Top Data Engineer Jobs in the Netherlands

Build and operate production data pipelines supporting analytics, AI, and agentic workflows. Responsibilities include implementing canonical data models, maintaining Databricks or Snowflake platforms, monitoring reliability, responding to incidents, validating healthcare data mappings, improving performance and cost, and documenting architecture. Requires strong SQL and Python skills, cloud data platform experience, ETL/ELT orchestration expertise, and healthcare or pharmaceutical data experience.

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 improve data products supporting a data mesh, including ingestion and transformation pipelines, data quality checks, data modeling, access management, documentation, and security reviews. Collaborate with data engineers, data stewards, product owners, and software engineers across Sweden and Amsterdam. Improve processes, advocate for data mesh practices, provide technical guidance, and optimize the existing technology stack.

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.

Designs, develops, tests, optimizes, and maintains scalable batch and near-real-time data pipelines and architectures. Ensures data quality, builds API integrations, improves internal data processes, and supports machine learning, data science, BI, and product initiatives. The role requires experience with data warehouses, relational and NoSQL databases, data modeling, event-driven architectures, SQL, Python, Airflow, Spark, and AWS data services.

Own and evolve a production data platform on Google Cloud, designing BigQuery and dbt data models, ingestion and transformation pipelines, orchestration, CI/CD, and infrastructure practices. Integrate digital analytics, marketing, campaign, and customer data for reporting and activation. Support machine learning model deployment and operations, ensure reliability and cost efficiency, define engineering standards, and mentor colleagues while partnering directly with business stakeholders.

Owns the design, development, operation, and governance of data pipelines and integration patterns supporting Medical Affairs AI products. Builds APIs and ETL/ELT solutions connecting enterprise systems to analytics platforms, RAG pipelines, vector databases, and GraphRAG applications. Ensures data quality, privacy, lineage, compliance, and protection of sensitive information. Partners with architecture, engineering, product, and business stakeholders to deliver reusable, production-grade data integrations.

Build and maintain production ELT pipelines, dbt data models, datasets, and metrics. Monitor pipeline reliability and data quality, resolve incidents, optimize query performance and warehouse costs, and improve schema and partitioning strategies. Develop internal tooling and documentation while partnering with Analytics, Product, Science, Engineering, and platform teams to deliver reliable, trusted data infrastructure.

Designs, builds, and operates production-grade agentic AI systems and orchestration frameworks. Responsibilities include prompt architecture, tool and API integrations, monitoring, evaluation, cost controls, reliability improvements, and governance documentation. The engineer collaborates with data science, data engineering, and governance teams to ensure reliable, compliant workflows using structured healthcare and pharmaceutical data.

Own the data engineering side of DoiT’s integrations framework by building and maintaining third-party billing and usage API integrations. Ensure data correctness, completeness, normalization, reconciliation, and support for negotiated rates. Manage orchestration, scheduling, backfills, and pipeline operations while using AI throughout development. Collaborate with product, support, and engineering teams to expand vendor coverage and improve cloud and SaaS spend visibility.

Own data engineering for a cloud and SaaS billing integrations platform. Build and maintain third-party billing API integrations, operate ingestion pipelines, normalize vendor data, manage orchestration and backfills, and ensure financial-grade correctness through reconciliation and quality checks. Use AI throughout the engineering workflow and collaborate with product, support, and platform teams to expand vendor coverage and improve customer cost visibility.

Own data engineering for cloud and SaaS billing integrations. Build and maintain third-party API integrations, normalize vendor billing data, operate ingestion pipelines, manage orchestration and backfills, and ensure financial-grade correctness through reconciliation and quality checks. Use AI throughout the engineering workflow and collaborate with product, support, and backend teams to expand vendor coverage and improve customer visibility into technology spend.

Own data platform architecture and engineering from source-system ingestion through reliable, actionable data products. Design data contracts, schemas, batch and streaming pipelines, orchestration, incremental loads, backfills, and quality monitoring across SQL, NoSQL, cloud, and on-premises environments. Establish data quality baselines, detect staleness and drift, and communicate limitations clearly to customers and decision-makers. Work agentically with AI while maintaining strong technical standards.

Build and maintain cloud-based data engineering and machine learning infrastructure for client projects and internal AI solutions. Responsibilities include cloud architecture, Docker and Kubernetes orchestration, Python development, CI/CD, ETL pipelines, infrastructure as code, security implementation, and ML platform support. The role also involves consulting with clients, maintaining application lifecycles, and collaborating with data science and engineering teams.

Build and maintain data engineering, cloud, and machine learning infrastructure for client projects and internal AI solutions. Responsibilities include cloud architecture across GCP, AWS, and Azure; Docker and Kubernetes orchestration; Python development; CI/CD automation; ETL pipelines in Big Data environments; Infrastructure as Code; application security; and deployment and maintenance of data science platforms.

Build and maintain cloud-based data engineering and machine learning infrastructure for client projects and internal AI solutions. Responsibilities include cloud architecture, containerization with Docker and Kubernetes, Python development, CI/CD automation, ETL pipeline development, Infrastructure as Code, security implementation, and deployment and maintenance of data science applications across GCP, AWS, and Azure.

Build and maintain Azure-based data pipelines, warehouses, Data Vault and dimensional data marts using ADF, Azure SQL, and T-SQL. Develop Power BI datasets, semantic models, and reports; optimize stored procedures; investigate source systems; and implement data quality monitoring. The role also involves profiling datasets, validating business results, debugging data platforms, and collaborating with clients and a remote European team.

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.

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.

Lead development and operation of a self-service data and ML platform, including batch and streaming pipelines, lake management, orchestration, APIs, governance, monitoring, and data quality. Build scalable Spark and Python applications on Databricks and AWS, support data product teams, optimize reliability and costs, and collaborate with technical and non-technical 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.

Lead the design, implementation, and operation of enterprise data platforms, scalable ELT/ETL pipelines, master data structures, lakehouse and semantic models, and Power BI reporting. Own a cost-management data solution, establish data quality and governance controls, and implement DevOps practices. Partner with finance, operations, and business stakeholders to deliver secure, trusted, high-performing data products and provide technical leadership across architecture, deployment, and support.

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

Lead the architecture and scaling of Overstory’s data platform for AI-powered vegetation analysis. Design and operate large-scale geospatial and temporal data pipelines, orchestration systems, event-driven workflows, service-oriented architecture, data quality frameworks, and observability practices. Drive architectural standards, system design, scalability, reliability, and traceability while mentoring engineers and collaborating with data, ML, and product teams.

Build and own the architecture for Dune’s curated data lifecycle, including dependency-aware orchestration, backfills, schema evolution, retries, recovery, data quality, and alerting. The role combines backend software engineering with large-scale data systems work across Go, Kotlin, Rust, Python, and SQL. Responsibilities include designing ingestion and curation contracts, making architecture decisions, solving ambiguous problems, and leading work that other engineers can execute.