Full-stack engineering · Django · Python
Software Development
Full-stack Python and Django engineering for products where reliability, clear data models and a calm delivery process matter.
I build and maintain full-stack products, mostly with Python and Django on the server and Vue, React or plain, well-behaved HTML in the browser. My work has tended to land in places where downtime is expensive and data has consequences: clinical platforms, railway engineering, and agricultural decision support.
The short version: I like clear data models, small releases, and code that a new teammate can read without a guided tour.
What I bring
Django, done properly
Domain modelling, permissions, background jobs, APIs and the unglamorous work of making a codebase pleasant to change. PostgreSQL and PostGIS are home turf.
Interfaces without ceremony
Vue and React where they earn their keep, server-rendered HTML everywhere else. Accessible, responsive, and tested against real workflows rather than happy paths.
CI/CD and test-driven habits
Automated tests, repeatable deployments and environments that do not depend on one person remembering the magic command. Boring infrastructure is a feature.
Remote teams that ship
Leading distributed developers, running useful reviews, and replacing status meetings with written context and small, demonstrable increments.
Selected work
Clinical workflow platform
A patient-facing and internal platform for a medical organisation. I worked across the Django backend, the Vue frontend and the deployment pipeline, with particular attention to audit trails, role-based access and safe handling of sensitive records.
Survey project management and web-GIS
Backend systems that kept large surveying projects moving, plus browser-based maps for railway engineering work. The maps became the shared reference point between field crews, engineers and clients.
Crop modelling and yield prediction
Services that disseminated crop-model output to growers and agronomists, including a decision-support layer that turned model runs into practical recommendations.
Geospatial data pipelines
Ingestion, validation and publication of field and satellite data: scheduled jobs, tiling, caching, and APIs that keep map clients responsive when the data gets large.
Toolbox
How I work
- Understand the constraint. Every project has one: a deadline, a legacy database, a regulatory rule, or a user who works offline half the week. I start there.
- Model the data before the screens. Good boundaries make the rest of the build cheaper. Bad ones follow you into every sprint.
- Keep the loop short. Small changes, clear commit messages, tests around the behaviour that matters, and a demo at the end of it.
- Leave it legible. Documentation, sensible defaults and an honest README are part of the deliverable.
Software is a team sport played over time. Optimise for the person who inherits the repository, because that person will often be you.
Let's talk
I'm currently open to backend, geospatial and data-heavy product work — remote, or based around Wrocław. If you have a problem that needs a steady pair of hands, send me an email or find me on LinkedIn.