Top Data Engineer Jobs in Germany

Design and implement core YTsaurus functionality in C++, build and evolve platform-level capabilities, improve APIs and UX for internal users, own production quality including incident response/on-call, and collaborate with stakeholders to prioritize and deliver scalable data platform features.

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.

Support and maintain Earth Observation data processing systems: investigate anomalies, perform software release and deployment support, conduct IV&V and system testing, produce configuration-controlled technical documentation, and participate in technical reviews and engineering boards.

Lead technical direction and architecture for the data platform, mentor a team of engineers, own data infrastructure (lake, foundations, semantic layer, governance), drive streaming adoption, apply strong data modeling, and collaborate with product and engineering stakeholders to enable AI-powered analytics and a self-service data platform.

Lead technical direction and architecture of the data platform, mentor a team of 4–5 engineers, evolve data infrastructure (lake, semantic layer, governance), drive streaming adoption, data modeling, and self-service platform practices while collaborating with product and cross-functional stakeholders.

Lead technical direction and architecture of the data platform, mentor a team of 4-5 engineers, own data infrastructure (lake, foundations, semantic layer, governance), drive streaming adoption, advance data modeling, and collaborate with product and engineering stakeholders to enable AI and analytics features.

Join Zencore as a Senior Data Engineer, leveraging Google Cloud expertise and technology strategies to assist clients with data and application modernization.

The role involves data collection to support AI model training, managing cloud infrastructure on GCP, and improving data ingestion processes.

Lead design and build of high-throughput Reverse ETL and data pipelines, modernize pipeline architecture, choose appropriate storage and data models (MySQL, ClickHouse, OpenSearch, BigQuery), drive observability and reliability (Datadog), collaborate with backend and analytics engineers, and set engineering standards to scale sponsored content and CPC marketplaces.

Lead design, implementation, and operation of scalable data pipelines and platform infrastructure on AWS. Define data quality, observability, and operational controls; evolve analytics (ClickHouse, Metabase); build pipelines with Python (pandas, polars), dbt, and dlt; drive cross-team initiatives, mentor engineers, and improve platform reliability and developer experience.

Lead architecture and implementation of real-time data pipelines and dynamic tables at cloud scale. Drive technical strategy, resolve systemic bottlenecks, mentor senior engineers, run design reviews, partner with product and infrastructure, and deliver hands-on work in streaming ingestion and incremental processing.

Identify and prioritize AI use cases with business stakeholders; design, develop, deploy, monitor, and optimize AI-enabled data products end-to-end; implement and improve models (pricing, prediction, agents); collaborate across Business, IT, and Data to scale AI solutions and ensure robust model performance and industrialization.

The Senior Data Engineer will design and build data pipelines on GCP, ensuring high data quality and reliability for analytics and ML products. Responsibilities include mastering ETL processes, managing workflows with Airflow, and collaborating closely with ML engineers.

Own and scale the ingestion pipeline and backend abstractions for high-throughput AI tracing data. Optimize ClickHouse schemas and queries, operate Langfuse Cloud and self-hosted deployments, and build infrastructure-heavy features that enable product engineers to ship data-heavy capabilities.

Leading and mentoring a data platform engineering team while overseeing platform architecture, development, and compliance with security and governance in a cloud environment.

Build and maintain ML-scale data infrastructure: feature store, real-time and batch pipelines, data quality and validation, and low-latency systems. Translate Data Science needs into high-performance Flink streams, eliminate ETL bottlenecks, collaborate on online ML systems, and implement monitoring, governance, and deployment processes for production ML workflows.

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.

The DevOps Engineer will maintain application environments, ensure infrastructure availability, oversee deployment processes, and lead training sessions within the team.

Support Special Operations customers by collecting, cleaning, integrating, analyzing, and visualizing multi-source data. Develop dashboards, trackers, and executive-level data products; present findings and recommendations to stakeholders. Translate mission requirements into repeatable data solutions that inform planning, assessment, and decision-making.

Develop and integrate software for data processing and visualization to support engineering teams. Guide junior engineers, create web applications, and improve performance.

Lead design and delivery of scalable, secure Snowflake-based data platforms. Define architecture standards, build DBT ETL/ELT pipelines, enforce governance, ensure GDPR and pharma compliance, implement CI/CD/DataOps, optimize performance, mentor teams, and collaborate with global stakeholders for analytics and clinical insights.

As a Software Engineer for Data Infrastructure & Acquisition, you will manage data collection for model training, operate cloud infrastructure, and collaborate on dataset roadmaps.

Build and maintain robust, production-grade data foundations for R&D semiconductor data. Design data models and pipelines, implement automated data quality checks, improve traceability and metadata, and support digital twin, analytics, and workflow automation use cases. Evaluate AI/ML versus rule-based approaches and establish engineering standards for testing, monitoring, versioning, and documentation.