Our client is a European deep-tech startup developing next-generation radar systems for the detection and tracking of small, low-flying drones.

The team works across radar architecture, RF hardware, signal processing, embedded systems, AI and data-processing software. Engineers have real ownership of technical decisions and a direct impact on system performance and product capabilities.

This is a hands-on R&D environment for engineers who enjoy working with complex technical data, building reliable software tooling, and turning experimental workflows into structured, reusable systems.

About the role:

We are looking for a Python Engineer with 3–5 years of hands-on experience building Python projects.

Your focus will be on designing and developing structured Python pipelines used for data processing, analysis, experimentation and visualization.

This is not a role for someone who has mainly used Python for isolated scripts. We are looking for an engineer who knows how to take a growing collection of experiments and processing logic and turn it into a clean, maintainable Python codebase with reusable utilities, libraries, classes and well-defined pipelines.

You'll work closely with engineers and researchers working on signal processing, radar and AI/ML, helping them process, explore and understand complex datasets efficiently.

What you'll do:

• Build Python pipelines — Design, implement and maintain modular pipelines for loading, processing, transforming, analysing and exporting technical data.

• Structure Python projects — Turn experimental scripts and notebooks into maintainable packages with clear modules, utilities, libraries and interfaces.

• Develop reusable components — Build internal utilities and libraries that can be reused across experiments, datasets and engineering workflows.

• Object-oriented development — Design and work confidently with Python classes, inheritance/composition where appropriate, interfaces and reusable abstractions.

• Data analysis & EDA — Explore datasets, identify patterns and anomalies, validate assumptions and help engineers understand what is happening in the data.

• Data visualization — Build clear technical visualizations using tools such as matplotlib, Plotly or similar libraries to communicate results and support engineering decisions.