Top Data Engineer Jobs in Amsterdam

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.

Owns the lifecycle of critical financial data products, including security master data, ETF compositions, market data, and trading records. Builds and monitors automated pipelines, implements data quality checks and observability, resolves operational issues, and automates manual workflows. Collaborates with quantitative researchers, trading teams, and technical stakeholders to create accurate, reproducible, point-in-time datasets and improve data usage. Supports critical pipelines across global and continuously operating markets.

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 secure application deployment; and enabling data scientists to train and serve machine learning models.

Develop data engineering and cloud infrastructure solutions for client projects and internal AI products. Responsibilities include designing cloud architectures across GCP, AWS, and Azure; building Docker and Kubernetes environments; developing Python tools, REST APIs, ETL pipelines, and CI/CD workflows; deploying and maintaining machine-learning infrastructure; implementing Infrastructure as Code and application security; and supporting data science teams and client deployments.

Build and maintain reliable data pipelines supporting product analytics, compliance reporting, and operational dashboards. The role involves processing large and varied datasets, developing data models, managing cloud and containerized infrastructure, maintaining legacy systems, collaborating cross-functionally, and creating tests for edge cases. Required technologies include Python, SQL, dbt, cloud platforms, Docker, Kubernetes, and multiple programming languages.

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

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.

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 Engineer will build and scale data pipelines, ensure data quality, implement dbt best practices, and empower cross-functional teams with self-service data access.

Designs, develops, and maintains scalable Azure data pipelines and integrations. Uses Python, Azure Data Factory, Databricks, and Apache Spark for data ingestion, transformation, and processing. Monitors and optimizes pipeline performance, troubleshoots failures and data quality issues, and supports analytics and reporting requirements. Collaborates with technical and business stakeholders, follows development and testing standards, participates in Agile practices, and documents data solutions.

Develop and maintain scalable Microsoft Fabric data solutions, including ETL/ELT pipelines, data integration, transformation, orchestration, modeling, and optimization. Use SQL, Python, and PySpark with OneLake, Lakehouse, Data Warehouse, Delta Lake, and Medallion Architecture. Troubleshoot pipeline issues, implement security and governance practices, and collaborate with stakeholders and technical teams to deliver reliable, performant cloud data platforms.

Deploys, maintains, and scales physical server, network, and cabling infrastructure across global production data centers. Responsibilities include rack-and-stack deployments, structured cabling, hardware and network troubleshooting, component replacement, ticket resolution, corrective maintenance, asset tracking, and coordination with engineering teams and remote-hands vendors. The role requires hands-on infrastructure expertise, Linux and out-of-band management experience, physical lifting, and up to 50% domestic and international travel.

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.

Own and evolve Miele X’s e-commerce data platform by building ELT pipelines, data models, integrations, applications, and automation workflows. Shape the architecture of the agent platform, develop AI agents, and promote AI-native engineering practices. Establish technical standards, lead architectural decisions, collaborate with analysts and stakeholders, and mentor junior engineers while turning emerging AI and data technologies into production-ready solutions.

Own and evolve OrderYOYO’s Microsoft Fabric data platform, including lakehouse architecture, pipelines, semantic models, governance, monitoring, and legacy reporting migration. Build reliable integrations across operational, CRM, payment, marketing, and acquired-business data. Apply AI and automation to improve engineering, reporting, testing, and insights. Provide senior technical leadership, establish delivery standards, mentor engineers and analysts, and maintain production data reliability.

Builds scalable data, analytics, and agentic AI solutions using Python, SQL, Databricks, and cloud platforms. The role integrates enterprise data from Salesforce, SAP, and ServiceNow; develops curated datasets, semantic and metric layers; analyzes commercial and financial performance; and productionizes AI agents with evaluation, testing, governance, and CI/CD practices. It also coordinates cross-functional initiatives and communicates insights and recommendations to business and technical stakeholders.

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.

Lead backend engineering for scalable data platforms, APIs, and big-data systems. Responsibilities include architecture, technical project leadership, coding in Java, Scala, and Python, API and data-service development, engineering standards, Agile delivery, mentoring, and coordination across distributed teams. The role focuses on technologies including Spark, Flink, workflow orchestration, data fabrics, Databricks, Docker, Kubernetes, microservices, and relational databases.

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.

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.

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.

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 grow the Data Layer: build and operate ingestion, transformation, reconciliation, and serving pipelines (raw→refined→curated). Use agentic AI tools to accelerate delivery, ensure data correctness, and partner with backend, AI, and product teams to define reliable data contracts.

Staff Software Engineer responsible for designing, building, and operating distributed market data systems. The role focuses on real-time blockchain data ingestion, streaming, normalization, storage, APIs, reliability, data correctness, observability, incident recovery, and production support. The engineer will lead technical decisions, mentor teammates, translate ambiguous requirements into designs, and use AI-assisted development throughout the software lifecycle.