(Senior) Data Engineer (m/f/d)

Fehrmann Tech Group; Hamburg; Hybrid; EUR 90.000 - 125.000; Vollzeit

Benefits dieser Stelle

Hybrid work

Pension scheme

Corporate benefits

Team events

Zusammenfassung

Fehrmann Tech Group seeks a Senior Data Engineer to own key parts of the data platform enabling analytics, ML, and NLP/LLM applications. You will design scalable pipelines, govern data, and partner with materials science and AI teams to productionize data products.

You will build cloud-native and hybrid data solutions (primarily on Azure), work with large scientific datasets, and ensure traceability and auditability across experiments and simulations.

Qualifikationen

• Degree in Computer Science, Data Science, or related field.
• 3+ years of professional data engineering experience.
• Experience designing/operating data ecosystems with governance and provenance.

Aufgaben

• Design, implement, and operate scalable data pipelines for structured and unstructured data.
• Develop cloud-native and on-premises architectures for data workloads (Azure).
• Build and evolve a materials data ecosystem linking modeling data and laboratory data.
• Handle large-scale scientific datasets with efficient storage and access patterns.
• Integrate data from HPC/simulation workflows and lab systems into curated datasets.
• Define data models, metadata standards, and provenance for traceability.

Kenntnisse

Python

SQL

Vector databases

LLM workflows

RabbitMQ

Azure

CI/CD

DataOps

Security concepts

Orchestration tools

Data governance

Ausbildung

Degree in Computer Science, Data Science or related field

Tools

Microsoft Azure

RabbitMQ

Cloud databases

Jobbeschreibung

Your tasks

Your role
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.
Key responsibilities

• 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

Your qualification

Your profile
Education & experience

• 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 domainswith 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

We offer

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