Jules Henze
Software Engineer at Ledgy
Zurich, Zurich, Switzerland
Summary
👤
Senior🎓
Top SchoolJules Henze is a Staff Software Engineer based in Zurich with 11 years of experience building scalable, data-driven systems using Python and TypeScript. With a Master's in Environmental Engineering from ETH Zurich, he uniquely bridges domain expertise in hydrology and remote sensing with production-grade software—having built operational pipelines for snow cover and runoff forecasting and advised climate-resilient water-management projects. At Ledgy he focuses on backend engineering, and his open-source work includes improving NDVI processing, cloud masking, and search efficiency for Landsat imagery in the widely used developmentseed/landsat-util project. Practical field experience—from launching monitoring initiatives in Kenya to hands-on solar installation—underscores his emphasis on deployable, impact-oriented solutions. Colleagues describe him as a pragmatic engineer who turns complex environmental requirements into reliable, automated services.
11 years of coding experience
4 years of employment as a software developer
Master’s Degree, Environmental Engineering Technology/Environmental Technology, Master’s Degree, Environmental Engineering Technology/Environmental Technology at Eidgenössische Technische Hochschule ZürichMatura, Matura at Kantonsschule Kreuzlingen
Github Skills (13)
rasterio10
spatial-data-analysis10
geospatial10
spatial-analysis10
python10
image-processing10
geospatial-analysis10
matplotlib9
data-visualisation9
data-visualization9
data-visualizations9
sens8
git6
Programming languages (5)
Github contributions (5)
developmentseed/landsat-util
Jul 2015 - Jul 2015
A utility to search, download and process Landsat 8 satellite imagery
Role in this project:
Back-end Developer & Data Scientist
Back-end Developer & Data ScientistContributions:15 commits, 1 PR in 8 days
Contributions summary:Jules significantly contributed to the development of NDVI (Normalized Difference Vegetation Index) functionality within the landsat-util project. Their work included adding options for grayscale and color NDVI output, including a colorbar. They also implemented cloud masking based on QA band data, improving the accuracy of NDVI calculations. Furthermore, the user enhanced the latest search feature, optimizing date range searches and addressing potential database search inefficiencies.
satellite-imagerysatelliteearth-observationsatellite-dataimagery
hydrosolutions/MODISdata-server
Feb 2018 - Jun 2019
Contributions:93 pushes, 5 branches in 1 year 4 months
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