Python Engineer

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. - Work with scientific Python — Use libraries such as NumPy, Pandas and SciPy to process and analyse numerical and time-series data. - Improve engineering quality — Refactor existing code, reduce duplication, introduce clear abstractions and help establish good practices around maintainability, testing and documentation. - Containerize development workflows — Use Docker to create reproducible development and execution environments. - Collaborate effectively — Work comfortably in an engineering workflow using Git for version control, Jira for task and sprint management, and Slack for day-to-day team communication. - Work with R&D teams —…