Top Data Engineer Jobs in Amsterdam

Build and operate large-scale on-premise big-data platforms, focusing on OLAP systems or data engineering tooling. Develop streaming and batch pipelines, optimize distributed compute and storage (Druid, Spark, Trino, ClickHouse), improve data quality/lineage, and maintain CI/CD and private-cloud deployment for high-throughput analytics.

Design, build, and operate large-scale on-premise big-data platforms (OLAP and data engineering ecosystems). Scale distributed systems, optimize streaming and batch pipelines, improve data quality/lineage, and enhance CI/CD and private-cloud deployment tooling to support analysts, data scientists, and hundreds of developers.

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.

Design, build, and maintain scalable data pipelines and storage for fraud detection. Support fraud analytics, feature engineering, model integration and serving, pipeline monitoring, and documentation. Collaborate with data, product, and engineering teams to enable detection, reporting, and operationalisation of fraud models.

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

Build and maintain data-driven solutions for a banking client by onboarding data sources, developing reports, enhancing a SQL data warehouse, and maintaining pipelines. Integrate data from staging through data marts, ensure data quality, optimize T-SQL procedures and queries, and support legacy MS Access, text-file, and Excel environments. Collaborate with business stakeholders and cross-functional teams to deliver reliable, scalable data solutions.

Build and maintain data engineering and machine learning infrastructure for client projects and internal AI solutions. Develop Python tools, ETL pipelines, REST APIs, and containerized applications using Docker and Kubernetes. Implement GCP cloud architectures, CI/CD pipelines, Infrastructure as Code, application security, and ML model deployment services. Collaborate with data scientists, consultants, and clients while supporting applications throughout their development, deployment, and maintenance lifecycles.

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 ML workloads; monitoring costs; and implementing application security. The role also requires collaboration with data science teams, client deployment support, and strong English and Dutch communication skills.

Consults on data engineering projects and develops Sia AI solutions. Responsibilities include designing cloud architectures across GCP, AWS, and Azure; managing Docker and Kubernetes environments; building Python tools, REST APIs, ETL pipelines, and CI/CD workflows; implementing Infrastructure as Code; supporting machine learning infrastructure; and maintaining application security. The role requires collaboration with data science teams and clients, strong quantitative education, technical expertise, and fluent English and Dutch.

As a Data Engineer, you'll build and enhance scalable big data platforms, working with distributed systems and data processing tools while collaborating with diverse teams.

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.

Design, build, and operate enterprise reporting and analytics platforms. Create scalable ELT/ETL pipelines, master data foundations, and lakehouse/warehouse semantic models. Deliver Power BI datasets and dashboards, implement data quality, governance, DevOps practices, and cost-management data solutions. Partner with finance and stakeholders and provide technical leadership across architecture, deployment, and run-state support.

Design, develop, and maintain scalable data pipelines and processing workflows using Databricks, PySpark, Azure Data Factory, and Python. Implement data governance, quality, and lineage practices. Optimize and troubleshoot pipelines to ensure reliable, enterprise-grade data delivery.

Design and build scalable ELT pipelines and curated data models on a Databricks Lakehouse. Partner with business teams to create reliable data products, implement governance and quality checks, and enable BI and self-service analytics while contributing to platform best practices.

As a Data Analyst, you'll analyze user behavior and product performance, create reports and dashboards, and collaborate with teams to drive data-informed decisions.

Design, build, and maintain scalable Azure-based data pipelines and ETL/ELT workflows using Azure Data Factory, Databricks, PySpark, and Python. Implement CI/CD in Azure DevOps, optimize performance, ensure data quality, security, and governance, and collaborate with stakeholders to translate requirements into production-ready data solutions.

Design, develop, and maintain scalable PySpark/Databricks data pipelines. Build and optimize Python data processing workflows, implement orchestration (Airflow preferred), ensure data quality and performance, integrate multiple sources into analytics-ready structures, debug and enhance pipelines, and collaborate in Agile cross-functional teams.

Design, build, and maintain scalable Azure data pipelines using ADF and Databricks. Develop Python-based ETL/ELT processes, ensure data quality, governance, performance optimization, and collaborate with architects and analysts in Agile teams to deploy and monitor enterprise data solutions.

Own and build the full financial data pipeline: design Snowflake schema and dbt model architecture, deliver ingestion and transformation with tests and docs, ensure pipeline reliability through financial close (on-call), establish data quality and governance, partner with analytics and finance stakeholders, extend models to OPEX/COGS/ASC 340-40, and support AI consumers with row-level security and freshness SLAs.

As a Software Engineer for Speechify, you'll focus on data collection for AI model training, manage cloud infrastructure, develop data ingestion pipelines, and collaborate on dataset roadmaps.

Own end-to-end AI-automated data platform migration projects (1-4 concurrently): scope, plan, execute, and hand off. Act as primary customer contact, configure Datafold's Migration Agent, partner with engineering on execution, and help refine delivery playbooks.

The Staff Engineer will define architecture, lead data pipeline design, ensure platform reliability, and mentor engineers, driving data initiatives and technical standards.

The Principal Data Engineer will improve data pipeline architecture, optimize data flows, and support data projects for various teams in the company.

Design, build, and maintain scalable ELT data pipelines with Airflow; develop dbt data models and tests; optimize Athena tables/queries and data workflows; provision infrastructure with Terraform; ensure data quality and reliability; collaborate with Analytics, BI, and Engineering; troubleshoot pipeline and data quality issues.

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.