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.

Develop and maintain the reporting framework, manage data access, transform data formats, and build scalable APIs while collaborating cross-functionally.

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 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.

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

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.

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 (Docker/Kubernetes), develop Python services and REST APIs, implement CI/CD and IaC, support ML infrastructure, and ensure application security and deployment.

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, build, and operate scalable Databricks data pipelines and Lakehouse data models using SQL, Python, and PySpark. Implement ELT/ETL and streaming patterns, data quality checks, Unity Catalog governance, performance optimization, monitoring, documentation, and operational support.

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 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 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.

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

As a Junior Data Engineer, you will build data processing pipelines, support decision-making, visualize finance indicators, and monitor improvements.

The Data Engineer will design, build, and maintain data infrastructure, creating data models, developing ETL processes, and optimizing data pipelines for analysis and reporting.

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.

As a Senior Data Engineer, you'll build and manage data pipelines, ensure data quality, and collaborate across teams, contributing to the Data Platform's growth.

Lead design and implementation of client study standards aligned to CDISC (SDTM/ADaM) and regulatory guidance. Provide guidance, training, mentorship, and governance support; partner with statisticians and programmers to build tools and metadata repository solutions; ensure submission readiness and represent IQVIA in industry standards initiatives.

Build and operate live pricing data systems and historical replay frameworks, integrate external datasets, optimize performance across C++/Python boundary, and collaborate with traders and researchers to deliver production-quality pricing data pipelines with monitoring and data-quality validation.

Design, build, and operate a scalable data platform for quantitative research and backtesting. Develop production-grade Python applications to ingest, process, validate, and distribute market and reference data. Build automated data pipelines (Airflow, AWS Batch), ensure data quality and observability, and collaborate with researchers, software, and infrastructure teams to accelerate dataset onboarding and research workflows.

Work with Product and Insurance teams to define KPIs, analyze A/B tests, build and maintain real-time dashboards, contribute to dbt-based data pipelines, and provide data-driven recommendations to influence product roadmap and outcomes.

The Software Engineer will design, build, and operate data integration platforms, ensuring reliable data pipelines, API services, and compliance with data governance regulations.