Top Data Engineer Jobs in the Netherlands

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.

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.

Deploys, maintains, and scales physical server, network, and cabling infrastructure across global production data centers. Responsibilities include rack-and-stack deployments, structured cabling, hardware and network troubleshooting, component replacement, ticket resolution, corrective maintenance, asset tracking, and coordination with engineering teams and remote-hands vendors. The role requires hands-on infrastructure expertise, Linux and out-of-band management experience, physical lifting, and up to 50% domestic and international travel.

Designs, develops, and maintains scalable Azure data pipelines and integrations. Uses Python, Azure Data Factory, Databricks, and Apache Spark for data ingestion, transformation, and processing. Monitors and optimizes pipeline performance, troubleshoots failures and data quality issues, and supports analytics and reporting requirements. Collaborates with technical and business stakeholders, follows development and testing standards, participates in Agile practices, and documents data solutions.

Build and maintain cloud-based data 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, supporting ML platforms, and applying application security practices. The role also involves client collaboration, continuous learning, and contributing to a growing data engineering team.

Build and maintain cloud-based data and machine learning infrastructure for client projects and internal AI solutions. Responsibilities include cloud architecture, storage and compute optimization, containerization with Docker and Kubernetes, Python development, CI/CD automation, ETL pipelines, Infrastructure as Code, application security, and ML platform enablement. The role also involves consulting with clients, supporting deployments, and contributing to the growth of the data engineering team.

Build and maintain cloud-based data engineering and machine learning infrastructure for client projects and internal AI solutions. Responsibilities include developing Python tools and ETL pipelines, managing Docker and Kubernetes environments, implementing CI/CD and Infrastructure as Code, optimizing cloud services, supporting ML deployments, and applying application security practices. The role requires collaboration with data science teams and clients in a DevOps culture.

Design and implement scalable data pipelines, APIs, and backend systems for TRACT’s insights platform. Translate business requirements into reliable technical solutions, optimize performance, ensure data integrity, and collaborate with cross-functional teams. The role requires Google Cloud, Terraform, Python, FastAPI, PostgreSQL, Airflow, and BigQuery experience, along with strong architectural knowledge, communication, mentoring, and agile development skills.

Lead a Synapse-to-Microsoft Fabric migration for a lakehouse data platform supporting asset management, HR, and finance. Build scalable PySpark and SQL pipelines, dimensional models, data products, quality frameworks, and CI/CD workflows. Own roadmap initiatives, optimize performance and costs, translate stakeholder needs into technical solutions, lead code reviews, and coach data engineers. The role is hybrid in Rotterdam for an initial six-month interim engagement, with possible extension up to two years.

Lead development and ownership of heat-pump control software and the connected-product data platform. Responsibilities include coding, testing, deployment, diagnostics, telemetry and time-series data reliability, observability, controlled rollouts, architecture evolution, and cross-functional collaboration with hardware, product, service, and operations teams. The role also establishes engineering practices, documentation, team structure, recruiting, mentoring, and shared operational ownership while remaining hands-on.

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 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, develops, and maintains scalable Azure data pipelines and ETL/ELT solutions using Azure Data Factory, Databricks, PySpark, Python, and SQL. Builds CI/CD workflows with Azure DevOps, optimizes large-scale data processing, monitors and troubleshoots pipelines, and ensures data security, governance, compliance, and reliability. Collaborates with technical and business stakeholders to translate requirements into enterprise data engineering solutions.

Build and maintain scalable production data pipelines, machine learning data pipelines, and data transformation frameworks for financial crime detection solutions. Collaborate with product, R&D, data, analytics, customers, and partners; optimize large-scale data architectures; support feature engineering and root cause analysis; and train customer teams. The role requires customer-facing collaboration, English and Spanish fluency, and domestic and international travel when needed.

Lead data engineering initiatives for Optiver’s research and trading teams. Design and maintain scalable ETL/ELT pipelines using Spark, Structured Streaming, Databricks, and Delta Lake; productionize research workflows; establish engineering best practices; develop reusable data patterns; collaborate globally; and mentor junior engineers while advancing Lakehouse and DataOps capabilities.

Build and maintain data pipelines and infrastructure for analytics, experimentation, and company metrics. Collaborate with machine learning engineers on training, quality assessment, and model release automation. Improve data delivery processes, automate manual workflows, and develop scalable data models. The role requires strong Apache Spark and Python expertise, SQL proficiency, Git experience, and familiarity with cloud data platforms and Kubernetes.

Build and maintain data pipelines, infrastructure, data models, and automation for analytics and machine learning workflows. Collaborate with ML engineers on model training, quality assessment, and releases. Improve internal data delivery processes and enable efficient access to large-scale organizational data.

Supports the design, development, integration, validation, deployment, and monitoring of AI and data science solutions for ESA space programs. Responsibilities include data collection and preprocessing, exploratory analysis, predictive modeling, machine learning, image processing, anomaly detection, data visualization, and use of HPC-based information factory environments. The role also contributes to AI standards, technology development plans, knowledge sharing, and digital innovation initiatives.

Designs and maintains scalable GCP-based ingestion, CDC, ETL, and data delivery pipelines. Builds dbt and Airflow workflows, optimizes Snowflake and ClickHouse integrations, and enforces data quality, governance, schema standardization, and incremental processing for large-scale metadata and AI/ML datasets.

Develop and deploy scalable, production-grade machine learning and AI solutions for financial research. Build LLM- and NLP-based applications, collaborate with senior engineers, and contribute to reliable AI-driven products. Responsibilities require Python, software engineering, deep learning, databases, Docker, and at least one major deep learning framework. Preferred experience includes RAG pipelines, vector databases, LLM fine-tuning, cloud platforms, and Kaggle competitions.

Senior Data Engineer Consultant responsible for managing client engagements, advising senior stakeholders, supporting business development, representing the firm at professional events, and mentoring junior consultants. The role requires data engineering expertise, strong analytical and communication skills, and familiarity with Databricks, Snowflake, and Azure. Candidates will work across data and AI-related projects, potentially serving energy and finance clients, with flexible office, home, and client-site arrangements.

Lead client engagements delivering data engineering solutions, including scalable pipelines, cloud and distributed systems, data modeling, integration, and DataOps. Manage stakeholders, advise senior clients, develop consulting offerings, support business development, represent the firm externally, and mentor junior consultants. The role requires strong technical communication, analytical problem-solving, relevant professional experience, a technical master’s degree, and fluency in English plus Dutch or French.

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 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.

Develops and governs clinical data standards and study specifications aligned with CDISC and regulatory guidance. Partners with statisticians and programmers to create compliance and productivity tools, mentors teams, monitors standards and regulatory updates, supports submissions, and represents the organization in industry standards initiatives. Requires extensive programming, clinical trial data, regulatory submission, mentoring, and CDISC expertise.