Top Data Engineer Jobs in Berlin

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.

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

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.

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.

As a Data Engineer, you'll build and manage data pipelines for cement and concrete plant data, ensuring efficient data flow for machine-learning models while collaborating with customers and teams.

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.

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

As a Data Engineer, you will design and build data pipelines, models, and infrastructure for analytics and machine learning while ensuring data quality and compliance.

Design, implement, scale, and operate Python APIs, backend services, and end-to-end data pipelines on cloud platforms. Build event-driven real-time/asynchronous processing, apply data modeling and database design, enforce CI/CD and testing, drive observability and performance, and own services from schema and API design to deployment and monitoring.

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.

Lead design and implementation of a scalable modern data platform and real-time pipelines. Build, optimize, and maintain data models, ETL/ELT pipelines and integrations (Kafka, Spark, Delta Live Tables), enforce data governance and quality, enable AI/ML workflows and MLOps, mentor the team, evaluate new technologies, and collaborate with product and analytics stakeholders to deliver reliable, self-service data products.

Build and maintain scalable ETL/data pipelines and orchestration for content publishing, optimize and monitor data infrastructure, automate processes, and collaborate with data and content teams.

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 maintain data pipelines and features to improve supply-chain vendor experience. Ensure pipeline reliability and scalable cloud architectures, implement CI/CD for ML models, evolve system architecture and engineering practices, and collaborate with data scientists to optimize model lifecycle, monitoring, and alerting.

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.

As a Senior Data Engineer, you will design scalable data pipelines, implement new technologies, collaborate with Data Scientists, and lead technical aspects while maintaining documentation on data processes.

As a Senior Data Engineer, you'll design, implement, and optimize scalable data pipelines and Data Lake architecture, collaborate cross-functionally, and ensure data reliability and quality in a rapidly growing AI-driven environment.

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.

The Associate Director will lead mechanical design for data centres, manage project delivery, engage with clients, mentor junior engineers, and stay updated on industry trends.

As a Senior Data Scientist, you will build and maintain ML models for AML transaction monitoring, enhance features, and ensure regulatory compliance by collaborating with cross-functional teams.

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