Top Data Engineer Jobs in Berlin

Lead development of Data and AI features from data architecture to optimization and simulation. Design scalable data architectures, reliable pipelines, and DataOps/DevOps workflows. Partner with analysts, product managers and supply chain specialists to turn complex logistics data into actionable solutions.

Design, build, and operate large-scale distributed data systems and real-time/batch data pipelines. Collaborate with product, infrastructure, and data science teams, contribute to architecture and roadmaps, and write clean, tested production-quality code.

Design, build, and operate scalable real-time and batch data processing systems. Own features end-to-end, make architectural decisions, ensure production reliability and observability, collaborate cross-functionally, write high-quality tested code, and mentor junior engineers.

The Technical Privacy Expert will implement privacy-by-design principles, ensure compliance with regulations, mentor teams, and develop technical frameworks for privacy in technologies.

Own and build the internal data warehouse and analytics platform: consolidate product and operational data, define data models and contracts, create transformation pipelines, enable AI/ML datasets, implement data quality, lineage, and observability, and collaborate with stakeholders to support BI and analytics.

Lead and operate production-grade data pipelines and streaming systems to ingest, enrich, process, and serve web event and intent data at scale. Own cloud infrastructure as code, observability, data quality, and CI/CD. Collaborate with product and ML teams to deliver reliable, low-latency data powering customer-facing features and AI/ML workflows.

Lead design and implementation of scalable, real-time data infrastructure. Build and optimize production-grade streaming pipelines, employ spec-driven development and agentic AI workflows, implement testing/monitoring, take ownership of systems, and mentor junior engineers.

Build and operate large-scale data pipelines and orchestration for generative AI research, focusing on video/audio datasets, feature extraction, labeling, and dataset quality to improve model performance.

Design and develop cloud-native backend services in Rust to process terabytes of data daily. Build and operate data-intensive microservices on AWS and Kubernetes, integrate with distributed databases and messaging systems (Kafka/RabbitMQ), and collaborate across teams to deliver observability features.

As a Senior Software Engineer, you'll design and build scalable market data systems, improve API architecture, and enhance overall system performance, contributing to Alpaca's infrastructure for trading financial assets.

Build and operate production-grade data, evaluation, and observability infrastructure for LLM agents. Implement online/offline evaluation, quality gates, data pipelines, trace analysis, retention and replay, and observability tooling while working inside the backend and agent architecture to improve reliability, performance, and cost.

Build and operate Kafka and CDC pipelines, help architect an Iceberg-backed Lakehouse, deliver scalable data serving layers, enable self-service analytics, drive data observability, and consult teams on integration, modelling, and schema design.

The Senior Data Platform Engineer will lead project delivery for data infrastructure, manage near real-time services, and collaborate across teams while contributing to AI and data governance frameworks.

As a Senior Software Engineer at ClickHouse, you'll enhance the Python data ecosystem by creating tools and integrations for data practitioners, focusing on performance and reliability while collaborating with various teams.

Own and evolve the end-to-end customer identity resolution pipeline, improve probabilistic matching models, build scalable BigQuery/GCS/Cloud Run data pipelines, add monitoring and validation, and collaborate with stakeholders to deliver reliable, production-grade identity master data.

Lead ownership of data ingestion and warehouse pipelines, manage and improve the data platform (Kafka -> BigQuery), implement DWH best practices, collaborate with DevOps and R&D, and support data modelling to enable company-wide analytics.

The Senior Data Platform Engineer at Taxfix will design and maintain data infrastructure, build pipelines for analytics and compliance, ensure data quality, and prepare data for AI projects.

Build, operate, and maintain scalable data infrastructure on AWS and Kubernetes. Develop streaming and data platform components (Kafka, Snowflake), implement IaC (Terraform) and CI/CD (GitLab), add monitoring/alerting (Datadog), support stakeholders, participate in on-call rotations, and own features end-to-end to ensure reliable data availability for analytics and operations.

Own and evolve a GCP-native data platform from ingestion to reporting. Lead architecture, reliability, observability, data quality, cost optimization, and AI-assisted engineering practices while mentoring engineers and collaborating with product and customer teams.

Design, develop, and deploy production-grade ML and AI solutions including LLM fine-tuning, RAG architectures, NLP pipelines, time-series forecasting, supplier risk and anomaly detection, scalable data pipelines, and RESTful integrations with enterprise systems.

As a Senior Data Architect, you'll design and evolve data architecture, ensuring scalability and reliability, define data modeling standards, build data pipelines, enhance data quality, and collaborate with teams to optimize data as a strategic asset.

Design and develop data models, ETL processes, and cloud-based data solutions using Azure and various big data technologies. Support report generation and data analysis.

Design and maintain scalable data pipelines and platforms, lead migration from Snowflake to client service, propose improvements, drive initiatives, and collaborate with senior engineers, product/project managers, and data analysts.

Own and evolve Enode's data platform and pipelines to support the Flex product: build reliable datasets, ensure data quality and monitoring, enable self-serve analytics, and partner with data science on feature pipelines and model readiness. Drive pragmatic platform improvements, documentation, and tooling to scale data reliability and trust across delivery, billing, and reporting.

Lead delivery of end-to-end data and analytics solutions: build dimensional models, develop Power BI dashboards, perform exploratory analysis, design Azure-based data pipelines, capture client requirements, run scenario modelling, test and assure solutions, prepare client communications and lead project teams.