Senior Data Engineer (m/f/d)

Key responsibilities

• Design, implement, and operate scalable data pipelines for structured and unstructured data (batch and, where needed, streaming)
• Develop cloud-native architectures for data and AI workloads (primarily on Microsoft Azure)
• Build and evolve a materials data ecosystem linking physics-based modeling/simulation data and experimental laboratory data
• Handle large-scale scientific datasets (e.g., atomistic simulations, DFT/MD, high-throughput campaigns), including efficient storage, metadata, and performant access patterns
• Integrate data from HPC/simulation workflows and laboratory systems (instrument exports, LIMS/ELN where applicable) into curated, analysis-ready datasets
• Define and implement data models, metadata standards, and provenance to ensure traceability, reproducibility, and auditability across simulations and experiments
• Establish robust data quality practices (validation rules, unit consistency, schema controls) and data quality monitoring aligned with operational SLAs/SLOs
• Implement data governance foundations (cataloging, access control, lineage) and enable policy-driven data sharing across teams
• Enable production deployment of ML and NLP/LLMs/VLMs/VLAs etc. applications through strong MLOps/DataOps practices
• Integrate Azure OpenAI and LLM-based services into enterprise applications in a secure and maintainable way
• Implement Infrastructure-as-Code, CI/CD pipelines, and automation for data and AI systems
• Ensure reliability, security, GDPR compliance, monitoring/observability (logging, metrics, alerting), and cost efficiency of cloud platforms
• For LLM/RAG systems: manage embeddings and vector retrieval lifecycle, introduce evaluation and monitoring, and apply prompt/config versioning for repeatable releases
• Provide technical leadership through design reviews, documentation of standards, and mentoring where appropriate

 

Education & experience

• Degree in Computer Science, Data Science & Engineering, Mathematics, Natural Sciences, or a comparable field
• 5+ years of professional experience in data engineering, cloud engineering, or MLOps
• Hands-on experience deploying ML and/or LLM-based applications in enterprise-grade cloud environments
• Proven experience working with large-scale scientific datasets, ideally in simulation-heavy or R&D environments (materials, chemistry, physics, CAE, computational engineering)
• Experience designing or operating data ecosystems that unify multiple data domains (simulation + experiments) with strong governance and provenance

Technical skills

• Strong Python skills and experience with data processing frameworks
• Strong SQL skills and experience with data modeling for analytics and production use cases
• Proven experience with Microsoft Azure (Data Services, Compute, Storage, Azure OpenAI); AWS/GCP experience is also valued
• Solid understanding of DevOps/DataOps/MLOps practices, CI/CD pipelines, and automation
• Strong experience with containers (Kubernetes), Linux, Git, and system integration
• Familiarity with cloud databases, security concepts, and enterprise integration patterns
• Experience with orchestration tools and operational reliability practices
• Exposure to LLM workflows and RAG architectures; agentic AI experience is a strong plus
• Practical knowledge of distributed data processing and scaling patterns for ingestion/transform/query of very large datasets
• Bonus: familiarity with computational materials science/materials informatics, simulation pipelines, or lab data management (LIMS/ELN/instrumentation exports)

Working style

• Structured, pragmatic, and hands-on with strong ownership
• Able to communicate clearly across technical and non-technical stakeholders
• Curious about new technologies and able to translate them into reliable production systems
• Excellent communication skills in English; German is a plus

 

Equal opportunity & data privacy
Fehrmann MaterialsX is an equal opportunity employer. We welcome applications from all qualified candidates regardless of age, disability, gender identity or expression, marital status, nationality, ethnicity, religion or belief, sexual orientation, or any other status protected by applicable law. We process personal data submitted as part of the application process solely for recruitment purposes and in accordance with applicable data protection laws (including GDPR). Your data will be treated confidentially and stored only for as long as necessary for the recruitment process.
 
What we offer

• Innovative, fast-growing environment with a long-term perspective
• Flat hierarchies, fast decisions, and direct collaboration with management
• Permanent employment with flexible working hours
• Hybrid setup with remote work up to 2 days/week
• Pension scheme, corporate benefits, team events, and a well-connected office location

 

How to apply
Send your CV, LinkedIn profile and a short note highlighting relevant experience in data platforms, scientific data (simulation/experimental), and production ML/LLM deployment, etc. to: career@fehrmann.tech