Top Data Engineer Jobs in the Netherlands

Design and maintain scalable data pipelines, connectors, and reusable data products to make enterprise data AI-ready. Enable RAG, semantic search, agentic workflows, and production deployment of AI solutions while enforcing data quality, governance, metadata, security, and cross-team collaboration.

Build the companys first data stack and data products (internal analytics and customer-facing). Work across data engineering, devops, and software tasks; ship early features, improve developer experience, document proposals, and iterate based on feedback.

Build and maintain cloud data infrastructure, containerized applications, CI/CD pipelines, ETL processes, and machine-learning services across client projects and internal AI solutions. Responsibilities include architecture across GCP, AWS, and Azure; infrastructure automation; Kubernetes cluster management; Python development; application security; and deployment and maintenance throughout the code lifecycle. The role also involves collaborating with data engineering and data science teams and advising clients on data and AI opportunities.

Assist design, build, and maintain batch and streaming data pipelines, cloud-based data systems, and data infrastructure for manufacturing plants. Support data modeling, formats, automated deployments, and stakeholder requirements to make manufacturing data accessible for analysis and reporting within a digital product team.

Build and operate large-scale on-premise big-data platforms, focusing on OLAP systems or data engineering tooling. Develop streaming and batch pipelines, optimize distributed compute and storage (Druid, Spark, Trino, ClickHouse), improve data quality/lineage, and maintain CI/CD and private-cloud deployment for high-throughput analytics.

Design, build, and operate large-scale on-premise big-data platforms (OLAP and data engineering ecosystems). Scale distributed systems, optimize streaming and batch pipelines, improve data quality/lineage, and enhance CI/CD and private-cloud deployment tooling to support analysts, data scientists, and hundreds of developers.

Build and maintain cloud data engineering infrastructure for client projects and Sia AI solutions. Responsibilities include cloud architecture, storage and compute optimization, Docker and Kubernetes orchestration, Python development, CI/CD automation, ETL pipelines in big data environments, machine learning infrastructure, application security, and Infrastructure as Code. The role also requires collaboration with data scientists and clients in a DevOps culture.

Build, maintain, and optimize ETL pipelines, data warehouses, integrations, SQL queries, and stored procedures. Support data modelling, reporting, analytics, automated data-quality controls, troubleshooting, documentation, and reliable data delivery. The role also contributes to AI-enabled data solutions and collaborates with engineering, finance, software, and analytics teams while developing ownership under experienced guidance.

Design, develop, maintain, and optimize scalable Azure data engineering solutions. Build and manage Azure Data Factory pipelines, use Azure Databricks for large-scale data processing and transformation, and develop solutions with Python and SQL. Responsibilities include integrating multiple data sources, monitoring and troubleshooting pipeline failures, improving performance and reliability, translating technical requirements, and collaborating with technical and business stakeholders.

Design, build, deploy, and maintain scalable Databricks data pipelines using Spark, cloud services, and data lake or warehouse technologies. Extract, transform, cleanse, and load data from databases, APIs, and streaming platforms. Implement data models, quality checks, governance, workflow orchestration, and performance optimization. Collaborate with data scientists, analysts, business intelligence teams, and stakeholders to deliver reliable data solutions and support data-driven decision-making.

Design, build, and operate enterprise reporting and analytics platforms. Create scalable ELT/ETL pipelines, master data foundations, and lakehouse/warehouse semantic models. Deliver Power BI datasets and dashboards, implement data quality, governance, DevOps practices, and cost-management data solutions. Partner with finance and stakeholders and provide technical leadership across architecture, deployment, and run-state support.

The Data Engineer will build data pipelines for materials science, ensuring data quality and collaborating with ML researchers while managing large datasets.

Build and maintain data-driven solutions for a banking client by onboarding data sources, developing reports, enhancing a SQL data warehouse, and maintaining pipelines. Integrate data from staging through data marts, ensure data quality, optimize T-SQL procedures and queries, and support legacy MS Access, text-file, and Excel environments. Collaborate with business stakeholders and cross-functional teams to deliver reliable, scalable data solutions.

Design, develop, and support Denodo virtual views and data services; integrate Denodo with databases, APIs and cloud platforms; build reusable semantic/logical layers; optimize queries and performance; collaborate with architects and business teams; document designs, test, review code, deploy, and provide production support.

The Data Engineer will design and maintain data pipelines for medical imaging, support data lifecycle processes, and improve legacy datasets while ensuring compliance with regulatory standards.

Build and maintain data pipelines for production, test, calibration, quality, and shipment data. Extract and transform test logs, DAQ data, and ECU outputs into reliable datasets. Create dashboards for yield, failures, and product performance; support root cause and supplier analyses; automate data preparation and reporting; validate data quality and traceability; and document pipelines and data definitions. Collaborate closely with production, test operations, quality, engineering, and industrialization teams.

Build and maintain data engineering and machine learning infrastructure for client projects and internal AI solutions. Develop Python tools, ETL pipelines, REST APIs, and containerized applications using Docker and Kubernetes. Implement GCP cloud architectures, CI/CD pipelines, Infrastructure as Code, application security, and ML model deployment services. Collaborate with data scientists, consultants, and clients while supporting applications throughout their development, deployment, and maintenance lifecycles.

Build and maintain cloud-based data engineering and machine learning infrastructure for client projects and internal AI solutions. Responsibilities include designing cloud architectures, managing Docker and Kubernetes environments, developing Python tools and ETL pipelines, automating CI/CD, implementing Infrastructure as Code, optimizing cloud costs, and applying application security practices. The role also supports deployment and maintenance across the full software lifecycle while collaborating with data science teams and clients.

As a Data Engineer, you'll build and enhance scalable big data platforms, working with distributed systems and data processing tools while collaborating with diverse teams.

Design and build a greenfield data mesh platform and stream-processing backend on AWS. Implement scalable ETL/streaming pipelines (Spark, Kafka), ingest varied data sources, ensure data quality, handle schema evolution and failures, and collaborate with stakeholders as a consultant.

Build and own cloud data infrastructure and ETL pipelines on major cloud platforms. Collect, transform, and serve large-scale data; create reusable integrations and orchestration for multi-use cases while working in Agile teams.

Design, build, and maintain large-scale data environments and reusable ETL pipelines. Collect and transform data from multiple sources, develop integration services in cloud or hybrid infrastructures, and apply orchestration, API development, and ML/AI knowledge to ensure high-performance, production-ready datasets.

Design, build, and maintain cloud-native data infrastructure and scalable ETL/ELT pipelines to power AI/ML models. Integrate diverse data sources, optimize for reliability and cost, support CI/CD for data/ML workloads, and collaborate with Data Scientists, MLOps, and Software Engineers to deploy model-driven product features.

Build, operate, and improve Azure-based data pipelines integrating SAP S/4HANA and other systems. Design and optimize Azure SQL databases, develop ADF pipelines, handle daily operations and incident tickets, support ad hoc data requests, implement CI/CD with Azure DevOps and Git, consume REST APIs, and document solutions while modernizing legacy processes.

Design, develop, and maintain scalable data pipelines and processing workflows using Databricks, PySpark, Azure Data Factory, and Python. Implement data governance, quality, and lineage practices. Optimize and troubleshoot pipelines to ensure reliable, enterprise-grade data delivery.