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 — Collaborate closely with signal-processing, radar and AI/ML engineers to translate experimental requirements into reliable Python tooling and pipelines.
What we’re looking for:
3–5 years of professional experience working with Python, ideally on engineering, scientific, data-intensive or R&D projects.
Strong practical Python skills — you should be comfortable building complete projects rather than only writing standalone scripts.
Strong understanding of how to structure a Python codebase using modules, packages, utilities and reusable libraries.
Ability to design and develop multi-step data-processing pipelines with clear inputs, transformations and outputs.
Strong understanding of object-oriented programming in Python and confidence creating, extending and working with classes.
Experience working with numerical or analytical data using libraries such as NumPy, Pandas and/or SciPy.
Strong EDA skills — ability to investigate datasets, identify patterns, anomalies and data-quality issues, and communicate findings.
Experience
