AlphaIgnis

Senior Data Engineer (F/M/D)

🇩🇪 Munich, Німеччина На місці IT Повна зайнятість Lead Опубліковано Тра 28, 2026
Формат роботи На місці
Тип зайнятості Повна зайнятість
Рівень досвіду Lead
Категорія IT
IT-категорія Інженер даних
Мова English
Опубліковано 28 травня 2026 р.
Остання перевірка 28 травня 2026 р.
Контекст JobGrid

Огляд ролі від JobGrid

Senior Data Engineer (F/M/D) at AlphaIgnis: Munich, Німеччина; На місці; Повна зайнятість; Lead; IT. JobGrid adds normalized role facts, source context, and a path to the employer application page so candidates can compare the listing before applying.

  • Location and workplace: Munich, Німеччина, На місці
  • Role classification: IT, Інженер даних, Повна зайнятість, Lead
  • Source freshness: checked by JobGrid on 2026-05-28.
  • Application path: candidates continue to the employer application page with non-personal referral tags.

The Opportunity

We’re looking for a Senior Data Engineer to architect and scale the data backbone powering next-generation AI models in robotics and real-world environments.

This role sits at the intersection of distributed systems, multimodal data processing, and applied machine learning, with a strong focus on building high-quality datasets for robotic foundation models. You will ensure that data pipelines, infrastructure, and data strategy directly translate into measurable improvements in model performance.

Your Responsibilities

  • Drive the model–data loop by connecting application requirements with data collection, and translating model failures into data-driven improvements through collection, curation, and augmentation
  • Build and scale distributed data pipelines (Ray/Anyscale or similar) for TB-scale video, sensor, and robotics datasets
  • Design multimodal data schemas aligning video, actions, and high-frequency sensor streams
  • Develop Python tooling for data quality, including cleaning, anomaly detection, and dataset versioning
  • Own dataset quality and coverage, including annotation workflows, data diversity, and storage trade-offs
  • Lead a small team and coordinate with data providers and annotation vendors
  • Oversee real-world data collection, including technical setup, compliance, and secure data handling

Technologies

  • Python (advanced, production-grade)
  • Ray / Anyscale or Apache Spark
  • AWS / GCP for large-scale data and GPU training pipelines
  • Video and sensor data formats (H.264/H.265, ROS bags, MCAP)
  • PyTorch, NumPy
  • DVC, LakeFS or similar data versioning tools
  • Distributed data processing and storage systems