Asks for

The ad says“Data analysis & EDA — Explore datasets, identify patterns and anomalies, validate assumptions and help engineers understand what is happening in the data.”
The ad says“Data visualization — Build clear technical visualizations using tools such as matplotlib, Plotly or similar libraries to communicate results and support engineering decisions.”
The ad says“Containerize development workflows — Use Docker to create reproducible development and execution environments.”
The ad says“Strong working knowledge of Git, including branches, pull requests, merge workflows and collaborative code review.”
The ad says“Comfortable working with Jira for task tracking, sprint planning and engineering workflows.”
The ad says“Data visualization — Build clear technical visualizations using tools such as matplotlib, Plotly or similar libraries to communicate results and support engineering decisions.”
The ad says“Work with scientific Python — Use libraries such as NumPy, Pandas and SciPy to process and analyse numerical and time-series data.”
The ad says“Strong understanding of object-oriented programming in Python and confidence creating, extending and working with classes.”
The ad says“Work with scientific Python — Use libraries such as NumPy, Pandas and SciPy to process and analyse numerical and time-series data.”
The ad says“Data visualization — Build clear technical visualizations using tools such as matplotlib, Plotly or similar libraries to communicate results and support engineering decisions.”
The ad says“We are looking for a Python Engineer with 3–5 years of hands-on experience building Python projects.”
The ad says“Work with scientific Python — Use libraries such as NumPy, Pandas and SciPy to process and analyse numerical and time-series data.”
The ad says“Comfortable using Slack as a primary collaboration and communication tool within an engineering team.”

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 creating technical visualizations with matplotlib, Plotly or similar tools.
Strong working knowledge of Git, including branches, pull requests, merge workflows and collaborative code review.
Hands-on experience with Docker, including building and running containers and using containerized development environments.
Comfortable working with Jira for task tracking, sprint planning and engineering workflows.
Comfortable using Slack as a primary collaboration and communication tool within an engineering team.
Ability to write clear, readable and maintainable production-quality Python code.
Comfortable working in an environment where requirements evolve through experimentation and engineering discovery.
Strong problem-solving skills and the ability to work independently while collaborating with engineers from different technical disciplines.

Nice to have:

Experience working on Data Science or Machine Learning projects.
Familiarity with scikit-learn, PyTorch or similar ML libraries.
Experience processing large numerical, sensor, time-series or signal datasets.
Experience supporting researchers or ML engineers by turning notebooks and prototypes into reusable software.
Familiarity with testing Python applications using tools such as pytest.
Experience with performance optimisation or vectorised numerical processing.
Previous experience in deep-tech, robotics, autonomous systems, radar, signal processing or another R&D-heavy environment.

What We Offer:

Impact
Build the Python infrastructure that engineers and researchers use to process, analyse and understand real-world data. Your work directly improves how quickly the team can experiment, validate ideas and move technology forward.
Engineering Challenges Worth Solving
Work with complex datasets and technical problems where clean software engineering has a direct impact on R&D speed and product performance.
A Team You'll Learn From
Collaborate closely with signal-processing engineers, AI/ML engineers, scientists and other specialists working across software and hardware.
Ownership & Autonomy
We're looking for someone who can see an experimental Python workflow and think beyond the immediate script — how should this be structured, reused, tested and maintained as the system grows? You'll have the autonomy to improve the architecture, introduce better engineering practices and build tools that become part of the team's everyday workflow.