Top Data Engineer Jobs in Amsterdam

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.

The Data Center Engineer will manage data center assets, maintain DCIM documentation, collaborate on deployments, and oversee global operations.

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.

Work on client projects and internal AI solutions building cloud architectures (GCP/AWS/Azure), data pipelines/ETL, containerized deployments (Docker, Kubernetes), CI/CD, and ML infrastructure. Develop Python server-side tools, implement IaC and security controls, and support deployment and operations of data science workloads.

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.

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.

Design, build, and maintain scalable Databricks data pipelines: extract from databases/APIs/streaming, transform with Spark/PySpark, load to data lakes/warehouses, ensure data quality and governance, integrate with cloud storage, develop data models, and collaborate with data teams and stakeholders.

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.

Work as a data engineer consultant on client projects and internal AI solutions: design cloud architectures (GCP/AWS/Azure), build ETL/data pipelines, containerize and orchestrate apps with Docker/Kubernetes, develop Python server-side tools and CI/CD, support ML infrastructure, and implement security and IaC practices.

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, build, and maintain scalable raw data pipelines for Tencent's overseas games. Optimize ingestion, processing, and storage with upstream engineering; support analysts, data scientists, and ML engineers downstream. Improve pipeline performance, quality, and maintainability using batch and streaming technologies, data modeling, programming, and SQL.

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.

Lead a team of software engineers to design and build a strategic cloud data product on Google Cloud. Engineer solutions, propose improvements, and accelerate customer data solutions. Act as a Google Cloud data engineering expert, mentor teammates, and collaborate with clients in a consulting environment.

As a Junior Software Engineer, you will build data pipelines, implement API endpoints, ensure data quality, and participate in code reviews within the Data Integration team.

Own and evolve the company data platform: build ingestion pipelines into BigQuery, create SQLMesh transformations and dimensional models, improve data quality and observability, manage infrastructure with Terraform and GCP, enable BI and reverse ETL, and set technical direction while mentoring engineers and leading incident response.

Senior Data Software Engineer designs and builds data engineering and software solutions across diverse projects. Responsibilities include developing new systems, enhancing existing platforms, collaborating with clients and stakeholders, and delivering solutions within agile teams using Python, SQL, and cloud platforms (Azure/AWS).

Design and build governed data pipelines, semantic models, and data products using Databricks, dbt, SQL, and PySpark. Implement Unity Catalog and DataHub governance (lineage, access controls, classification). Collaborate with stakeholders, drive CI/CD, testing, observability, and deliver production-grade analytics and AI-ready data assets.

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.

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.

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.

Design and build highly scalable, low-latency market data systems and APIs. Implement backend services, influence architecture, maintain test coverage, participate in code reviews, troubleshoot incidents, and occasionally be on-call. Work spans streaming and historical market data endpoints and developer-focused API improvements.

Design, build, and scale low-latency market data systems and public APIs. Influence architecture, implement backend services, maintain quality and tests, participate in reviews, troubleshoot incidents, and optionally serve on-call.