Top Data Engineer Jobs in Germany

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, optimize, and maintain data pipelines and data systems using Python and PySpark. Evaluate and annotate AI-generated ETL outputs, ensure data accuracy and scalability, integrate data solutions with software teams, document processes, follow data governance, and support backlog and best practices under senior guidance.

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

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.

Build and own ABOUT YOU’s modern data platform, including Dagster, dbt, AWS ECS, and Google BigQuery infrastructure. Maintain high-volume ELT pipelines moving terabytes of data from MySQL to BigQuery while protecting source-system performance. Deliver critical executive reports and develop tooling for monitoring, data quality, connectors, cost control, testing, CI/CD, and cataloging. Collaborate pragmatically with engineering and business stakeholders using strong software engineering, SQL, Python, and communication skills.

Own and evolve the company’s data platform using Dagster, dbt, AWS ECS, and Google BigQuery. Build foundational tooling, monitoring, data checks, connectors, testing, CI/CD, cataloging, and cost controls. Maintain high-volume ELT pipelines moving terabytes from AWS MySQL to BigQuery while protecting source-system performance. Deliver critical executive reports and enable other data teams through reliable, scalable data infrastructure.

Build and ship full-stack features for a data intelligence SaaS platform, spanning backend services and frontend interfaces. Use AI coding assistants to accelerate development while applying sound engineering judgment. Participate in architecture discussions, code reviews, testing, incident response, troubleshooting, documentation, and knowledge sharing. Partner with product managers to translate requirements into technical solutions. The role emphasizes SaaS architecture, AWS cloud fundamentals, Docker, Kubernetes, CI/CD, agile delivery, autonomy, and production ownership.

Design high-speed data connector and PCB solutions using SolidWorks, CATIA, Altium Designer, and Mentor Graphics Xpedition. Apply DFM/DFT principles, signal integrity practices, and ANSYS FEA/CFD simulations to evaluate manufacturability, structural integrity, thermal behavior, and electrical performance. Manage cost, mass, timing, design changes, documentation, revisions, and product data through Siemens Teamcenter PLM.

Design and optimize Snowflake data models, customer 360 views, identity resolution, segmentation, and CDP-ready schemas. Build pipelines syncing customer data to Braze, Hightouch, and related platforms. Improve performance, cost efficiency, data quality, governance, consent management, and audience activation while collaborating with CRM, marketing, analytics, and technical stakeholders.

The Finance Data Engineer will design and deploy automated pipelines, ensure finance data integrity, and implement AI-driven solutions while maintaining data sovereignty and compliance with government contracts.

Build and operate scalable batch and streaming data solutions supporting fraud prevention. Develop point-in-time-correct datasets, historization, data quality controls, observability tooling, and reliable pipelines for model training, inference, backtesting, and fraud-rule evaluation. Partner with Data Science and Fraud Operations teams to deliver resilient data products and maintain consistency between batch and real-time processing.

Build and maintain Camunda 8 backend data-layer services across relational databases and Elasticsearch/OpenSearch. Design scalable storage, query, retention, streaming, and secondary-storage solutions; investigate production data issues; improve performance and operability; and deliver engineering initiatives collaboratively with Product, QA, Support, and SRE. Participate in L3 support and on-call rotations.

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.

Lead mechanical due diligence and feasibility for data center sites and deals. Validate cooling topology and equipment strategy (air, liquid, hybrid), assess capacity, vendor lead times, and integration with budgets and schedules. Define mechanical readiness gates, deliver validated scope to Delivery, coordinate with stakeholders and vendors, and help build repeatable evaluation standards for AI/hyperscale infrastructure acquisitions.

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.

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.

Designs and implements security frameworks for Snowflake and cloud data platforms across GCP and Azure. Automates infrastructure provisioning and security configurations with Terraform, manages GitHub Actions CI/CD security, and optimizes security solutions for threat detection and response. The role requires enterprise data protection, access control, DLP, DevSecOps, SIEM, cloud security, programming, documentation, and cross-functional collaboration with internal teams and vendors.

As a Senior Data & AI Engineer, you'll advise clients on data and AI solutions, analyze requirements, design tailored data solutions, and collaborate with project teams to deliver results.

As a Lead Data Engineer, you will design, build, and maintain scalable data platforms, assess data landscapes, and collaborate with stakeholders to translate requirements into robust data solutions.

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

Design, build, and optimize real-time and batch data pipelines using Apache Beam and GCP services. Integrate multiple data sources, ensure data quality and observability, improve pipeline performance and cost efficiency, and apply CI/CD, infrastructure-as-code, monitoring, governance, security, and compliance practices. Collaborate with data scientists, analysts, and software engineers to deliver reliable data for analytics, AI, and business intelligence.

Own the architecture and development of scalable data models using Data Vault 2.0, dbt, BigQuery, and Airflow. Establish modeling standards, data quality, lineage, governance, testing, and CI/CD practices. Optimize cloud data warehouse performance and costs, oversee orchestration workflows, collaborate with analytics and business stakeholders, mentor engineers, and address scalability challenges across modern cloud data platforms.

Build scalable data pipelines and architectures for data and AI products, lead cloud data migration to Azure, optimize large-scale datasets, and support reporting and analytics. Collaborate with analysts, ML engineers, DevOps, backend, product, and business teams. Promote clean code, automation, maintainability, and continuous improvement across the data platform.

The Staff Engineer will define architecture, lead data pipeline design, ensure platform reliability, and mentor engineers, driving data initiatives and technical standards.

Design and maintain data warehouse systems, develop ETL pipelines, manage datasets for analytics, and implement real-time data solutions.