Top Data Engineer Jobs in Germany

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.

Zuhlke is seeking an Expert Data Engineer focused on Databricks to support client transformation initiatives. The provided description contains limited role-specific responsibilities, qualifications, or project details. The position appears aligned with data engineering and the development of modern data solutions within Zuhlke’s multidisciplinary engineering and innovation teams.

Own the design, delivery, and operation of complex data pipelines, datasets, and platform components. Translate business needs into scalable data models and reliable data products, improve data quality and observability, resolve production issues, and establish reusable engineering tools and standards. Collaborate with product and business stakeholders, contribute to architecture decisions, mentor engineers, and participate in on-call support.

Develop and maintain BI dashboards, reports, data models, and ETL processes supporting commodity trading, risk, P&L, inventory, and logistics analysis. Integrate data from trading platforms, market providers, ERP systems, and warehouses. Automate reporting, improve data quality, monitor market indicators, and translate stakeholder requirements into scalable analytical solutions and visualizations.

Build and maintain Azure-based data pipelines, warehouses, Data Vault and dimensional data marts using ADF, Azure SQL, and T-SQL. Develop Power BI datasets, semantic models, and reports; optimize stored procedures; investigate source systems; and implement data quality monitoring. The role also involves profiling datasets, validating business results, debugging data platforms, and collaborating with clients and a remote European team.

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 maintain scalable data pipelines, ETL workflows, data models, dashboards, reports, and visualizations supporting game analytics, live operations, business intelligence, and reporting. Integrate multiple data sources, optimize SQL queries, ensure data quality, monitor the data platform, and collaborate with cross-functional teams to deliver reliable and actionable data solutions.

Build and maintain scalable data pipelines, ETL workflows, data models, and infrastructure supporting game analytics, live operations, reporting, and business intelligence. Develop dashboards and visualizations, integrate and validate data from multiple sources, optimize SQL queries, monitor data platforms and reporting systems, and collaborate with cross-functional teams to deliver reliable, actionable data solutions.

Develop and maintain scalable data engineering features, pipelines, workflows, and cloud-based architectures for quick-commerce operations. Collaborate with data scientists and engineers to optimize machine-learning model lifecycles, monitoring, alerting, and CI/CD pipelines. Contribute to architecture evolution and engineering best practices, including test-driven development and continuous integration and delivery.

Designs, develops, deploys, operationalizes, and maintains enterprise-scale data pipelines, workflows, and analytic applications. The role organizes disparate structured and unstructured data for reporting, analytics, and applied production AI. Responsibilities include assessing, designing, building, and maintaining solutions while collaborating with analysts, developers, data engineers, and data consumers in an agile environment. The engineer will also guide multidisciplinary teams and may support mission-driven defense projects.

Design and operate a governed data platform for contract-driven publishing and consumption. Build PostgreSQL-to-Debezium-to-Kafka CDC workflows, GitOps-style contract validation and reconciliation, developer tooling, Kubernetes runtimes, and DataHub catalog integrations. Define data product standards covering schemas, ownership, lifecycle, compatibility, lineage, access, and recovery. Collaborate with infrastructure, product, analytics, data engineering, and AI/ML teams to deliver reliable, reusable production data capabilities.

Owns the end-to-end data processing and dataset lifecycle for robotics AI/ML systems. Designs scalable pipelines that convert raw sensor recordings into reliable training, validation, benchmarking, and regression datasets. Implements data quality checks, versioning, lineage, reproducibility, distribution, and infrastructure integration across cloud and compute environments. Develops dashboards monitoring data quality, scenario coverage, labeling, and ML readiness while collaborating with AI/ML, perception, validation, and platform engineering teams.

Build and maintain reliable data models and ETL/ELT pipelines across AWS-based data lake layers. Ingest high-volume billing, CRM, network, and customer data; process Kafka events; develop dbt transformations; orchestrate workflows with Airflow; and implement data quality, monitoring, documentation, and reconciliation practices. Collaborate with stakeholders and engineers to deliver accurate, timely, traceable datasets for reporting and self-service analytics.

Build, deploy, operate, and sustain scalable data and AI solutions for mission users. Responsibilities include requirements discovery, full-stack and data engineering, ETL/ELT pipeline development, data modeling, agentic AI workflows, testing, deployment, training, and system lifecycle management. The role works directly with senior stakeholders and embedded end users across multiple concurrent projects, integrating emerging platforms and technologies in mission environments.

Designs, builds, operationalizes, and maintains enterprise-scale data applications and scalable ETL/ELT workflows. Develops systems for collecting, ingesting, storing, processing, modeling, warehousing, and retrieving structured and unstructured data. Integrates Generative AI, LLM-based workflows, and agentic automation into secure mission systems supporting Intelligence Community objectives. Collaborates with data scientists, analysts, software developers, and stakeholders in Agile environments.

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.

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.

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

Build and operate a scalable AWS-based data platform and lakehouse. Develop and maintain data pipelines, ingest internal and third-party data, prepare/clean data, create data models for reporting, analytics and AI, and implement testing and monitoring. Collaborate with product and engineering teams and share best practices.

Design, build, and operate scalable AWS-based data pipelines and a lakehouse. Clean and model structured/unstructured data for reporting, analytics, and AI. Implement testing and monitoring for pipeline reliability. Collaborate with product and engineering teams and share data engineering best practices across the company.

Create and maintain illustrated parts catalog (IPC) data and spare-part configurations for helicopters. Analyze change proposals, extract parts data from technical documents, manage SAP master data and MET, and prepare/validate technical documentation to international standards while collaborating with engineering, logistics, quality, and airworthiness teams.

Build scalable backend functionality, algorithms, and data pipelines for industrial IoT products. Responsibilities include optimizing microservice architecture, collaborating with frontend and backend engineers, participating in architecture through testing and maintenance, and evaluating emerging technologies. The role requires modern API development, serverless and streaming architectures, SQL and NoSQL databases, Linux, and backend DevOps technologies.

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.

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.