Top Data Engineer Jobs in Berlin

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.

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.

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.

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.

Design, operate, and optimize scalable ELT pipelines and platform infrastructure for international healthcare data. Build entity-resolution and MDM workflows, implement schema governance and data contracts, optimize cloud storage and warehouse costs, and automate CI/CD for data pipelines while partnering with analytics and domain experts to deliver production-ready datasets.

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.

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.

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.

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

Build and operate a scalable AWS-based data platform and lakehouse. Develop and maintain data pipelines, ingest internal and third-party data, prepare/clean data, create data models for reporting, analytics and AI, and implement testing and monitoring. Collaborate with product and engineering teams and share best practices.

Design, build, and maintain scalable ELT data pipelines with Airflow; develop dbt data models and tests; optimize Athena tables/queries and data workflows; provision infrastructure with Terraform; ensure data quality and reliability; collaborate with Analytics, BI, and Engineering; troubleshoot pipeline and data quality issues.

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

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.

The role involves building and maintaining data pipelines using various technologies, collaborating with stakeholders, monitoring data health, and promoting team knowledge sharing.

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

As a Data Engineer, you will develop and manage data pipelines, ensuring reliable data ingestion and delivery while optimizing for simplicity and performance.

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 Data Engineer role focuses on developing predictive models and enhancing data engineering practices to support business objectives. Responsibilities include writing clean code, model evaluation, and collaboration with a global team to improve reporting and campaign management.

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.