Top Data Engineer Jobs in Hamburg

Own and evolve data storage and warehouse foundations (schemas, migrations, performance, reliability). Design data models and marts for analytics, build backend services and RESTful APIs in Python, improve testing, monitoring, and reliability, and collaborate with product and engineering teams to deliver data-driven features.

Build and maintain reusable data products and production data pipelines using Python. Ingest and process internal and external data, ensure feed reliability through monitoring, alerting and validation, document and align datasets to standards, support users with domain knowledge, and improve dataset structure and reusability.

The Data Engineer will manage tracking data pipelines, ensure data quality and governance, optimize ELT processes, and collaborate with teams to enhance data accessibility and reliability.

Build and maintain scalable ETL/data pipelines and orchestration for content publishing, optimize and monitor data infrastructure, automate processes, and collaborate with data and content teams.

The Data Engineer will develop and optimize ETLs/ELTs, manage data workflows, collaborate with teams, and ensure data quality and security.

Own end-to-end AI-automated data platform migration projects (1-4 concurrently): scope, plan, execute, and hand off. Act as primary customer contact, configure Datafold's Migration Agent, partner with engineering on execution, and help refine delivery playbooks.

The Principal Data Engineer will improve data pipeline architecture, optimize data flows, and support data projects for various teams in the company.

Build and maintain scalable data pipelines, transformations, and analytics-ready models using dbt, SQL, Python, and Snowflake. Implement ingestion, CDC, API integrations, testing, monitoring, and observability. Collaborate with analysts and stakeholders to define metrics, ensure data governance and compliance, and optimize data platform reliability.

Build and maintain robust, production-grade data foundations for R&D semiconductor data. Design data models and pipelines, implement automated data quality checks, improve traceability and metadata, and support digital twin, analytics, and workflow automation use cases. Evaluate AI/ML versus rule-based approaches and establish engineering standards for testing, monitoring, versioning, and documentation.

The Data Engineer will design and maintain scalable data pipelines, optimize data infrastructure, monitor data quality, and enable analytics capabilities, ensuring alignment with business goals.

Design, build, and operate enterprise reporting and analytics platforms. Create scalable ELT/ETL pipelines, master data foundations, and lakehouse/warehouse semantic models. Deliver Power BI datasets and dashboards, implement data quality, governance, DevOps practices, and cost-management data solutions. Partner with finance and stakeholders and provide technical leadership across architecture, deployment, and run-state support.

Responsible for deploying and maintaining data center infrastructure, troubleshooting hardware and network issues, managing inventory, and setting up data centers.

Own and transform supply data into governed, analysis-ready datasets. Build and monitor GCP/Python pipelines, optimize SQL, document schemas/lineage, manage access and costs, ensure data validity, and mentor analysts while aligning stakeholders on data contracts.

The Software Engineer will manage data collection and processing to enhance AI model training, collaborating with scientists and extending cloud infrastructure.

Lead development of Data and AI features from data architecture to optimization and simulation. Design scalable data architectures, reliable pipelines, and DataOps/DevOps workflows. Partner with analysts, product managers and supply chain specialists to turn complex logistics data into actionable solutions.

Lead design and build of high-throughput Reverse ETL replication pipelines, modernize data pipelines into scalable architectures, choose optimal storage and modeling, partner with backend and analytics engineers, and champion observability, reliability, and engineering standards to grow Retail Media revenue.

As a Senior Data Engineer, you will build and optimize data platforms and pipelines, ensuring the smooth transfer of terabytes of data and delivering critical reports for executive decisions.

The Technical Privacy Expert will implement privacy-by-design principles, ensure compliance with regulations, mentor teams, and develop technical frameworks for privacy in technologies.

Design, build, and optimize production-grade data pipelines and architectures. Implement DataOps practices, automate workflows, improve data quality, and collaborate with Data Scientists and stakeholders to deliver scalable cloud data solutions.

Build and operate large-scale data pipelines and orchestration for generative AI research, focusing on video/audio datasets, feature extraction, labeling, and dataset quality to improve model performance.

Design and develop cloud-native backend services in Rust to process terabytes of data daily. Build and operate data-intensive microservices on AWS and Kubernetes, integrate with distributed databases and messaging systems (Kafka/RabbitMQ), and collaborate across teams to deliver observability features.

As a Senior Software Engineer, you'll design and build scalable market data systems, improve API architecture, and enhance overall system performance, contributing to Alpaca's infrastructure for trading financial assets.

The Senior Data Platform Engineer will lead project delivery for data infrastructure, manage near real-time services, and collaborate across teams while contributing to AI and data governance frameworks.

As a Senior Software Engineer at ClickHouse, you'll enhance the Python data ecosystem by creating tools and integrations for data practitioners, focusing on performance and reliability while collaborating with various teams.

End-to-end delivery of ML-powered services, involving research, prototyping, production, and operations. Collaborate on product requirements while maintaining high quality and operational excellence.