Top Data Engineer Jobs in Munich

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.

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.

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.

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.

Design, build, and operate scalable data systems and ETL pipelines for research and live trading. Transform raw data into reliable datasets, collaborate with researchers, manage storage systems, and maintain production observability and performance.

Build high-fidelity simulation environments and synthetic camera and LiDAR data for autonomous off-road vehicles. Develop scenarios, data augmentation, domain randomization, and Sim2Real pipelines to improve perception and autonomy systems. Integrate simulation modules with ROS2 robotics software and collaborate with AI and robotics teams to accelerate model training, testing, and validation.

The role involves analyzing data for insights, creating dashboards, and collaborating with teams to optimize product performance using SQL, Python, and visualization tools.

Build auditable cloud data pipelines and perform statistical analyses on clinical and medical imaging datasets. Apply classical ML, deep learning, and LLM methods to extract and analyze clinical data, maintain data lineage and reproducibility, create dashboards and observability tooling, and produce evidence suitable for regulatory submissions and audits.

Build, optimize, and maintain data pipelines and data systems using Python and PySpark. Evaluate and annotate AI-generated ETL outputs, ensure data accuracy and scalability, integrate data solutions with software teams, document processes, follow data governance, and support backlog and best practices under senior guidance.

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.

The Finance Data Engineer will design and deploy automated pipelines, ensure finance data integrity, and implement AI-driven solutions while maintaining data sovereignty and compliance with government 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 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.

Architect and build a greenfield data warehouse, develop resilient ETL/ELT pipelines, ensure data integrity, and lead junior engineers. Work on modern data infrastructure with a focus on AI integration.

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

Build and ship full-stack features for a data intelligence SaaS platform, spanning backend services and frontend interfaces. Use AI coding assistants to accelerate development while applying sound engineering judgment. Participate in architecture discussions, code reviews, testing, incident response, troubleshooting, documentation, and knowledge sharing. Partner with product managers to translate requirements into technical solutions. The role emphasizes SaaS architecture, AWS cloud fundamentals, Docker, Kubernetes, CI/CD, agile delivery, autonomy, and production ownership.

Design and optimize Snowflake data models, customer 360 views, identity resolution, segmentation, and CDP-ready schemas. Build pipelines syncing customer data to Braze, Hightouch, and related platforms. Improve performance, cost efficiency, data quality, governance, consent management, and audience activation while collaborating with CRM, marketing, analytics, and technical stakeholders.

Build and maintain Camunda 8 backend data-layer services across relational databases and Elasticsearch/OpenSearch. Design scalable storage, query, retention, streaming, and secondary-storage solutions; investigate production data issues; improve performance and operability; and deliver engineering initiatives collaboratively with Product, QA, Support, and SRE. Participate in L3 support and on-call rotations.

Own and scale the ingestion pipeline and backend abstractions for high-throughput AI tracing data. Optimize ClickHouse schemas and queries, operate Langfuse Cloud and self-hosted deployments, and build infrastructure-heavy features that enable product engineers to ship data-heavy capabilities.

Lead mechanical due diligence and feasibility for data center sites and deals. Validate cooling topology and equipment strategy (air, liquid, hybrid), assess capacity, vendor lead times, and integration with budgets and schedules. Define mechanical readiness gates, deliver validated scope to Delivery, coordinate with stakeholders and vendors, and help build repeatable evaluation standards for AI/hyperscale infrastructure acquisitions.

Build and maintain scalable data pipelines, transformations, and analytics-ready models using dbt, SQL, Python, and Snowflake. Implement ingestion, CDC, API integrations, testing, monitoring, and observability. Collaborate with analysts and stakeholders to define metrics, ensure data governance and compliance, and optimize data platform reliability.

The Staff Engineer will define architecture, lead data pipeline design, ensure platform reliability, and mentor engineers, driving data initiatives and technical standards.

Design and maintain data warehouse systems, develop ETL pipelines, manage datasets for analytics, and implement real-time data solutions.