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.

Build and maintain automated, near real-time data pipelines and BI solutions to optimise cloud, SaaS, and AI costs. Deliver analytics, dashboards, anomaly detection and forecasting using AI/ML, collaborate with users for excellent data experience, and contribute to data and software architecture in a multi-cloud FinOps environment.

Build and maintain scalable AWS-based data pipelines and a lakehouse, prepare and model structured and unstructured data for reporting, analytics and AI, implement testing/monitoring, collaborate with product and engineering teams, and promote best practices across the data organization.

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 and build a scalable data layer for an AI-powered SAP consulting system. Own metadata architecture, develop pipelines ingesting SAP telemetry and consulting insights, and collaborate with AI/ML teams to provide high-quality signals for training and inference while ensuring data integrity, versioning, and long-term scalability.

Lead research, design, and production implementation of algorithms and predictive models for warehouse scheduling, planning, and routing. Deliver end-to-end solutions from prototype to highly available production services, mentor applied scientists, and influence scalable, data-driven architecture and development methodologies.

Build and maintain pipelines that process petabytes of raw and semi-raw data, transform large-scale datasets about domains and brands, implement analytics for marketers and SEO analysts, and deploy/manage production systems using Go, ClickHouse, and CI/CD practices.

Design, build, and maintain ELT pipelines and dbt SQL models for analytics; optimize performance, scalability, and costs; apply AI-enabled tools to improve engineering workflows; collaborate to translate requirements into production-grade data assets.

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.

Lead development and scaling of data processing pipelines for road and navigation products. Design and implement new pipelines, monitor performance metrics, and enforce security best practices. Collaborate within a cross-functional team to build high-quality, distributed data systems supporting mapping and navigation features.

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.

Design, build, and operate scalable Databricks data pipelines and Lakehouse data models using SQL, Python, and PySpark. Implement ELT/ETL and streaming patterns, data quality checks, Unity Catalog governance, performance optimization, monitoring, documentation, and operational support.

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

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

As a Data Engineer at GALVANY, you'll architect data infrastructure, process real-time energy data, build scalable data pipelines, and ensure data quality for an AI-based Operating System.

Build and maintain the central data platform powering domain Analytics Engineering and BI: evolve dbt platform, enable self-service tooling, manage IaC and GitOps, enforce federated governance and PII handling, add observability/testing, and partner with domain teams to unblock and enable safe, scalable model delivery.

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.

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.