Top Data Engineer Jobs in Amsterdam

Build, deploy, and operate high-quality ELT pipelines and data architectures on Adyen’s Big Data Platform. Integrate diverse data sources, ensure data quality and reliability, optimize Spark and query performance, and implement testing, monitoring, validation, governance, and discoverability practices. Collaborate with data scientists, analysts, engineers, product managers, and customers to develop solutions, while mentoring teammates and promoting data engineering standards.

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, build, deploy and operate scalable ELT pipelines and data architectures. Collaborate with stakeholders to translate requirements, ensure data quality, implement testing/monitoring, optimize performance, and champion data engineering best practices across the organization.

Build and maintain cloud-based data engineering 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; ETL pipelines in Big Data environments; Infrastructure as Code; application security; and deployment and maintenance of data science platforms.

Develop cloud-based data engineering and machine learning infrastructure for client projects and internal AI solutions. Responsibilities include designing cloud architectures, managing Docker and Kubernetes environments, building Python tools and ETL pipelines, automating CI/CD, implementing infrastructure as code, and supporting ML workloads. The role also requires application security experience, collaboration with data scientists and clients, and strong English and Dutch communication skills.

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 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.

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 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.

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.

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.

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.

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.

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.

Build next-generation distributed data storage and processing systems for Databricks Runtime. The role focuses on scalable, high-performance systems supporting workloads from ETL to data science, using strong algorithms, data structures, distributed systems, databases, and big data technologies. The engineer will work toward a multi-year technical vision while delivering incremental customer value.

Build and operate large-scale distributed data systems and SaaS products. Responsibilities may include developing front-end, back-end, and reliability features; architecting scalable services; improving user experiences; deploying and operating cloud infrastructure; and contributing to products such as SQL Analytics, Jobs and Workflows, and the developer ecosystem.

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.

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.

Build and modernize batch and streaming data pipelines on Databricks, migrate legacy systems, and optimize platform performance, cost, governance, and reliability. Collaborate with analysts and BI developers to enable self-service reporting and Power BI data models. Contribute to scalable data architecture, data quality, AI automation, and platform strategy while supporting teams across the business.

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.

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.