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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # (Senior) Data Engineer - **Company:** Fehrmann Tech Group - **Location:** Hamburg, Germany (Remote available) - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Amazon Web Services, Data Analysis, Audit Trail, Microsoft Azure, Batch Processing, Big Data, Cloud Database, Cloud Engineering, Continuous Integration, Data as a Services, Information Engineering, Data Governance, Data Sharing, Dataspaces, Database Queries, DevOps, Distributed Computing Environment, Python (Programming Language), Laboratory Information Management Systems, Machine Learning, Message Broker, Metadata, Metadata Standards, Natural Language Processing, RabbitMQ, DataOps, Search Technologies, Unstructured Data, Workflow Management Systems, Enterprise Application Integration, Data Logging, Enterprise Software Applications, Large Language Models, Information Technology, Deployment Automation, Integration Frameworks, Data Management, Data Pipelines - **Published:** September 21, 2026 - **Apply:** https://www.adzuna.de/details/5892734695 ## About the Role * Degree in Computer Science, Data Science & Engineering, Mathematics, Natural Sciences, or a comparable field * 3+ years of professional experience in data engineering. * Experience designing or operating data ecosystems that unify multiple data domains 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 * Familiarity with Vector databases, semantic search, text chunking strategies, LLM workflows and RAG architectures. * Experience with message brokers and asynchronous processing patterns; practical experience with RabbitMQ is a strong plus * Proven experience with Microsoft Azure (Data Services, Compute, Storage, Azure OpenAI); AWS/GCP experience is also valued * Solid understanding of DataOps practices, CI/CD pipelines, and automation * Familiarity with cloud databases, security concepts, and enterprise integration patterns * Experience with orchestration tools and operational reliability practices 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 ## Description As a Senior Data Engineer, you will own key parts of our data ecosystem and platform capabilities that enable advanced analytics, machine learning, and NLP/LLM applications. Your focus is to make data usable at scale: well-structured, traceable, governed, and accessible for downstream AI/ML use cases and enterprise applications. You will work closely with the materials experts, simulation teams, lab stakeholders, and AI/ML colleagues to translate product goals into robust data products and production-ready solutions., * Design, implement, and operate scalable data pipelines for structured and unstructured data, including batch processing and event-driven or streaming workflows where needed. * Develop cloud-native and on-premises 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 * Work with the DevOps team to implement and improve CI/CD pipelines, deployment automation, and infrastructure requirements for data and AI workloads. * Ensure reliability, security, GDPR compliance, monitoring/observability (logging, metrics, alerting), and cost efficiency of cloud platforms * Provide technical leadership through design reviews, documentation of standards, and mentoring where appropriate ## Related Videos - [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works) - [Beyond Kafka & RabbitMQ: Why NATS is the Future of Microservices Messaging](https://www.wearedevelopers.com/videos/1646-beyond-kafka-rabbitmq-why-nats-is-the-future-of-microservices-messaging) - [From DevOps to Scaled DevOps: How We’re Rebuilding Continuous Delivery as a Platform](https://www.wearedevelopers.com/videos/100018-from-devops-to-scaled-devops-how-we-re-rebuilding-continuous-delivery-as-a-platform) - [Modern Data Architectures need Software Engineering](https://www.wearedevelopers.com/videos/1030-modern-data-architectures-need-software-engineering) - [Why Systems Break After Initial Success: The Architectural Failures That Take Months to Surface](https://www.wearedevelopers.com/videos/2048-why-systems-break-after-initial-success-the-architectural-failures-that-take-months-to-surface) - [DevOps Maturity Check – a way to balance autonomy and alignment](https://www.wearedevelopers.com/videos/58-devops-maturity-check-a-way-to-balance-autonomy-and-alignment) ## Related Articles - [Data Analyst Salary Germany](https://www.wearedevelopers.com/magazine/277-data-analyst-salary-germany) - [The Biggest German Tech Companies](https://www.wearedevelopers.com/magazine/424-the-biggest-german-tech-companies) - [Backend Developer Salary in Germany [2023]](https://www.wearedevelopers.com/magazine/196-backend-developer-salary-in-germany-2023) - [Frontend Developer Salary in Germany [2023]](https://www.wearedevelopers.com/magazine/195-frontend-developer-salary-in-germany-2023) - [Fullstack developer salary in Germany [2023]](https://www.wearedevelopers.com/magazine/197-fullstack-developer-salary-in-germany-2023) - [Software Developer Salary in Germany [2023]](https://www.wearedevelopers.com/magazine/194-software-developer-salary-in-germany-2023)