Top Data Engineer Jobs in Munich

As a Data Engineer at Zapier, you'll build scalable data systems, enhance product functionality through data, and collaborate with teams to improve data access and usability.

As a Principal Database Engineer, you will architect scalable PostgreSQL solutions, ensuring data reliability, performance, and collaborating with teams to build a robust data platform for GitLab's growth.

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

As a Middle Data Engineer, migrate SQL Server data warehouses to Snowflake, refactor ETL processes, build data pipelines, and ensure reliable data movement.

As a Data Engineer at Screenverse, you'll build ETL pipelines, ensure data quality, and contribute to data strategy in a collaborative environment.

The Data Engineer will design and implement data infrastructure, build scalable data pipelines, create data models, and automate workflows while collaborating with various teams to enhance analytics capabilities.

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.

N-iX seeks a Data Engineer with MLOps experience to create scalable ML pipelines, deploy models, and contribute to best practices in data engineering and coding.

As a Data Engineer, you'll design, develop, and implement high-performance data infrastructure and pipelines while communicating effectively with various stakeholders.

The (Senior) Data Engineer designs and maintains data pipelines, collaborates with clients on data needs, and develops dashboards for insights.

The Data Engineer will design and maintain ETL pipelines, optimize data models, develop exporters, and ensure reliable data services for the Lambda app in a fast-paced startup environment.

Develop interactive data visualizations and enhance the chart library for Celonis, collaborating with cross-functional teams and advocating for UX best practices.

Design, build, and optimize scalable data platforms using Azure and Databricks. Responsible for developing ETL/ELT pipelines, maintaining data quality, and collaborating with analytics teams.

As an Analytics Engineer, you will design and maintain SQL models for on-chain data, work with transformation tools, and collaborate with engineers on data pipelines.

Design and architect scalable backend services, develop robust governance systems, build SaaS solutions, and mentor junior engineers to enhance IoT platform capabilities.

As a Data Engineer, you'll design and maintain ETL pipelines, write TypeScript code, and collaborate with stakeholders to ensure data quality and performance.

As a Data Engineer, you'll maintain and configure the Snowflake data platform, design integration pipelines, and innovate with new tools. Your role supports data security and performance while collaborating across teams.

The Junior Data Engineer will analyze data, design experiments, build a marketing data stack, and collaborate with teams to improve data access and insights.

As a Data Engineer at Helsing, you will lead and automate data acquisition and labeling processes for AI development, support data preparation pipelines, and collaborate with engineering teams.

The Senior Data Engineer designs, maintains, and optimizes ML applications and data pipelines, ensuring compliance and quality while mentoring junior engineers.

Develop scalable cloud-based data platforms for analytics, design data pipelines in AWS, and support reporting solutions for stakeholders.

Develop and maintain healthcare data management systems, including APIs and microservices. Collaborate with teams to innovate solutions in healthtech.

As a Senior Data Engineer, you will build complex data platforms and pipelines, mentor engineers, and improve quality assurance processes, focusing on Data and AI projects.

Responsible for developing backend AI features, integrating machine learning models, building data pipelines, and maintaining AI services within a retail ecosystem.

Drive the development of Data and AI features, design scalable data architectures, ensure reliability and accuracy of data solutions, shape DevOps workflows, and collaborate with analysts and product managers to transform complex data into practical solutions.