Top Data Engineer Jobs in Germany

Own and evolve the core data platform: warehouse design, dbt transformations, and production AWS pipelines. Refactor and harden API-driven data flows, ensure observability and testing, partner with backend and product teams, and contribute to data-aware backend features to deliver reliable, product-focused data.

Own and harden the platform: tune HikariCP/ProxySQL/XtraDB connection paths, eliminate DB locking in JobRunr/Quartz queues, optimize Hibernate/JPA usage, manage Kubernetes availability and graceful shutdowns, instrument observability with Prometheus/Grafana, and plan migration to high-throughput messaging (NATS/RabbitMQ/Redis).

Develop backend services using Rust for cloud-native systems processing terabytes of data. Contribute to innovation in observability solutions.

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 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.

Lead architecture and development of scalable simulation, validation, and data platforms for autonomous robotics. Build fleet data collection, telemetry pipelines, and cloud-native ingestion/processing systems. Architect SiL/HiL and large-scale virtual testing frameworks, automate validation pipelines, and drive system-level verification. Provide technical leadership across robotics, autonomy, embedded, and platform teams, mentor engineers, and support field integration and operational readiness.

As a Senior Data Engineer, you'll build and manage data pipelines, ensure data quality, and collaborate across teams, contributing to the Data Platform's growth.

Lead migration of legacy Hadoop/Spark/Impala data pipelines to a cloud-native Databricks platform. Design and build scalable Airflow/Databricks ETL pipelines, ensure data quality, improve observability and reliability, and use AI coding assistants to accelerate modernization while validating performance and architecture.

Build and maintain high-performance, fault-tolerant distributed backend services and data pipelines for large genomic and health datasets. Design storage, indexing, and async event workflows; optimize latency, throughput, and reliability. Partner with bioinformatics and product teams to validate scientific accuracy and guard AI-driven responses.

Lead technical vision and architecture for enterprise data platforms. Own end-to-end platform design from ingestion to distribution, set engineering standards, mentor senior engineers, and collaborate with security, compliance, product, and stakeholders to deliver scalable, reliable, and compliant data platform capabilities.

Design, build, and operate scalable data infrastructure on AWS and Kubernetes. Develop streaming and platform components (Kafka, Snowflake), implement IaC (Terraform) and CI/CD (GitLab), build monitoring/observability, own features end-to-end, collaborate with analysts and data scientists, and participate in on-call rotations to ensure platform reliability and performance.

Build end-to-end features for a data orchestration platform across Java/Micronaut backend and Vue 3/TypeScript frontend. Implement data-aware orchestration (assets, lineage, freshness, quality), create integrations/plugins (dbt, Fivetran, Airbyte, warehouses), improve UI (flow editor, topology, dashboards), collaborate on 8-week releases, review PRs, and engage with the open-source community.

Build and maintain scalable backend services, data ingestion pipelines, and data platforms. Improve reliability, observability and cost-efficiency of data infrastructure, develop reusable tooling, automate orchestration, and collaborate with engineering and data teams to deliver high-performance data solutions.

Lead design and implementation of customer-facing data intelligence applications: build shared API layers, low-latency data-access and caching strategies, advanced interactive visualizations, LLM-enabled interfaces, and mentor engineers while shaping architecture and standards.

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.

As a Software Engineer, analyze and process real-time athlete data to enhance performance and reduce injury risks, developing algorithms and optimizing data pipelines.

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

Design and build highly scalable, low-latency market data systems and APIs. Implement backend services, influence architecture, maintain test coverage, participate in code reviews, troubleshoot incidents, and occasionally be on-call. Work spans streaming and historical market data endpoints and developer-focused API improvements.

Design, build, and maintain end-to-end ETL pipelines and self-service data tooling for Constellation Services. Collaborate with cross-functional stakeholders to deliver monitoring, visualizations, and insights, mentor peers, and evolve data architecture, CI/CD, and observability for geospatial and event-driven data systems.

Design and automate large-scale data pipelines (dbt, Airflow), optimize Trino/Athena SQL over multi‑TB daily volumes, implement memory-efficient Python processing, run rigorous A/B test analysis, mentor analysts, and translate analytics into stakeholder-facing dashboards and recommendations.

Translate factory requirements into PLDM and ERP implementations, develop and improve PLM toolsets, support industrialization and supply chain, manage change requests via CCB, drive global knowledge sharing, enable adoption through releases and training, and ensure alignment of quality system elements, procedures and documentation.

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.

Own and operate Databricks-based data platform production support, build and migrate ETL/ELT pipelines to Databricks across Azure/AWS, optimize compute and performance, enforce Delta Lake architecture and data security, implement observability and CI/CD, support multi-tenant onboarding and on-call incident response, and maintain runbooks and SLA metrics.

Drive the complete tape-out flow including DGS database integration, mask data preparation, and DfX co-integration for graphene photonic platforms.