Top Data Engineer Jobs in the Netherlands

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

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, build and maintain Databricks-based data pipelines and models to deliver validated, timely data for Continuity & Security dashboards. Implement complex business rules, ensure regulatory-grade data validation, automate DevOps ingestion pipelines, and collaborate with stakeholders to provide resilience and risk insights.

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 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.

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.

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.

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, 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.

Design and build scalable ELT pipelines and curated data models on a Databricks Lakehouse. Partner with business teams to create reliable data products, implement governance and quality checks, and enable BI and self-service analytics while contributing to platform best practices.

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

As a Data Analyst, you'll analyze user behavior and product performance, create reports and dashboards, and collaborate with teams to drive data-informed decisions.

Own end-to-end AI-automated data platform migration projects (1-4 concurrently): scope, plan, execute, and hand off. Act as primary customer contact, configure Datafold's Migration Agent, partner with engineering on execution, and help refine delivery playbooks.

Lead a team of software engineers to design and build a strategic cloud data product on Google Cloud. Engineer solutions, propose improvements, and accelerate customer data solutions. Act as a Google Cloud data engineering expert, mentor teammates, and collaborate with clients in a consulting environment.

Design, build, and maintain ETL/ELT data pipelines; assess and remediate data quality; apply data modelling, metadata management, and platform integration for federated/defence contexts.

Design, build, and operate scalable Databricks data pipelines and Lakehouse data models using SQL, Python, and PySpark. Implement ELT/ETL and streaming patterns, data quality checks, Unity Catalog governance, performance optimization, monitoring, documentation, and operational support.

Designs and maintains data pipelines, assesses and transforms data from multiple sources, applies data modelling and metadata standards, performs data quality checks and remediation, and integrates data platforms to enable semantic interoperability within NATO IT environments.

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

Design, implement, and maintain data lakehouse solutions on AWS (S3, Glue, Lake Formation, Athena, Redshift) using Databricks/Delta Lake or similar. Build secure, compliant data pipelines focusing on PII protection and data residency. Advise and manage client stakeholders, collaborate with cross-functional teams, and support ML feature stores and model deployment.

Design and implement cloud-native data infrastructure and ETL pipelines across GCP/AWS/Azure; containerize and orchestrate workloads with Docker/Kubernetes; build Python server-side tools and APIs; manage CI/CD, IaC (Terraform) and ML-serving infrastructure; enforce application security (HTTPS, OAuth2, certificates); support client projects and internal AI solutions.

Work on client projects and internal AI solutions building cloud architectures (GCP/AWS/Azure), data pipelines/ETL, containerized deployments (Docker, Kubernetes), CI/CD, and ML infrastructure. Develop Python server-side tools, implement IaC and security controls, and support deployment and operations of data science workloads.

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 apps (Docker/Kubernetes), develop Python services and REST APIs, implement CI/CD and IaC, support ML infrastructure, and ensure application security and deployment.

Build and operate data infrastructure and ML-supporting platforms for clients and internal products. Work on cloud architecture (GCP/AWS/Azure), containerization (Docker, Kubernetes), ETL/data pipelines, CI/CD, IaC, and application security to deploy and maintain data-driven solutions.

Design, build, and maintain scalable data pipelines and storage for fraud detection. Support fraud analytics, feature engineering, model integration and serving, pipeline monitoring, and documentation. Collaborate with data, product, and engineering teams to enable detection, reporting, and operationalisation of fraud models.