ESSENTIAL SKILLS

• 5+ years of professional experience in data engineering, with at least 2 years in an environment focused on supporting machine learning or AI
• Expert-level proficiency in Python and its data manipulation libraries (e.g., Pandas, NumPy)
• Strong experience with big data technologies (Apache Spark, Hadoop) and orchestration frameworks (e.g., Apache Airflow, Prefect, Dagster)
• Proven experience working with multimodal datasets, including text, image, video, and robotic time-series data
• Hands-on expertise with data versioning tools and best practices to ensure reproducibility and traceability of complex datasets
• Ability to design, build, and optimize scalable data pipelines that support heterogeneous data types
• Expertise in designing and optimizing data loading pipelines for large-scale (multi-node) machine learning workflows, ensuring high throughput, low latency, and efficient resource utilization
• Hands-on experience with at least one major cloud platform (e.g., AWS, Azure, GCP) and a deep understanding of its data and storage services (e.g., S3, Google Cloud Storage, Azure Data Lake)
• Advanced SQL skills and experience with both relational and NoSQL databases
• Solid understanding of machine learning concepts, data science workflows, and the data needs of models (e.g., feature engineering, model training, inference)
• Experience with Docker and container orchestration platforms (Kubernetes) and building automated CI/CD pipelines for data and ML-related code
• Exceptional analytical and problem-solving skills, with a proven ability to handle complex, unstructured datasets
• Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or a related quantitative field

BENEFICIAL SKILLS

• Strong knowledge of the MCAP format for efficient storage and processing of robotic data streams
• Experience with a feature store (e.g., Feast)
• Knowledge of deep learning frameworks (TensorFlow, PyTorch)
• Familiarity with real-time data streaming technologies (e.g., Kafka)
• Contributions to open-source data or ML projects
• Leadership experience, experience building a team

ABOUT US

Agile Robots SE is an international high-tech company based in Munich, Germany with a production site in Kaufbeuren and more than 2300 employees worldwide. Our mission is to bridge the gap between artificial intelligence and robotics by developing systems that combine state-of-the-art force-moment-sensing and world-leading image-processing technology. This unique combination of technologies allows us to provide user-friendly and affordable robotic solutions that enable intelligent precision assembly.
This is made possible by our employees, who bring out the best in each and every day with creativity and enthusiasm. Become part of this team and shape the future of robotics with us!
We are proud of our diversity and welcome your application regardless of gender and sexual identity, nationality, ethnicity, religion, age, or disability.

ABOUT THE ROLE

We are seeking a highly experienced and passionate Senior Data Engineer to join our core team. In this role, you will be the architect and builder of the data infrastructure that powers our AI and machine learning models. You’ll work at the intersection of data science and engineering, designing and implementing robust, scalable data pipelines that ensure our data scientists have the high-quality, reliable data they need to train, evaluate, and deploy state-of-the-art models. This is a critical position for someone who is excited by the unique data challenges of a cutting-edge AI company.

YOUR RESPONSIBILITIES

• Architect, develop, and maintain complex ETL/ELT pipelines to ingest, clean, and transform large-scale, often unstructured, data from diverse sources for use in AI and machine learning workflows
• Work directly with data science and machine learning teams to understand their data requirements for model training, feature engineering, and inference
• Implement data quality, validation, and monitoring systems to ensure the integrity, freshness, and usability of data for our models. You’ll be a champion for data governance in an ML context
• Contribute to the architectural design and evolution of our data lake, feature store, and data warehouse, ensuring it is optimized for high-performance ML workloads
• Integrate data pipelines with ML orchestration platforms (e.g., MLflow, Kubeflow) to automate the full lifecycle of data-driven models
• Provide expert support and a strong engineering presence for data-related issues, helping to debug model performance and data drift problems
• Act as a technical leader, mentoring junior engineers and promoting data engineering best practices across the organization