Top Data Engineer Jobs in Hamburg

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.

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.

Lead development and scaling of data processing pipelines for road and navigation products. Design and implement new pipelines, monitor performance metrics, and enforce security best practices. Collaborate within a cross-functional team to build high-quality, distributed data systems supporting mapping and navigation features.

Design, build, and operate governed data pipelines and context layers for AI agents integrating Salesforce, SAP, and service data. Implement feedback loops, data-quality frameworks, semantic models, Power BI reporting, and secure API-based integrations to support enterprise commercial AI workflows.

Design, build, and operate scalable Databricks data pipelines and Lakehouse data models using SQL, Python, and PySpark. Implement ELT/ETL and streaming patterns, data quality checks, Unity Catalog governance, performance optimization, monitoring, documentation, and operational support.

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.

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

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.

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.

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.

Lead design, build, and operate scalable lakehouse/warehouse data platforms and pipelines for analytics and ML. Own end-to-end ingestion, transformation, storage, governance, DevOps/DataOps, and delivery. Drive architecture, tooling, cost/performance optimization, mentor engineers, engage in presales, and partner with stakeholders to deliver reliable, secure data solutions.

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

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.

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 the companys first data stack and data products (internal analytics and customer-facing). Work across data engineering, devops, and software tasks; ship early features, improve developer experience, document proposals, and iterate based on feedback.

Build and productionize AI-driven workflows that analyze and recommend data visualizations. Design and evaluate LLM features and prompts, structure and enrich data, integrate with Java backends, and define quality and success metrics.

Build and maintain high-performance market data infrastructure in Rust to ingest, process, store, and serve real-time pricing feeds. Solve latency, data quality, and cross-exchange consistency problems, contribute trading/market-data expertise to engineering decisions, communicate with a distributed team, and potentially lead a small team over time.

Own and evolve ClickHouse's Python connector and SDK ecosystem, build integrations with orchestration and transformation tools, drive AI/LLM integration strategy for RAG and feature pipelines, engage with open-source community, and collaborate across product and engineering to prioritize real-world data workflows.

Lead design and build of high-throughput Reverse ETL and data pipelines, modernize pipeline architecture, choose appropriate storage and data models (MySQL, ClickHouse, OpenSearch, BigQuery), drive observability and reliability (Datadog), collaborate with backend and analytics engineers, and set engineering standards to scale sponsored content and CPC marketplaces.

Own and operate Databricks-based data platform production support, build and migrate ETL/ELT pipelines to Databricks across Azure/AWS, optimize compute and performance, enforce Delta Lake architecture and data security, implement observability and CI/CD, support multi-tenant onboarding and on-call incident response, and maintain runbooks and SLA metrics.

Own and evolve a GCP-native data platform from ingestion to reporting. Lead architecture, reliability, observability, data quality, cost optimization, and AI-assisted engineering practices while mentoring engineers and collaborating with product and customer teams.

Build and maintain ML-scale data infrastructure: feature store, real-time and batch pipelines, data quality and validation, and low-latency systems. Translate Data Science needs into high-performance Flink streams, eliminate ETL bottlenecks, collaborate on online ML systems, and implement monitoring, governance, and deployment processes for production ML workflows.

Design and develop data models, ETL processes, and cloud-based data solutions using Azure and various big data technologies. Support report generation and data analysis.