Peter Goetz
Software Development Engineer at Amazon Web Services (AWS)
Stuttgart, Baden-Württemberg, Germany
Summary
🤩
Rockstar🎓
Top SchoolPeter Goetz is a Software Development Engineer with 16 years of experience building resilient backend systems and cloud-native tooling, currently at AWS in Stuttgart. He has a strong track record across major cloud and enterprise teams—including Cloud Foundry, IBM, Pivotal, and Amazon—working on API design, acceptance testing, and production-grade services. An active open-source contributor, he has improved Cloud Foundry controllers and acceptance tests and extended the causal-inference Python library DoWhy with graphical model interventions and Shapley estimation features. His background spans hands-on DevOps, backend engineering, and ML-focused contributions, reflecting a pragmatic blend of reliability engineering and research-minded feature work. Colleagues know him as a "Quality Pirate" who systematically hardens test frameworks and documentation to reduce surprises in production. He brings both startup founder experience and large-company delivery discipline, enabling fast iteration without sacrificing correctness.
15 years of coding experience
16 years of employment as a software developer
Diploma, Diploma at Universität StuttgartUniversity of Massachusetts Dartmouth
German, English, French, Spanish
Stackoverflow
99reputation
506kreached
3answers
0questions
Github Skills (22)
causal10
cloud-foundry10
python10
apidoc10
testing10
machine-learning10
test-framework10
causality10
cicd10
causal-inference10
ruby10
go10
api10
pytest9
web-deploy9
Programming languages (13)
Github contributions (5)
py-why/dowhy
May 2022 - Jan 2023
DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language for causal inference, combining causal graphical models and potential outcomes frameworks.
Role in this project:
ML Engineer
ML EngineerContributions:248 reviews, 127 commits, 101 PRs in 8 months
Contributions summary:Peter contributed significantly to the `dowhy` repository by implementing and testing new features related to causal inference using graphical causal models (GCMs). They added a GCM intervention feature, functionality for Shapley value estimation, and distribution change attribution capabilities, demonstrating a focus on extending the library's core functionalities for causal analysis. The commits include code changes to both existing and new Python files, accompanied by unit tests to ensure the accuracy of these novel methods.
fairness-mlcausal-modelspythoncausalbayesian-networks
cloudfoundry/cf-acceptance-tests
Apr 2016 - Feb 2019
CF Acceptance tests
Role in this project:Back-end & DevOps Engineer
Back-end & DevOps EngineerContributions:7 commits, 4 PRs, 3 pushes in 2 years 10 months
Contributions summary:Peter's contributions primarily involved improving the cloud foundry acceptance tests. They modified existing tests to use v3 endpoints and address issues related to instance termination, indicating backend testing skills. Additionally, the user addressed issues related to download and redirect mechanisms. Their work included implementing error handling and increasing the resilience of the testing framework, demonstrating DevOps and testing capabilities.
testingacceptance-testscff-wg-app-runtime-deploymentsacceptance
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