Top Data Engineer Jobs in Germany

As a Data Engineer at Superhuman, you'll design and implement data systems, ensuring scalability and security for handling billions of daily events, while collaborating across teams and mentoring engineers.

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

Design, build, and maintain scalable data pipelines and storage for fraud detection. Support fraud analytics, feature engineering, model integration and serving, pipeline monitoring, and documentation. Collaborate with data, product, and engineering teams to enable detection, reporting, and operationalisation of fraud models.

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.

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.

The Data Engineer will build data pipelines for materials science, ensuring data quality and collaborating with ML researchers while managing large datasets.

As a Data Engineer at GALVANY, you'll architect data infrastructure, process real-time energy data, build scalable data pipelines, and ensure data quality for an AI-based Operating System.

Design, build, operationalize, and maintain enterprise-scale data pipelines and analytic solutions. Lead cross-functional teams to integrate disparate data sources, enable production AI, and support reporting, visualization, and geospatial analyses.

Implement and operationalize data pipelines and workflows to support production AI. Design, build, and maintain enterprise-scale analytic solutions; guide cross-functional teams of analysts, engineers, and developers; perform analytical exploration and data examination; deliver reporting and visualization solutions. Position requires travel up to 25% and TS/SCI clearance.

Build and maintain the central data platform powering domain Analytics Engineering and BI: evolve dbt platform, enable self-service tooling, manage IaC and GitOps, enforce federated governance and PII handling, add observability/testing, and partner with domain teams to unblock and enable safe, scalable model delivery.

Design, model, and develop AWS-based data ecosystems and pipelines. Gather and document business and technical requirements, optimize data storage (lakes/warehouses), tune performance, troubleshoot processing and infrastructure issues, integrate diverse data sources, mentor junior engineers, and engage directly with clients.

Own and build data ingestion and cleaning pipelines for plant data, maintain APIs and PostgreSQL databases, support ML pipelines, debug anomalies with customers, and improve onboarding with AI-powered ingestion.

Develop and maintain scalable data infrastructure and ETL pipelines, support machine learning applications, ensure data quality, and collaborate on data models.

As a Data Engineer, you will develop data pipelines, organize data from various sources, and contribute to analytics solutions while collaborating with a multi-disciplinary team.

Design, build, and maintain scalable data platforms and pipelines (ETL/ELT) to ingest, parse, and process structured and unstructured data. Lead cross-functional teams to operationalize data applications, develop scripts to convert data formats, monitor and scale platforms, and support analytics, streaming, and mission-driven use cases.

As a Data Analyst at Air Apps, you'll analyze user behavior and product performance, create reports and dashboards, and provide actionable insights using data visualization tools and programming languages.

The role involves building and maintaining data pipelines using various technologies, collaborating with stakeholders, monitoring data health, and promoting team knowledge sharing.

As a Data Engineer, you will develop and manage data pipelines, ensuring reliable data ingestion and delivery while optimizing for simplicity and performance.

Design, develop, and maintain ETL mappings, workflows, and transformations using Informatica PowerCenter and PowerExchange. Integrate data across Oracle, DB2, Snowflake, Linux, and mainframe systems, optimize performance, automate ETL operations with shell scripts, and support production operations and incident management. Translate business requirements into scalable data integration solutions and collaborate on data warehousing and reporting needs.

The Data Engineer will own the data stack, develop cloud-based data platforms, maintain data pipelines, and support data-driven decision-making within the company.

As a Senior Data Engineer, you will develop AWS-based data platforms, build data pipelines, ensure data accuracy, and collaborate with teams on data strategies.

As a Data Engineer, you will manage laboratory and clinical data projects, building machine learning models and supporting data analytics in the life sciences domain.

Build and maintain unstructured-data pipelines to parse logs, code diffs and developer comments into queryable datasets. Define AI-related KPIs, analyze productivity impact, produce ROI reporting, and evangelize adoption through workshops, training, and cross-brand coordination while navigating legal, security, and regional blockers.

Design, build and improve scalable cloud-native data pipelines and architectures using Azure and Databricks. Collaborate with teams to process large datasets, improve performance, ensure data quality, and apply CI/CD practices for deployments.

Design, build and optimize scalable cloud-native data pipelines and architectures using Azure and Databricks. Develop DBT models, ensure data quality, apply CI/CD practices, collaborate with stakeholders, and support Data & AI initiatives across finance, e‑commerce, pharma, and supply chain domains.