Top Data Engineer Jobs in Germany

Analyze oilfield and sensor data to optimize well and reservoir production, define operational strategies, collaborate with customers to deliver data-driven insights, and contribute to development and validation of flow sensors and data-driven measurement systems using reservoir simulation and machine learning.

Build and operate production data and machine learning systems for algorithmic pricing. Responsibilities include designing scalable pipelines, enabling model training and deployment, productionizing models, monitoring performance, managing Databricks and Kubernetes workloads, creating dashboards and alerts, and translating business requirements into reliable software. The role also emphasizes testing, code review, CI/CD, observability, system reliability, and collaboration with scientists, product managers, engineers, and commercial stakeholders.

Designs and builds scalable data pipelines, architectures, and data products; leads cloud data migration to Azure; transforms large datasets for analytics and reporting; and collaborates with analysts, ML engineers, DevOps, developers, product managers, and stakeholders. The role also promotes clean code, automation, maintainability, and continuous improvement across data platforms.

Own and optimize high-throughput batch and streaming data pipelines, distributed Apache HBase storage, and production infrastructure for maritime telemetry and predictive analytics. Operate containerized AWS environments using Kubernetes, Docker, Terraform, and GitOps; establish reliability metrics and operational SLAs. Collaborate with data science, product, and SRE teams, mentor engineers, and promote strong code review and AI-assisted development practices.

Build and operate production data pipelines, Python services, APIs, database systems, monitoring, tests, and data quality checks. Work across batch and near-real-time workloads while improving the performance, reliability, and cost efficiency of a large data warehouse. Own ambiguous engineering problems from concept through production, using Python, SQL, and databases such as PostgreSQL or MongoDB.

Own and build the company’s end-to-end data platform, including lakehouse architecture, ingestion, storage, orchestration, governance, and near-real-time processing. Develop trusted commercial and logistics datasets for finance, sales, procurement, and logistics. Establish data quality monitoring, testing, and alerting, while creating the foundation for pricing, forecasting, and AI agents. Partner with the Head of Data and help establish engineering standards for a future data team.

Develop, maintain, and support web-based applications, APIs, and core data infrastructure for FWW. Implement new data layers, visualization tools, automations, and provide cross-departmental technical support.

Designs and operates scalable data integration, transformation, quality, and management solutions for digital R&D. Responsibilities include building data pipelines and models, maintaining a data mesh catalog, implementing CI/CD and infrastructure automation, and supporting monitoring, observability, metadata, lineage, access control, and governance. The role partners with cloud architects and platform teams to digitalize chemical research and development.

Develops and governs clinical data standards and study specifications aligned with CDISC and regulatory guidance. Partners with statisticians and programmers to create compliance and productivity tools, mentors teams, monitors standards and regulatory updates, supports submissions, and represents the organization in industry standards initiatives. Requires extensive programming, clinical trial data, regulatory submission, mentoring, and CDISC expertise.

Owns supply data solutions by building reliable analytics datasets, reusable data marts, monitored Python and GCP pipelines, documentation, data governance, access controls, and data quality checks. Partners with scientists, analysts, and supply stakeholders to establish data contracts and improve platform reliability. The role also mentors junior analysts and promotes SQL best practices.

Develops and maintains secure data pipelines, ETL/ELT workflows, data transformations, and analytics-ready datasets. Processes structured and unstructured data using platforms such as Databricks, Spark, Palantir Foundry, and Maven Smart Systems. Collaborates with data scientists, engineers, and mission stakeholders while applying data validation, modeling, quality, testing, documentation, and Agile practices. The role requires onsite work in a SCIF and support for classified national security missions.

Build and improve Modash’s large-scale creator search system across retrieval, indexing, ranking, embeddings, relevance, and low-latency serving. Own initiatives end to end, from requirements and architecture through implementation, production deployment, measurement, and iteration. Develop multimodal search capabilities using image, video, text, audio, LLMs, and vector search. Collaborate with data, product, customers, and leadership while balancing quality, cost, latency, and reliability.

Build and operate resilient, large-scale backend and data collection systems managing 400M+ creator profiles and billions of data points. Design distributed services that handle API instability, rate limits, outages, and partial failures; optimize collection economics; and own architecture, implementation, deployment, observability, and production operations. Collaborate with data and product teams to improve freshness, reliability, coverage, and system performance.

Own end-to-end data engineering for growth initiatives, including source integration, pipelines, models, metrics, data products, and self-service capabilities. Partner with Marketing, Analytics, and Engineering to support acquisition, conversion, engagement, and retention decisions. Lead system design and improve testing, observability, performance, reliability, and cost efficiency. Mentor teammates, influence technical standards, and maintain production data systems.

Own Synera’s data warehouse and ingestion infrastructure, integrating GTM and business systems into reliable, documented, monitored pipelines. Build data models, reporting and forecasting layers, and agent-ready interfaces using APIs, MCP connectors, permissions, and governance. Develop retrieval infrastructure for documents and unstructured content, including chunking, embeddings, and indexing. Partner with teams across the company to improve data quality and make business information usable by AI agents.

Own and scale Andercore’s Databricks lakehouse, including ingestion, storage, orchestration, governance, domain modeling, data quality, monitoring, and near-real-time processing. Build trusted datasets and data foundations for pricing, forecasting, and AI agents while partnering with business stakeholders. Establish engineering standards as the first dedicated data platform hire, with potential to lead a growing data engineering team.

Build and maintain scalable GCP data pipelines, BigQuery data models, enterprise semantic layers, and CI/CD workflows for a modernized marketing data platform. Rebuild legacy pipelines, support DataHub adoption, enforce data quality, optimize performance and costs, and enable secure multi-brand marketing reporting. Collaborate with product, Martech, analytics, and data engineering teams.

Zuhlke is seeking an Expert Data Engineer focused on Databricks to support client transformation initiatives. The provided description contains limited role-specific responsibilities, qualifications, or project details. The position appears aligned with data engineering and the development of modern data solutions within Zuhlke’s multidisciplinary engineering and innovation teams.

Develop and maintain BI dashboards, reports, data models, and ETL processes supporting commodity trading, risk, P&L, inventory, and logistics analysis. Integrate data from trading platforms, market providers, ERP systems, and warehouses. Automate reporting, improve data quality, monitor market indicators, and translate stakeholder requirements into scalable analytical solutions and visualizations.

Build and maintain scalable data pipelines, ETL workflows, data models, dashboards, reports, and visualizations supporting game analytics, live operations, business intelligence, and reporting. Integrate multiple data sources, optimize SQL queries, ensure data quality, monitor the data platform, and collaborate with cross-functional teams to deliver reliable and actionable data solutions.

Build and maintain scalable data pipelines, ETL workflows, data models, and infrastructure supporting game analytics, live operations, reporting, and business intelligence. Develop dashboards and visualizations, integrate and validate data from multiple sources, optimize SQL queries, monitor data platforms and reporting systems, and collaborate with cross-functional teams to deliver reliable, actionable data solutions.

Own end-to-end industrial software deployments, from defining use cases and onboarding machine and production data through validation, rollout, training, and adoption. Work directly with operators, plant managers, IT, and OT teams to solve production problems. Build KPIs, anomaly signals, analytical workflows, and root-cause analyses using Python, SQL, statistics, and time-series data. Lead customer projects, identify expansion opportunities, and feed field insights back into the product.

Build and ship production-grade full-stack software integrated with agentic AI systems. Responsibilities include integrating LLM APIs, using AI-assisted development tools, evaluating AI-generated outputs, building application and API layers, tracking AI productivity KPIs, and supporting Agile delivery from design through production. Contribute to reusable components, AI tooling standards, and integrations involving agent architectures, RAG pipelines, and enterprise AI workflows.

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 and operate a governed data platform for contract-driven publishing and consumption. Build PostgreSQL-to-Debezium-to-Kafka CDC workflows, GitOps-style contract validation and reconciliation, developer tooling, Kubernetes runtimes, and DataHub catalog integrations. Define data product standards covering schemas, ownership, lifecycle, compatibility, lineage, access, and recovery. Collaborate with infrastructure, product, analytics, data engineering, and AI/ML teams to deliver reliable, reusable production data capabilities.