Top Data Engineer Jobs in Germany

Build, analyze, and maintain scalable data infrastructure and reliable data pipelines. Translate business challenges into data models, ETL processes, algorithms, and production code. Collaborate with international teams across the full development lifecycle, including prototyping, software development, optimization, and maintenance. Work with distributed data processing, databases, cloud services, workflow orchestration, and large datasets in an agile environment.

Design and maintain scalable data pipelines, connectors, and reusable data products to make enterprise data AI-ready. Enable RAG, semantic search, agentic workflows, and production deployment of AI solutions while enforcing data quality, governance, metadata, security, and cross-team collaboration.

Installs, configures, tests, and troubleshoots servers, switches, PDUs, cabling, and other data center infrastructure. Supports deployments and upgrades, documents port matrices, resolves equipment issues using command-line tools, participates in on-call rotations, and collaborates with local and remote teams. The role requires Linux, networking, hardware, cabling, and data center experience, along with the ability to lift up to 35 kg and occasionally travel between Frankfurt-area facilities.

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, deploy, operate, and maintain scalable data and AI solutions for mission users. Responsibilities span requirements discovery, full-stack and data engineering, ETL/ELT pipelines, data science, modeling and simulation, agentic AI workflows, testing, deployment, training, and sustainment. The role partners directly with senior mission stakeholders, integrates emerging technologies, and supports multiple operational projects in classified environments.

Build and maintain scalable data applications, pipelines, and ETL/ELT workflows for structured and unstructured data. Develop software for data retrieval, parsing, transformation, reporting, and analytics at enterprise scale. Support planning and operational activities for U.S. Army Europe Africa Headquarters and collaborate on mission-focused technology solutions. Candidates must hold a Secret clearance and bachelor's degree.

Develop and operationalize data and machine learning pipelines, process structured and unstructured data, integrate large language models, and build AI-driven applications. Collaborate with data scientists, ML engineers, analysts, and platform engineers to deliver end-to-end solutions supporting analytics, feature engineering, model development, and operational decision-making.

Support and maintain data pipelines in Microsoft Fabric, onboard data sources, transform and validate data across Bronze, Silver, and Gold layers, and maintain datasets for reporting, analytics, and AI initiatives. Collaborate with engineers, analysts, and stakeholders, monitor processing, resolve issues, document processes, and contribute to data platform modernization and continuous improvement.

Supports and maintains enterprise data processes across Google Cloud Platform and Microsoft Fabric. Responsibilities include monitoring and validating data flows, performing data quality and reconciliation checks, troubleshooting inconsistencies, maintaining ETL/ELT pipelines, onboarding integrations, documenting processes, and improving the data platform. The role collaborates with data engineering, infrastructure, analytics, and business teams to ensure secure, accurate, and trusted data for reporting and decision-making.

Build data warehouses, cloud infrastructure, AI workflows, and scalable data products. Integrate data from multiple systems, enable organizational data access, assess AI use cases, and collaborate with analytics, product, and software engineering teams in an agile environment.

As a Data Engineer, you will design and build data pipelines, models, and infrastructure for analytics and machine learning while ensuring data quality and compliance.

Build and maintain scalable production data pipelines, machine learning data pipelines, and data transformation frameworks for financial crime detection solutions. Collaborate with product, R&D, data, analytics, customers, and partners; optimize large-scale data architectures; support feature engineering and root cause analysis; and train customer teams. The role requires customer-facing collaboration, English and Spanish fluency, and domestic and international travel when needed.

Designs, develops, operationalizes, and maintains enterprise-scale data applications and scalable ETL/ELT pipelines. Processes structured and unstructured data, creates reporting and analytics solutions, and develops data visualizations using tools such as Tableau, Qlik, Power BI, or Foundry. Collaborates with multidisciplinary teams in agile environments and supports mission-driven defense and intelligence projects.

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 data pipelines and infrastructure for analytics, experimentation, and company metrics. Collaborate with machine learning engineers on training, quality assessment, and model release automation. Improve data delivery processes, automate manual workflows, and develop scalable data models. The role requires strong Apache Spark and Python expertise, SQL proficiency, Git experience, and familiarity with cloud data platforms and Kubernetes.

Build and maintain data pipelines, infrastructure, data models, and automation for analytics and machine learning workflows. Collaborate with ML engineers on model training, quality assessment, and releases. Improve internal data delivery processes and enable efficient access to large-scale organizational data.

Design and operate scalable knowledge graph systems for SAP’s Physical AI platform. Translate industry-specific knowledge requirements into reliable infrastructure, document best practices, guide technical decisions, collaborate with engineers and domain experts, and mentor colleagues. The role owns the knowledge layer supporting industry-contextual AI, with emphasis on data modeling, scalability, quality, governance, and cross-functional technical leadership.

Architect, implement, scale, and maintain petabyte-scale data processing platforms for frontier AI models. Build reliable, observable, cost-efficient pipelines with heterogeneous and GPU-accelerated workloads, including failure recovery and restartability. Ensure datasets and intermediate outputs are versioned, reproducible, traceable, and documented with datasheets. Partner with AI researchers to integrate datasets into training pipelines and guide sustainable technology decisions.

Leads strategic data analytics initiatives for Security Services, delivering executive insights, predictive models, machine learning pipelines, data pipelines, dashboards, and automated workflows. Partners with engineering, product, and senior leadership to translate complex business challenges into data-driven strategies, improve data governance, and implement AI and emerging technologies.

Design, build, and optimize large-scale batch and streaming ETL pipelines and ML infrastructure for real-time recommender systems. Implement high-throughput/low-latency data stores, ensure data governance and quality, collaborate with product, data science, and engineering teams, and influence architecture and team objectives to deliver measurable business impact.

Lead the architecture and development of a multi-country Snowflake and GCP data platform. Build CDC ingestion pipelines, dbt semantic models, and DataOps CI/CD workflows; implement governance, RBAC, PII masking, compliance, FinOps, and performance optimization. Partner with business stakeholders, document architectural decisions, and mentor data engineers across LATAM operations.

Analyze oilfield and sensor data to optimize well and reservoir production, define operational strategies, collaborate with customers to deliver data-driven insights, and contribute to development and validation of flow sensors and data-driven measurement systems using reservoir simulation and machine learning.

Perform and monitor routine data processing operations for Copernicus missions, including ingestion, processing, distribution, anomaly resolution, validation, reporting, and contingency support. Develop operational procedures, documentation, tools, and automation using scripting and databases. Support mission phases, configuration management, technical coordination, training, and on-call operations for Earth observation data processing systems.

Build and operate production data and machine learning systems for algorithmic pricing. Responsibilities include designing scalable pipelines, enabling model training and deployment, productionizing models, monitoring performance, managing Databricks and Kubernetes workloads, creating dashboards and alerts, and translating business requirements into reliable software. The role also emphasizes testing, code review, CI/CD, observability, system reliability, and collaboration with scientists, product managers, engineers, and commercial stakeholders.

Own voize’s analytics foundation, including dbt data models on Redshift, ingestion pipelines, KPI dashboards, metric definitions, testing, documentation, and monitoring. Partner with Finance, RevOps, Product, and Customer Success to turn business questions into trusted self-service analytics. As the first dedicated analytics hire, establish data standards, architecture, and scalable workflows while leveraging AI for automation.