Enrico Minack
Software Engineer
Hanover, Lower Saxony, Germany
Summary
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RockstarEnrico Minack is a seasoned software engineer with 16 years of experience, based in Hanover, Germany, who blends backend engineering and DevOps to improve reliability and developer workflows. He is an active open-source maintainer and contributor—most notably for LF AI Horovod and Apache Spark—focused on testing infrastructure, CI/CD, and observable metrics for large-scale data and ML systems. His work spans practical improvements like test robustness and GitHub Actions for publishing unit test results to UX-focused enhancements in Spark’s SQL query plan visualization. Enrico’s contributions often sit at the intersection of distributed training, Spark integration, and CI resilience, reflecting deep familiarity with MPI, ElasticSearch, and dataset observation patterns. He gravitates toward projects that make data and AI stacks more maintainable and testable, and he has a track record of simplifying complex APIs (e.g., a Scala Observation helper) to make metrics easier to extract. Pragmatic and detail-oriented, he prefers durable engineering wins that reduce flakiness and speed developer feedback loops.
16 years of coding experience
Github Skills (49)
unit-testing10
github-ci10
spark10
pytest10
python10
amazon-elasticsearch10
testing10
net10
mpi10
big-data10
machine-learning10
dotnet10
metadata10
java10
scala10
Programming languages (17)
Github contributions (5)
EnricoMi/publish-unit-test-result-action
Aug 2020 - Jan 2023
GitHub Action to publish unit test results on GitHub
Role in this project:
QA Engineer / Test Automation Engineer
QA Engineer / Test Automation EngineerContributions:65 releases, 107 reviews, 500 commits in 2 years 5 months
Contributions summary:Enrico primarily contributed to the development of a GitHub Action designed for publishing unit test results, demonstrated by the provided commit messages and code changes. Their work involved writing code in Python to parse JUnit XML files, aggregate test statistics, and publish results as check runs, issue comments, and summaries. The user modified the tool to better highlight tests that were skipped, and failures, providing enhanced readability for test reports.
publishreportingtestingunittest-results
horovod/horovod
Nov 2019 - Jan 2023
Distributed training framework for TensorFlow, Keras, PyTorch, and Apache MXNet.
Role in this project:Back-end Developer & DevOps Engineer
Back-end Developer & DevOps EngineerContributions:5 releases, 307 reviews, 349 commits in 3 years 2 months
Contributions summary:Enrico primarily contributed to the `horovod/horovod` repository by refactoring the `horovod.spark.run()` functionality, indicating involvement with the Spark integration. Their work included modifications to testing code, ensuring proper invocation of `mpi_run` within the Spark environment, and addressing potential issues with environment variables within the Spark worker. The commits demonstrate experience with MPI, Spark, and testing frameworks.
mpikeras-tensorflowtrainingbaidutensorflow
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