Top Data Engineer Jobs in Germany

Design, develop, and optimize GPU- and CPU-accelerated image and data processing libraries. Implement and performance-tune parallel numerical algorithms, benchmark across architectures, contribute to technical roadmaps, provide technical leadership, and collaborate with product and engineering teams to meet feature and performance requirements.

Build a greenfield analytics and AI foundation for a mobile game, including event tracking, data warehouse architecture, pipelines, semantic models, dashboards, automated reporting, and insight-generation workflows. Use agentic AI tools to accelerate engineering while owning correctness, quality gates, and production review. Analyze gameplay, monetization, attribution, and player behavior data to support product and leadership decisions, while establishing agentic engineering standards across the organization.

Build a greenfield data and analytics foundation for a mobile game, including event tracking, cloud data architecture, pipelines, semantic models, dashboards, automated reporting, and AI-powered insight workflows. Lead agentic engineering practices, review AI-generated code and analytics, ensure data quality, and deliver actionable intelligence on gameplay, monetization, attribution, and player behavior. Partner with product and leadership teams while driving analytics capabilities from concept through production.

Own and evolve the platform that turns research models and data into reproducible, auditable client deliverables. Lead Python-based data infrastructure, CI/CD, pipeline orchestration, GCP architecture, infrastructure-as-code, monitoring, access management, and geospatial workflows. Review designs, establish engineering standards, mentor engineers, and coordinate infrastructure priorities. The role also involves production data systems, schema design, model versioning, containers, observability, and collaboration with software, data, science, and solutions teams.

Own process safety, control philosophy, instrumentation, P&IDs, interlocks, and automation architecture for a chemical pilot and demo plant. Lead HAZOP, LOPA, SIL, ATEX, and regulatory activities; manage skid suppliers through design, FAT, and SAT; and oversee control cabinets and campaign operations. Build data acquisition, historian, batch-record, and modeling infrastructure using Python and process simulation tools to ensure safe, controlled, and reproducible chemical processing.

Build the company’s greenfield data foundation, including its warehouse, resilient ETL/ELT pipelines, governance, quality controls, and security for sensitive financial data. Select cloud and orchestration technologies, integrate complex third-party APIs, and support AI-driven finance products. The role offers opportunities to hire and mentor engineers and grow into leadership.

Design, build, and operate Python and TypeScript microservices, data pipelines, integrations, and LLM-powered workflows. Own PostgreSQL data modeling, asynchronous processing, observability, performance, deployments, and production incidents end to end. Drive architecture decisions, technical designs, testing, and monitoring while mentoring engineers. The role also contributes to web crawling, CRM and email integrations, automation, and AI-enhanced development workflows.

Build and evolve Trade Republic’s analytics data platform, including query experiences, conversational analytics, dashboards, integrations, and Airflow orchestration. Engineer resilient, observable systems at scale by defining SLOs, error budgets, testing, safe rollouts, incident reviews, and automated guardrails. Own the platform roadmap, partner with product managers, evaluate technology trade-offs, and create opinionated paved roads that simplify data use for hundreds of consumers.

Develop optimization and algorithmic solutions, build reliable AWS infrastructure and orchestration pipelines, write production-grade Python, manage CI/CD and CloudFormation deployments, monitor system health, and mentor junior engineers. The role contributes to redesigning a pricing engine that integrates optimization and forecasting models for outlet deals while translating business requirements into scalable software solutions.

Leads mechanical engineering design and delivery for complex data centre projects. Responsibilities include designing cooling, ventilation, automation, controls, and ancillary systems; coordinating with electrical and multidisciplinary teams; supervising engineers; producing and checking technical designs; managing resources and timelines; and maintaining client relationships. The role also involves applying building physics, CFD, sustainable design principles, CAD, BIM, and other digital design tools.

Own end-to-end industrial AI deployments, from defining customer use cases and onboarding machine data to validating analytics, training operators, and driving adoption. Diagnose production and data issues using Python, SQL, statistics, time-series analysis, KPIs, anomaly detection, and root-cause analysis. Collaborate with operators, plant managers, IT, OT, and commercial teams, manage customer projects, identify expansion opportunities, and feed field insights back into the product. The role is hands-on, customer-facing, Germany-based, and involves approximately 20% travel to customer sites.

Designs and builds scalable enterprise data pipelines, integrations, and lakehouse infrastructure connecting SAP, Siemens Teamcenter, engineering, manufacturing, and operational systems. Responsibilities include data modeling, ETL/ELT, orchestration, quality validation, metadata and lineage, monitoring, governance, and reusable data services. The role requires hands-on technical ownership, collaboration with enterprise stakeholders and software engineers, and establishing engineering standards for reliable analytics, AI/ML, automation, and application data access.

Support and maintain data pipelines in Microsoft Fabric, onboard data sources, transform and validate data across Bronze, Silver, and Gold layers, and maintain datasets for reporting, analytics, and AI initiatives. Collaborate with engineers, analysts, and stakeholders, monitor processing, resolve issues, document processes, and contribute to data platform modernization and continuous improvement.

Supports and maintains enterprise data processes across Google Cloud Platform and Microsoft Fabric. Responsibilities include monitoring and validating data flows, performing data quality and reconciliation checks, troubleshooting inconsistencies, maintaining ETL/ELT pipelines, onboarding integrations, documenting processes, and improving the data platform. The role collaborates with data engineering, infrastructure, analytics, and business teams to ensure secure, accurate, and trusted data for reporting and decision-making.

Own and evolve Grover’s data platform, including high-volume ingestion pipelines, AWS and warehouse automations, Apache Airflow orchestration, observability, data quality, migrations, and infrastructure reliability. Optimize Snowflake performance and costs through usage tracking and FinOps practices. Partner with Analytics Engineers and data champions to establish scalable architecture, technical standards, documentation, and decentralized analytics enablement.

Design and implement a greenfield Databricks Lakehouse for CRM data, integrating Salesforce, Redshift, and other sources. Build scalable ETL/ELT pipelines, Medallion architecture, ML infrastructure, reverse ETL, monitoring dashboards, and BI solutions. Provision Databricks and Unity Catalog with Terraform while enforcing GDPR, PII, governance, and security standards. Serve as technical design authority, mentor engineers, review code, and drive operational excellence.

Architect and build scalable batch and streaming data pipelines, cloud data warehouses, lakehouse infrastructure, feature stores, and AI-ready data workflows. The role emphasizes Python, SQL, Scala, dbt, Snowflake, Airflow, testing, observability, governance, CI/CD, Terraform, and agentic workflow development. You will collaborate with marketing, game, product, and analytics teams to deliver reliable, high-impact data solutions.

Manage Earned Value Management and project controls for a major data center project. Responsibilities include integrating Primavera P6 schedules with cost and resource baselines, maintaining control structures and Rules of Credit, verifying site progress, processing payment applications, analyzing EVM metrics, preparing performance and variance reports, training contractors, and supporting schedule, cost, risk, change, and forecasting activities.

Own and grow the Data Layer: build and operate ingestion, transformation, reconciliation, and serving pipelines (raw→refined→curated). Use agentic AI tools to accelerate delivery, ensure data correctness, and partner with backend, AI, and product teams to define reliable data contracts.

Staff Software Engineer responsible for designing, building, and operating distributed market data systems. The role focuses on real-time blockchain data ingestion, streaming, normalization, storage, APIs, reliability, data correctness, observability, incident recovery, and production support. The engineer will lead technical decisions, mentor teammates, translate ambiguous requirements into designs, and use AI-assisted development throughout the software lifecycle.

Lead the architecture and development of Core Data Services for customer-facing applications on SAP BTP and SAP RISE. Design modular services using RAP and CAP, integrate through SAP BTP Integration Suite, and implement authentication and authorization with XSUAA. Provide technical leadership through architecture decisions, code reviews, mentoring, release governance, documentation, and deployment readiness. Contribute to AI-enabled solutions and deliver secure, scalable applications for regulated financial services environments.

Build and maintain data pipelines, infrastructure, data models, and automation for analytics and machine learning workflows. Collaborate with ML engineers on model training, quality assessment, and releases. Improve internal data delivery processes and enable efficient access to large-scale organizational data.

Build and maintain data pipelines and infrastructure for analytics, experimentation, and company metrics. Collaborate with machine learning engineers on training, quality assessment, and model release automation. Improve data delivery processes, automate manual workflows, and develop scalable data models. The role requires strong Apache Spark and Python expertise, SQL proficiency, Git experience, and familiarity with cloud data platforms and Kubernetes.

Architect, implement, scale, and maintain petabyte-scale data processing platforms for frontier AI models. Build reliable, observable, cost-efficient pipelines with heterogeneous and GPU-accelerated workloads, including failure recovery and restartability. Ensure datasets and intermediate outputs are versioned, reproducible, traceable, and documented with datasheets. Partner with AI researchers to integrate datasets into training pipelines and guide sustainable technology decisions.

Leads strategic data analytics initiatives for Security Services, delivering executive insights, predictive models, machine learning pipelines, data pipelines, dashboards, and automated workflows. Partners with engineering, product, and senior leadership to translate complex business challenges into data-driven strategies, improve data governance, and implement AI and emerging technologies.