Top Data Engineer Jobs in Hamburg

Designs, builds, and improves data workflows, backend services, AI agents, pipelines, and workflow orchestration systems. The role centralizes fragmented tools, supports production-grade internal platforms, improves reliability and automation, and translates analyst and stakeholder requirements into scalable technical solutions. Requires strong Python, SQL, data modeling, AI application, Git, and data engineering skills, with collaboration across analysts, engineers, and business stakeholders.

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.

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.

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.

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.

Design and deliver scalable data pipelines for model development and production ML workflows in autonomous driving. Ingest, transform, curate, validate, and monitor large-scale real-world, synthetic, and partner datasets. Improve pipeline performance and collaborate with ML engineers, Data Corpus, AI Platform, and partners to ensure datasets are reliable, traceable, and model-ready.

Design, operate, and optimize scalable ELT pipelines and platform infrastructure for international healthcare data. Build entity-resolution and MDM workflows, implement schema governance and data contracts, optimize cloud storage and warehouse costs, and automate CI/CD for data pipelines while partnering with analytics and domain experts to deliver production-ready datasets.

Gather, analyze, and interpret supply-related data; translate findings into actionable business insights; automate reporting; maintain and build Looker Studio dashboards; support ad hoc analysis; ensure data quality; and collaborate with engineers, data scientists, and stakeholders to promote data-driven decision-making.

Own supply data by building governed analytics datasets, reusable data marts, and reliable Python and GCP pipelines. Collaborate with analysts, data scientists, and supply stakeholders to establish data contracts and a shared source of truth. Monitor platform health, manage access and costs, optimize performance, validate data, document lineage, and simplify transformations. Mentor junior analysts and improve SQL capabilities across the supply organization.

Own and evolve the company’s data platform, including Dagster, dbt, AWS ECS, and BigQuery. Build tooling, monitoring, data quality checks, connectors, testing, CI/CD, cataloging, and cost controls. Maintain high-volume ELT pipelines transferring terabytes from AWS MySQL to BigQuery while protecting source performance. Deliver and oversee critical executive reports that support company decisions and enable other data teams.

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.

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.

Design, build, and optimize real-time and batch data pipelines using Apache Beam and GCP services. Integrate multiple data sources, ensure data quality and observability, improve pipeline performance and cost efficiency, and apply CI/CD, infrastructure-as-code, monitoring, governance, security, and compliance practices. Collaborate with data scientists, analysts, and software engineers to deliver reliable data for analytics, AI, and business intelligence.

Own the architecture and development of scalable data models using Data Vault 2.0, dbt, BigQuery, and Airflow. Establish modeling standards, data quality, lineage, governance, testing, and CI/CD practices. Optimize cloud data warehouse performance and costs, oversee orchestration workflows, collaborate with analytics and business stakeholders, mentor engineers, and address scalability challenges across modern cloud data platforms.

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.

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

Design, build, and maintain scalable ELT data pipelines with Airflow; develop dbt data models and tests; optimize Athena tables/queries and data workflows; provision infrastructure with Terraform; ensure data quality and reliability; collaborate with Analytics, BI, and Engineering; troubleshoot pipeline and data quality issues.

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.

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

Design and operate an AI-native gameplay analytics ecosystem: define tracking, storage, processing, and semantic layers; build AI-driven reporting and agentic workflows; ensure data correctness; generate product intelligence and set organization-wide agent standards.

Design, implement, and maintain Python backend services and CI/CD/GitOps pipelines on AWS/Kubernetes. Build and maintain data models (Avro, SQL), publish via Kafka and schema registry, create access-layer models in Snowflake, and coordinate between Data and Tech teams to align schemas, priorities, and timelines.

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.

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.