Top Data Engineer Jobs in the Netherlands

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.

Own production Python data pipelines, ETL/ELT workflows, and the ontology powering an AI platform. Integrate warehouses and business applications with graph databases and vector stores, while managing deployment tooling, cloud infrastructure, monitoring, and security. The role begins as hands-on individual contribution and develops toward technical leadership through mentoring and architecture ownership.

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.

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.

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.

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.

Designs and develops enterprise data platforms using Microsoft Fabric and Azure. Responsibilities include architecture across OneLake, Lakehouse, pipelines, warehouses, semantic models, and Power BI; implementation of Medallion Architecture; development with PySpark, Python, SQL, and Azure Data Factory; governance, security, data quality, lineage, performance optimization, and CI/CD. The role also requires stakeholder collaboration, architecture documentation, and technical decision-making.

Build and improve Modash’s large-scale creator search system across retrieval, indexing, ranking, embeddings, relevance, and low-latency serving. Own initiatives end to end, from requirements and architecture through implementation, production deployment, measurement, and iteration. Develop multimodal search capabilities using image, video, text, audio, LLMs, and vector search. Collaborate with data, product, customers, and leadership while balancing quality, cost, latency, and reliability.

Lead the design and delivery of data pipelines and warehouse layers. Mentor team members, ensure data quality, and improve engineering practices. Collaborate with cross-functional teams to enhance healthcare data utilization.

Build and own Go-based backend services, APIs, data migrations, ETL pipelines, billing logic, and integrations across ERP, CRM, payment, and inventory systems. Design data models, enforce validation and reconciliation, automate migration tooling, and improve observability and incident response. Collaborate with Finance, Product, and Business Analysts while contributing to architecture, code reviews, and mentoring. The hybrid role requires onsite work in Rotterdam and a valid EU work permit.

Build and maintain scalable GCP data pipelines, BigQuery data models, enterprise semantic layers, and CI/CD workflows for a modernized marketing data platform. Rebuild legacy pipelines, support DataHub adoption, enforce data quality, optimize performance and costs, and enable secure multi-brand marketing reporting. Collaborate with product, Martech, analytics, and data engineering teams.

Lead Netflix’s data health analytics strategy by defining data excellence metrics, building trusted data models and dashboards, analyzing lineage and asset utilization trends, and influencing engineering investments. Partner with platform teams to capture metadata and telemetry, standardize data quality evaluations, and measure improvements in developer productivity, data friction, and tooling adoption.

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.

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.

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.

Develop and improve a Salesforce- and Python-based master data platform supporting product data integration, modeling, validation, enrichment, monitoring, and downstream operations. Responsibilities include designing and testing platform features, improving integrations and workflows, collaborating with operational users, reviewing code, resolving incidents, and participating in occasional on-call support. The role offers growth toward deeper Python development and data platform architecture.

Design and build durable, distributed data-streaming infrastructure that decouples process data from Camunda’s live execution engine. Develop customer-facing integrations such as Kafka connectivity, support analytics data consumption at scale, and make architecture decisions around consistency, durability, and storage. Participate in production support and on-call rotations while collaborating with Product, Data Layer, Distributed Systems, QA, SRE, and Support teams. Lead ambiguous, cross-functional engineering initiatives from design through delivery.

Build and maintain Camunda 8 backend data-layer services spanning relational databases and Elasticsearch/OpenSearch. Design scalable storage, query, retention, and streaming systems; improve performance, indexing, and production tooling. Lead engineering initiatives from solution design through delivery, collaborate across teams, provide L3 support, and participate in on-call rotations. Investigate production data issues and contribute to schema evolution, workflow automation, and CI improvements.

Designs and operates secure, scalable Microsoft Azure infrastructure for enterprise data workloads. Implements Infrastructure as Code with Terraform or Azure Bicep, manages Azure DevOps repositories and CI/CD pipelines, and establishes security, networking, backup, disaster recovery, monitoring, and cost-management practices. The role also supports identity, private connectivity, cloud VM lifecycle management, capacity planning, incident resolution, compliance guardrails, and integrations with internal and external data sources.

Design and operate production data pipelines and standardized data models for a multi-tenant Azure SaaS platform serving container terminals. Build near-real-time and batch ELT pipelines from operational systems, develop Go-based connectors, implement data quality monitoring, and support source-to-curated transformations across PostgreSQL layers. Collaborate directly with terminal operations staff while owning architectural decisions and platform scalability.

Own the design, delivery, and operation of complex data pipelines, datasets, and platform components. Translate business needs into scalable data models and reliable data products, improve data quality and observability, resolve production issues, and establish reusable engineering tools and standards. Collaborate with product and business stakeholders, contribute to architecture decisions, mentor engineers, and participate in on-call support.

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.

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.