Github Skills (11)
kubernetes10
go10
back-end-development10
kubernetes-pods10
testing10
dockers9
user-authentication9
distributed-systems9
container9
authentication9
docker9
Programming languages (5)
Github contributions (5)
pachyderm/pachyderm
Oct 2022 - Jan 2023
Data-Centric Pipelines and Data Versioning
Role in this project:
Back-end Developer & DevOps Engineer
Back-end Developer & DevOps EngineerContributions:195 reviews, 89 commits, 162 PRs in 3 months
Contributions summary:Daniel's contributions primarily focused on back-end development tasks, specifically addressing PPS (Pachyderm Pipeline System) functionality. Their work included removing duplicate environment variables and modifying worker pod specifications, indicating an understanding of Kubernetes and related infrastructure. Furthermore, the user implemented and tested aspects of authentication, including test cases and integration within the codebase. These activities reveal a combined skill set in back-end development, system administration, and testing within a containerized, data-centric pipeline environment.
data-versioninggopachydermdockeranalytics
determined-ai/determined
Jul 2023 - Dec 2024
Determined is an open-source machine learning platform that simplifies distributed training, hyperparameter tuning, experiment tracking, and resource management. Works with PyTorch and TensorFlow.
Contributions:133 reviews, 35 PRs, 418 pushes in 1 year 5 months
distributed-traininghyperparameter-tuningmachine-learningpytorchtensorflow
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