Data infrastructure, cloud-native software, analytics systems, and machine learning workflows for operational and scientific environments.
I build technical systems that connect data engineering, software development, spatial infrastructure, and applied machine learning into production-ready platforms. My work spans SaaS products, analytics pipelines, geospatial systems, operational tooling, scientific modeling workflows, and cloud-native application infrastructure.
- Cloud-native software platforms using APIs, background workers, object storage, authentication systems, and scalable deployment patterns.
- Data infrastructure and analytics engineering for operational reporting, automation, forecasting, and decision support.
- Machine learning workflows including training pipelines, inference systems, reproducible experimentation, and deployment architecture.
- Spatial and scientific systems involving PostGIS, raster/vector processing, remote sensing workflows, and geospatial APIs.
- Product engineering for internal tools, dashboards, field workflows, SaaS applications, billing systems, and operational platforms.
- Technical communication through dashboards, documentation, architecture diagrams, reproducible examples, and analytical reporting.
| Repository | What it demonstrates |
|---|---|
harborsystems-cloud-geospatial-platform |
Cloud-native SaaS architecture, APIs, spatial infrastructure, team workflows, and scalable data delivery |
fafo-mobile-app-case-study |
Mobile application architecture, offline-aware workflows, mapping systems, and operational field tooling |
captainshq-saas-case-study |
SaaS product engineering, customer workflows, payments, PWA architecture, and operational systems |
analytics-pipeline-examples |
Automated analytics workflows, forecasting systems, reporting pipelines, and reproducible modeling |
geospatial-ml-demo |
Spatial machine learning workflows, feature engineering, spatial validation, and deployable ML outputs |
postgis-spatial-data-template |
PostGIS schema design, ingest pipelines, indexing strategies, and spatial query patterns |
dashboard-platform-template |
Analytics dashboards, operational interfaces, deployable frontend patterns, and reporting systems |
Python, R, TypeScript, SQL, FastAPI, Django, Next.js, React, PostgreSQL, PostGIS, Docker, Google Cloud, Cloud Run, Vercel, object storage, APIs, background workers, ML workflows, GeoPandas, Rasterio, Xarray, PyTorch, TensorFlow, XGBoost, Stan, brms, mgcv, Mapbox, Leaflet, and QGIS.
Good technical systems are operationally practical, maintainable under real-world conditions, and understandable to the people making decisions with them. I care about infrastructure and software that move cleanly from raw data to reliable analysis, usable interfaces, and scalable operational workflows.