AlphaIgnis

Senior Data Engineer (F/M/D)

🇩🇪 Munich, Niemcy Na miejscu IT Pełny etat Lead Opublikowano Maj 28, 2026
Lokalizacja Munich, Niemcy
Tryb pracy Na miejscu
Forma zatrudnienia Pełny etat
Poziom doświadczenia Lead
Kategoria IT
Kategoria IT Inżynier danych
Język English
Opublikowano 28 maja 2026
Ostatnio sprawdzono 28 maja 2026
Kontekst JobGrid

Podsumowanie roli od JobGrid

Senior Data Engineer (F/M/D) at AlphaIgnis: Munich, Niemcy; Na miejscu; Pełny etat; 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, Niemcy, Na miejscu
  • Role classification: IT, Inżynier danych, Pełny etat, 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