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A leading mobility tech company in Hamburg is seeking a Data Engineer to build robust data pipelines and collaborate with diverse teams for the development of autonomous driving systems. The ideal candidate will have a background in data engineering or related fields, strong programming skills in Python and/or C++, and experience in handling multi-modal sensor data. This role offers a competitive salary, hybrid work setup, and opportunities for continuous learning and development.
Hamburg, Germany
As the Verification & Validation team, we ensure that autonomous driving systems are safe, reliable, and ready for the road. Our mission is to answer the critical question: "Is the system ready to deploy?"
We build and maintain the data foundations that power simulation, testing, and real-world validation—enabling engineers across the company to trust the data they work with.
We're a diverse group with backgrounds spanning robotics, gaming, scientific computing, aerospace, and automotive. Whether you come from autonomous systems, simulation engines, or high-performance data processing in other domains, we value the fresh perspectives that diverse experiences bring. Collaboration, curiosity, and the drive to solve complex problems are what unite us.
Build robust data pipelines
Design, build, and maintain high-performance data pipelines that ingest, synchronize, transform, and validate sensor data from simulation and real-world test fleets.
Work with robotics logging formats (MCAP, ROSbags, or similar): reading, writing, parsing, indexing, validating, and transforming log data at scale.
Process multi-modal sensor data including camera, LiDAR, radar, GPS, and vehicle bus data.
Handle timestamp synchronization, sensor alignment, and high- frequency data processing.
Build cloud-scale storage solutions and data architectures for large datasets.
Compare simulation data against real-world logs, ensuring consistency and evaluating data quality.
Detect anomalies, assess data robustness, and ensure dataset quality for machine learning, perception, and downstream teams.
Perform data preparation, metadata extraction, indexing, and quality assessment.
Interpret complex sensor logs, identify issues, and extract actionable insights.
Debug data issues across multiple sensors and sources.
Collaborate across teams
Work closely with Simulation, Perception, ML, and Systems teams to translate requirements into robust data solutions.
Support cross-functional teams with data-driven insights for test strategy optimization.
Share knowledge through documentation, presentations, and collaboration sessions.
Contribute to continuous improvement of data processes and methodologies.
You have a background in data engineering, robotics, scientific computing, simulation, or a related technical field—with hands-on experience building data pipelines for sensor-rich or high-throughput environments.
You have strong programming skills in Python and or C++, plus proficiency with Bash and Linux environments.
You have experience with timeseries logging formats used in robotics or simulation (such as MCAP, ROSbags, HDF5, or similar) and have built large-scale data pipelines for complex datasets.
You have worked with multi-modal sensor data or high-dimensional timeseries data. Experience with camera, LiDAR, radar, or similar sensor types is valuable—but equivalent experience from scientific instrumentation, gaming, or simulation is equally relevant.
You're familiar with cloud platforms (AWS, GCP, or Azure) and understand how to build scalable data architectures.
You have strong debugging skills and enjoy investigating complex data issues across multiple sources.
You're curious and analytical, with persistence when untangling intricate datasets. You don't just find problems—you understand root causes and build solutions that prevent them.
Nice to have:
Experience with ROS / ROS2 or other robotics middleware.
Background in simulation platforms (Unity, Unreal, CARLA, Gazebo, or similar).
Contributions to open-source robotics or data tooling.
Experience with SLAM, mapping, localization, or perception systems.
Familiarity with visualization tools such as Foxglove, RViz, or custom interfaces.
Experience with workflow orchestration (Airflow, Kubeflow, Prefect).
Experience with containerized environments (Docker, Kubernetes).
Understanding of safety standards (ISO 26262, SOTIF) or automotive regulations.
Knowledge of sensor fusion, calibration, or coordinate systems.
You're comfortable working in a multi-national team with occasional travel for meetings (e.g., PI Planning, ~1x/month). You value diverse perspectives and enjoy working in a hybrid environment where experimentation and learning are encouraged.
Most importantly, you're excited about building the data foundations that power safe autonomous driving.
We welcome applicants from diverse backgrounds— even if you don’t meet every requirement. If you’re excited about the role and MOIA’s mission, we’d love to hear from you!
For student & internship positions, we have an adjusted set of benefits. You can find them here .
We are a member of Charta der Vielfalt and are dedicated to actively fostering a workplace that celebrates and promotes diversity in various aspects such as age, gender identity, race, sexual orientation, physical or cognitive ability, and ethnicity. At MOIA, we embrace a culture where people are accepted, respected, valued, appreciated, and included.
In our commitment to promoting diversity and inclusivity, we regularly provide unconscious bias training to all our employees. Furthermore, we continuously strive to enhance our hiring process by ensuring a diverse hiring panel.
Since we love to collaborate, it is clear to us that we don't want to become a fully remote company, but we also don't need to spend every day of the week in the office to do a great job.
Our current hybrid work approach focuses on adapting to different needs, including increased flexibility that works best for the teams and the individuals with as much self-determination as possible.
Get more insights on how we work on our blog to find out more about our hiring process or follow us on Instagram for a look inside MOIA.
At MOIA we’re reimagining the future of mobility – safe, autonomous and tailored to the needs of cities and their people. As a tech company with more than 400 employees, we build mobility solutions that truly move cities forward.
We launched our ridepooling service in Hamburg in 2019 and have been part of the city’s public transport system since 2023. Since launch, we have transported over 12 million passengers. Currently, MOIA is evolving from a mobility provider to a tech provider offering a scalable and safe turnkey solution for autonomous driving .
With office locations in Berlin and Hamburg, our diverse and international team comprising more than 60 nationalities works together toward that shared mission.
MOIA is more than a technology provider – we are a partner to cities and public transport operators committed to creating more livable spaces and delivering mobility solutions that are reliable, safe and comfortable.
We value authenticity and personal insights in your application responses. While AI tools can be useful, we encourage you to answer the following questions based on your own experiences and understanding. This helps us keep a human touch and better evaluate your unique perspective and match for the role.
To reinforce an unbiased screening process, we kindly ask you not to include your picture, age, address, or any other details unrelated to your qualifications and suitability for the role. Additionally, we anonymize applications during the initial review phase by removing personally identifiable information. This ensures that our evaluation focuses solely on your skills, experience, and potential – supporting a fair and inclusive hiring experience for all candidates.
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