Top Data Engineer Jobs in the Netherlands

As a Data Engineer, you will build and maintain high-quality data pipelines and architectures, ensuring data flows and integrity while collaborating with diverse stakeholders and adhering to best practices.

Design, build, deploy and operate scalable ELT pipelines and data architectures. Collaborate with stakeholders to translate requirements, ensure data quality, implement testing/monitoring, optimize performance, and champion data engineering best practices across the organization.

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.

Develop and maintain the reporting framework, manage data access, transform data formats, and build scalable APIs while collaborating cross-functionally.

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, build, test, and maintain reliable data pipelines and Snowflake-based data warehouse. Partner with stakeholders to gather requirements, model data, optimize performance, enable BI, and support CI/CD, data quality, and dashboarding (Power BI).

Design, build, and maintain scalable Databricks data pipelines: extract from databases/APIs/streaming, transform with Spark/PySpark, load to data lakes/warehouses, ensure data quality and governance, integrate with cloud storage, develop data models, and collaborate with data teams and stakeholders.

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

As a Junior Data Engineer, you will build data processing pipelines, support decision-making, visualize finance indicators, and monitor improvements.

The Senior AI and Data Engineer will lead AI adoption by developing and deploying AI solutions, collaborating with teams, conducting experiments, and building scalable systems.

As a Lead Cloud Data Engineer, you'll collaborate with software engineers to develop cloud solutions, suggesting improvements and implementing project requirements on Google Cloud.

Work as a Data Engineer consultant on client projects and internal AI solutions: design cloud architectures (GCP/AWS/Azure), build ETL/data pipelines, containerize and orchestrate applications (Docker/Kubernetes), implement CI/CD, support ML infrastructures, develop Python server-side tools, and apply security best practices.

Work as a Data Engineer consultant on client projects and internal Sia AI solutions. Design cloud architectures (GCP/AWS/Azure), build ETL and Big Data pipelines, containerize and orchestrate applications with Docker/Kubernetes, develop Python server-side tools and REST APIs, implement CI/CD and IaC, and provide ML-focused infrastructure and security solutions to support Data Scientists.

Work on client projects and internal AI solutions building cloud architectures (GCP/AWS/Azure), containerized deployments (Docker, Kubernetes), Python server-side tools, CI/CD pipelines, ETL data pipelines in big-data environments, and ML infrastructure. Implement IaC, security (OAuth2, HTTPS, certificate management), and support deployment and maintenance in a DevOps culture.

Design, model, and develop AWS-based data ecosystems and pipelines. Gather and document business and technical requirements, optimize data storage (lakes/warehouses), tune performance, troubleshoot processing and infrastructure issues, integrate diverse data sources, mentor junior engineers, and engage directly with clients.

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.

The Data Engineer will design, build, and maintain data infrastructure, creating data models, developing ETL processes, and optimizing data pipelines for analysis and reporting.

Develop and improve classified sandbox data pipelines to extract, deduplicate, convert and archive documents (office, image, video), extract metadata, build semantic search indexes, automate registrations and transfers to NATO archives, and report processing status and traceability.

The Data Center Engineer will manage data center assets, maintain DCIM documentation, collaborate on deployments, and oversee global operations.

Designs, builds, tests and maintains data infrastructure and pipelines. Collaborates with analysts and data scientists to source, transform, secure, and deliver structured and unstructured data for analytics and AI, ensuring reliability, performance, and compliance.

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.

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.

The role involves designing and maintaining cloud-native data pipelines, implementing medallion architecture, and ensuring data quality in a collaborative environment.

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.

Design, build, and maintain scalable raw data pipelines for Tencent's overseas games. Optimize ingestion, processing, and storage with upstream engineering; support analysts, data scientists, and ML engineers downstream. Improve pipeline performance, quality, and maintainability using batch and streaming technologies, data modeling, programming, and SQL.