Top Data Engineer Jobs in the Netherlands

Build and operate scalable data pipelines, metadata solutions, integrations, data products, and governance capabilities across IKEA’s enterprise platforms. The role focuses on data discovery, lineage, quality, observability, and trusted consumption. Responsibilities include developing CI/CD and Infrastructure as Code automation, integrating Databricks, Microsoft Fabric, Collibra, SAP, and M3, and collaborating with engineering, architecture, privacy, cybersecurity, and data management teams.

Owns the lifecycle of critical financial data products, including security master data, ETF compositions, market data, and trading records. Builds and monitors automated pipelines, implements data quality checks and observability, resolves operational issues, and automates manual workflows. Collaborates with quantitative researchers, trading teams, and technical stakeholders to create accurate, reproducible, point-in-time datasets and improve data usage. Supports critical pipelines across global and continuously operating markets.

Build and maintain scalable ETL/ELT pipelines and data-processing workloads using Databricks, PySpark, Python, and SQL. Develop lakehouse solutions with Delta Lake and Medallion Architecture, integrate structured and semi-structured data, and implement validation, monitoring, orchestration, recovery, and CI/CD. Optimize Spark and SQL performance while supporting production-grade data quality and enterprise data platforms.

Build and maintain production-grade data pipelines and enterprise integrations connecting Pigment with Workday, Salesforce, Looker, big data platforms, and third-party logistics warehouse systems. Lead API integrations, data transformations, master data management, quality controls, monitoring, retries, alerting, and testing. Support technical integration for global warehouse expansions and communicate complex architectures and failures to operational and business stakeholders.

Build and maintain cloud-based data engineering and machine learning infrastructure for client projects and internal AI solutions. Responsibilities include designing cloud architectures, managing storage and compute services, developing Python tools and ETL pipelines, containerizing applications with Docker and Kubernetes, automating CI/CD, implementing Infrastructure as Code, supporting ML deployments, and applying application security practices. The role also involves client collaboration, continuous learning, and ownership of the full software lifecycle.

Build and maintain cloud-based data engineering and machine learning infrastructure for client projects and internal AI solutions. Responsibilities include designing cloud architectures across GCP, AWS, and Azure; developing Python tools and ETL pipelines; managing Docker, Kubernetes, CI/CD, and Infrastructure as Code; supporting secure application deployment; and enabling data scientists to train and serve machine learning models.

Develop data engineering and cloud infrastructure solutions for client projects and internal AI products. Responsibilities include designing cloud architectures across GCP, AWS, and Azure; building Docker and Kubernetes environments; developing Python tools, REST APIs, ETL pipelines, and CI/CD workflows; deploying and maintaining machine-learning infrastructure; implementing Infrastructure as Code and application security; and supporting data science teams and client deployments.

The Data Engineer will build and scale data pipelines, ensure data quality, implement dbt best practices, and empower cross-functional teams with self-service data access.

Build and maintain reliable data pipelines supporting product analytics, compliance reporting, and operational dashboards. The role involves processing large and varied datasets, developing data models, managing cloud and containerized infrastructure, maintaining legacy systems, collaborating cross-functionally, and creating tests for edge cases. Required technologies include Python, SQL, dbt, cloud platforms, Docker, Kubernetes, and multiple programming languages.

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

Builds and maintains data systems, data lake and warehouse solutions, transforming raw data into reliable structured datasets. Develops algorithms and prototypes for predictive and prescriptive modeling, improves data quality, supports reporting and analysis, and automates business processes. Collaborates with data scientists, analysts, architects, marketing, and sales teams across central and local organizations. The role also requires secure data management, continuous technology development, and end-to-end ownership of data structures.

Own and modernize the enterprise data platform by designing scalable integrations, pipelines, data models, migrations, semantic models, and analytics solutions. Lead architecture standards, optimize performance and reliability, support Microsoft Fabric and Databricks adoption, and translate business needs into maintainable technical solutions. Collaborate with stakeholders, document designs, demonstrate solutions, and explore generative AI applications across development and platform 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.

Designs and maintains data infrastructure supporting naval production, engineering, and operational processes. Responsibilities include integrating and preparing data, developing ETL pipelines, optimizing data models and queries, troubleshooting technical issues, documenting systems, improving report performance, automating data processing, and collaborating with multidisciplinary teams. The role also involves technical support, knowledge sharing, and mentoring colleagues.

Designs and develops software products, APIs, integrations, platform services, and user experiences that simplify data discovery and consumption. Connects enterprise systems including Databricks, Microsoft Fabric, Collibra, SAP, and M3 while supporting metadata, lineage, governance, trusted access, automation, scalability, and self-service. Collaborates with engineering, product, architecture, and data teams while contributing to DevOps, CI/CD, testing, observability, and engineering standards.

Build and operate software products, APIs, integrations, and platform services that enable data discovery, metadata visibility, lineage, self-service access, and trusted data consumption. The role connects Databricks, Microsoft Fabric, Collibra, SAP, M3, and other enterprise systems while applying CI/CD, Infrastructure as Code, automated testing, observability, and scalable architecture practices.

Build and operate scalable batch and streaming data pipelines on Databricks. The role contributes to platform architecture, CI/CD standards, legacy migration, governance, security, cost optimization, reliability, and data quality. It also evaluates new Databricks capabilities, applies AI to engineering workflows, mentors junior engineers, and collaborates with analysts, BI developers, data scientists, and business stakeholders.

Build and operate scalable cloud data pipelines for quantum chemistry datasets used to train AI models. Responsibilities include transforming quantum chemistry software into cloud workflows, managing and validating scientific data, deploying containerized infrastructure through infrastructure as code and CI/CD, and ensuring reliability, security, observability, incident response, and capacity planning. The role collaborates with scientific and technical experts across disciplines.

Develop and maintain scalable Microsoft Fabric data solutions, including ETL/ELT pipelines, data integration, transformation, orchestration, modeling, and optimization. Use SQL, Python, and PySpark with OneLake, Lakehouse, Data Warehouse, Delta Lake, and Medallion Architecture. Troubleshoot pipeline issues, implement security and governance practices, and collaborate with stakeholders and technical teams to deliver reliable, performant cloud data platforms.

Build and operate scalable data platforms supporting proprietary research and systematic trading. Responsibilities include distributed storage and processing, data modeling, analytical engines, governance, lineage, access control, data quality, monitoring, and platform reliability. The role partners with quants and engineers, uses Python or Java, works across cloud environments, enables AI data consumption, resolves pipeline issues, and mentors engineers.

Build and maintain scalable, high-performance systems for trade and position lifecycle management, reference data, reporting, and front-office analytics. Automate financial lifecycle events, ensure data quality and system robustness, develop tested code, support CI/CD and production systems, and collaborate with trading, research, risk, operations, brokers, and fund administrators.

Own and evolve Miele X’s e-commerce data platform by building ELT pipelines, data models, integrations, applications, and automation workflows. Shape the architecture of the agent platform, develop AI agents, and promote AI-native engineering practices. Establish technical standards, lead architectural decisions, collaborate with analysts and stakeholders, and mentor junior engineers while turning emerging AI and data technologies into production-ready solutions.

Lead client data engineering engagements from strategy through execution. Design scalable data architectures, pipelines, cloud and distributed systems, databases, data models, integration patterns, and DataOps workflows. Manage stakeholders, advise senior clients, contribute to proposals and thought leadership, develop internal offerings, and mentor junior consultants. The role requires strong communication, analytical problem-solving, technical expertise, and fluency in English plus Dutch or French.

Represent the consulting firm at professional events, support thought leadership and business development, manage client engagements, advise senior stakeholders, and mentor junior consultants. The role requires data engineering expertise, strong communication, analytical problem-solving, stakeholder management, and fluency in English plus Dutch or French. Client technology stacks commonly include Databricks, Snowflake, and Azure.

Provides expert technical support for spacecraft on-board computers and data handling across ESA projects and R&D activities. Responsibilities include system architecture, verification and validation, project reviews, hardware procurement, laboratory facility management, integration, testing, benchmarking, debugging, documentation, and knowledge transfer. The role supports facilities involving OBCs, payload processing, CubeSat systems, AI/ML hardware and software, mission data links, and avionics architectures.