Top Data Engineer Jobs in Amsterdam

Build and operate production data pipelines supporting analytics, AI, and agentic workflows. Responsibilities include implementing canonical data models, maintaining Databricks or Snowflake platforms, monitoring reliability, responding to incidents, validating healthcare data mappings, improving performance and cost, and documenting architecture. Requires strong SQL and Python skills, cloud data platform experience, ETL/ELT orchestration expertise, and healthcare or pharmaceutical data experience.

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.

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 and maintain cloud-based data engineering and machine learning infrastructure for client projects and internal AI solutions. Responsibilities include cloud architecture, Docker and Kubernetes orchestration, Python development, CI/CD, ETL pipelines, infrastructure as code, security implementation, and ML platform support. The role also involves consulting with clients, maintaining application lifecycles, and collaborating with data science and engineering teams.

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

Designs, builds, and operates production-grade agentic AI systems and orchestration frameworks. Responsibilities include prompt architecture, tool and API integrations, monitoring, evaluation, cost controls, reliability improvements, and governance documentation. The engineer collaborates with data science, data engineering, and governance teams to ensure reliable, compliant workflows using structured healthcare and pharmaceutical data.

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.

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, develop, maintain, and optimize scalable Azure data engineering solutions. Build and manage Azure Data Factory pipelines, use Azure Databricks for large-scale data processing and transformation, and develop solutions with Python and SQL. Responsibilities include integrating multiple data sources, monitoring and troubleshooting pipeline failures, improving performance and reliability, translating technical requirements, and collaborating with technical and business stakeholders.

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.

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

Builds scalable data, analytics, and agentic AI solutions using Python, SQL, Databricks, and cloud platforms. Partners with business stakeholders to define analytical problems, integrate and govern enterprise data, analyze commercial and financial performance, and productionize AI agents. The role also coordinates cross-functional initiatives, develops semantic and metric layers, ensures testing and deployment quality, and contributes to data and AI strategy.

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.

Develop and improve a Salesforce- and Python-based master data platform supporting product data integration, modeling, validation, enrichment, monitoring, and downstream operations. Responsibilities include designing and testing platform features, improving integrations and workflows, collaborating with operational users, reviewing code, resolving incidents, and participating in occasional on-call support. The role offers growth toward deeper Python development and data platform architecture.

Designs and operates secure, scalable Microsoft Azure infrastructure for enterprise data workloads. Implements Infrastructure as Code with Terraform or Azure Bicep, manages Azure DevOps repositories and CI/CD pipelines, and establishes security, networking, backup, disaster recovery, monitoring, and cost-management practices. The role also supports identity, private connectivity, cloud VM lifecycle management, capacity planning, incident resolution, compliance guardrails, and integrations with internal and external data sources.

Own and evolve DataSnipper’s data platform, including event ingestion, streaming pipelines, dbt transformations, Snowflake models, reverse ETL, data quality, and infrastructure. Establish event contracts, improve platform performance and cost, integrate business data sources, and enable self-service analytics and AI-ready semantic layers. Partner with product, engineering, customer success, and go-to-market teams to define reliable data solutions and support customer-facing analytics.

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.

Lead the design, implementation, and operation of enterprise data platforms, scalable ELT/ETL pipelines, master data structures, lakehouse and semantic models, and Power BI reporting. Own a cost-management data solution, establish data quality and governance controls, and implement DevOps practices. Partner with finance, operations, and business stakeholders to deliver secure, trusted, high-performing data products and provide technical leadership across architecture, deployment, and support.

Lead the architecture and scaling of Overstory’s data platform for AI-powered vegetation analysis. Design and operate large-scale geospatial and temporal data pipelines, orchestration systems, event-driven workflows, service-oriented architecture, data quality frameworks, and observability practices. Drive architectural standards, system design, scalability, reliability, and traceability while mentoring engineers and collaborating with data, ML, and product teams.

Build and own the architecture for Dune’s curated data lifecycle, including dependency-aware orchestration, backfills, schema evolution, retries, recovery, data quality, and alerting. The role combines backend software engineering with large-scale data systems work across Go, Kotlin, Rust, Python, and SQL. Responsibilities include designing ingestion and curation contracts, making architecture decisions, solving ambiguous problems, and leading work that other engineers can execute.

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.

Own and evolve BlockTech’s self-hosted data infrastructure for trading systems, including low-latency ingestion, Kafka and NATS streaming, PostgreSQL, ClickHouse, and Iceberg storage. Ensure data reliability through gap detection, repair, schema evolution, capacity planning, and failure handling. Partner with trading and research teams, lead projects from concept through production, establish technical standards, mentor engineers, and influence platform architecture.