Top Data Engineer Jobs in Germany

Supports customer-facing deployment projects by gathering and analyzing data, directing AI agents to build data models and dashboards, configuring Celonis solutions, validating outputs, developing KPIs, and documenting solution designs. The intern learns AI-augmented software delivery, process analysis, specification writing, and context engineering while working alongside experienced Deployment Engineers.

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.

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.

Designs, develops, tests, optimizes, and maintains scalable batch and near-real-time data pipelines and architectures. Ensures data quality, builds API integrations, improves internal data processes, and supports machine learning, data science, BI, and product initiatives. The role requires experience with data warehouses, relational and NoSQL databases, data modeling, event-driven architectures, SQL, Python, Airflow, Spark, and AWS data services.

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.

Designs and operates scalable data integration, transformation, quality, and management solutions for digital R&D. Responsibilities include building data pipelines and models, maintaining a data mesh catalog, implementing CI/CD and infrastructure automation, and supporting monitoring, observability, metadata, lineage, access control, and governance. The role partners with cloud architects and platform teams to digitalize chemical research and development.

Own the data engineering side of DoiT’s integrations framework by building and maintaining third-party billing and usage API integrations. Ensure data correctness, completeness, normalization, reconciliation, and support for negotiated rates. Manage orchestration, scheduling, backfills, and pipeline operations while using AI throughout development. Collaborate with product, support, and engineering teams to expand vendor coverage and improve cloud and SaaS spend visibility.

Own data engineering for a cloud and SaaS billing integrations platform. Build and maintain third-party billing API integrations, operate ingestion pipelines, normalize vendor data, manage orchestration and backfills, and ensure financial-grade correctness through reconciliation and quality checks. Use AI throughout the engineering workflow and collaborate with product, support, and platform teams to expand vendor coverage and improve customer cost visibility.

Develops and maintains secure data pipelines, ETL/ELT workflows, data transformations, and analytics-ready datasets. Processes structured and unstructured data using platforms such as Databricks, Spark, Palantir Foundry, and Maven Smart Systems. Collaborates with data scientists, engineers, and mission stakeholders while applying data validation, modeling, quality, testing, documentation, and Agile practices. The role requires onsite work in a SCIF and support for classified national security missions.

Own Synera’s data warehouse and ingestion infrastructure, integrating GTM and business systems into reliable, documented, monitored pipelines. Build data models, reporting and forecasting layers, and agent-ready interfaces using APIs, MCP connectors, permissions, and governance. Develop retrieval infrastructure for documents and unstructured content, including chunking, embeddings, and indexing. Partner with teams across the company to improve data quality and make business information usable by AI agents.

Design, build and maintain scalable data pipelines and BI infrastructure. Ensure secure, high-quality data availability through modelling, performance tuning, governance, and collaboration with analysts and stakeholders. Mentor peers, review code, and introduce new tools and best practices.

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.

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 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.

Own end-to-end industrial software deployments, from defining use cases and onboarding machine and production data through validation, rollout, training, and adoption. Work directly with operators, plant managers, IT, and OT teams to solve production problems. Build KPIs, anomaly signals, analytical workflows, and root-cause analyses using Python, SQL, statistics, and time-series data. Lead customer projects, identify expansion opportunities, and feed field insights back into the product.

Assesses satellite imagery quality through manual review and automated tooling. Builds and maintains QGIS-based tools, Python scripts, and geospatial data pipelines; supports investigations into customer quality disputes; contributes to automated quality-assessment systems; and documents technical findings for engineering teams. The role combines optical remote sensing expertise, software development, visual QA, and technical communication in a hybrid Berlin office setting.

Lead cloud engineering and DevOps for data platform teams. Design secure multi-tenant Azure infrastructure, CI/CD pipelines, deployment automation, observability, Kubernetes environments, and infrastructure-as-code. Establish shared cloud and delivery standards, troubleshoot across environments, improve operational reliability, and mentor engineers. Collaborate with architects, product managers, developers, and data engineers on security, governance, cost optimization, and delivery practices.

Lead the design, implementation, and operation of enterprise reporting and analytics platforms. Build scalable pipelines, master data structures, lakehouse and warehouse models, Power BI semantic models, dashboards, and cost-rate databases. Ensure data quality, governance, security, lineage, and auditability while supporting DevOps, deployment, monitoring, and production operations. Partner with finance, operations, and senior stakeholders to deliver trusted data products and business outcomes across complex enterprise systems.

Build and maintain scalable backend services, APIs, data pipelines, orchestration workflows, and data infrastructure. The role focuses on reliable ingestion, processing, storage, distribution, observability, automation, and high data quality. It involves production troubleshooting, architecture decisions, and collaboration with software, data, and infrastructure engineers, primarily within a financial services and commodities trading environment.

Lead development of Data and AI features from data architecture to optimization and simulation. Design scalable data architectures, reliable pipelines, and DataOps/DevOps workflows. Partner with analysts, product managers and supply chain specialists to turn complex logistics data into actionable solutions.

Design and maintain high-throughput data replication pipelines connecting platform systems with reporting tools. Evaluate storage engines and data models across MySQL, OpenSearch, BigQuery, and ClickHouse, modernize legacy processing architecture, and establish scalable information structures. Drive monitoring, reliability, operational excellence, and secure migrations while collaborating with backend and analytics engineers and setting technical standards.