Top Data Engineer Jobs in the Netherlands

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.

The role involves designing and maintaining cloud-native data pipelines, implementing medallion architecture, and ensuring data quality in a collaborative environment.

Supports the design, installation, qualification, administration, security, compliance, and lifecycle management of AVEVA PI Data Historian and other automation data systems at a pharmaceutical manufacturing site. Partners with automation, IT, facilities, and information security teams; develops procedures; performs audits; maintains data integrity; and supports manufacturing teams as systems expand. The role is fully onsite and begins as a 12-month contract with potential conversion to permanent employment.

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.

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.

Own and evolve a cloud-agnostic data and ML platform supporting ingestion, storage, processing, model training, deployment, and lifecycle management. Build reliable, scalable, automated infrastructure using DevOps and MLOps practices, including CI/CD, Infrastructure-as-Code, containerization, monitoring, governance, security, and cost optimization. Collaborate with data science, ML engineering, and IT teams while maintaining vendor-delivered components, documentation, and operational processes.

Develop and maintain data quality platforms, Databricks solutions, CI/CD pipelines, and automated monitoring capabilities for Business Lending. Analyze data sources, support reliable deployments, and ensure security, compliance, and architectural standards. Collaborate with product, engineering, risk, and data management teams to improve data quality, enable data-driven decisions, and support digital client journeys.

Design, build, and maintain scalable Azure data platforms and pipelines using Databricks, PySpark, and structured streaming. Implement data lake/warehouse and serverless architectures, data ingestion, transformation, and cleansing workflows. Monitor DevOps pipelines and production workloads, troubleshoot issues, and drive performance, governance, and operational excellence while collaborating with stakeholders and architects.

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.

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.

Build and ship full-stack features for a data intelligence SaaS platform, spanning backend services and frontend interfaces. Use AI coding assistants to accelerate development while applying sound engineering judgment. Participate in architecture discussions, code reviews, testing, incident response, troubleshooting, documentation, and knowledge sharing. Partner with product managers to translate requirements into technical solutions. The role emphasizes SaaS architecture, AWS cloud fundamentals, Docker, Kubernetes, CI/CD, agile delivery, autonomy, and production ownership.

Design and optimize Snowflake data models, customer 360 views, identity resolution, segmentation, and CDP-ready schemas. Build pipelines syncing customer data to Braze, Hightouch, and related platforms. Improve performance, cost efficiency, data quality, governance, consent management, and audience activation while collaborating with CRM, marketing, analytics, and technical stakeholders.

Design, implement, and maintain data lakehouse solutions on AWS (S3, Glue, Lake Formation, Athena, Redshift) using Databricks/Delta Lake or similar. Build secure, compliant data pipelines focusing on PII protection and data residency. Advise and manage client stakeholders, collaborate with cross-functional teams, and support ML feature stores and model deployment.

Design, build, and operate end-to-end AI automations and data pipelines: extract and prepare data, orchestrate LLM-based agentic workflows on Azure, integrate with systems of record, and implement evaluation, monitoring, and guardrails. Maintain Power BI reporting and semantic models, join discovery workshops to assess feasibility, and build reusable automation patterns that deliver measured business impact.

Build and maintain Camunda 8 backend data-layer services across relational databases and Elasticsearch/OpenSearch. Design scalable storage, query, retention, streaming, and secondary-storage solutions; investigate production data issues; improve performance and operability; and deliver engineering initiatives collaboratively with Product, QA, Support, and SRE. Participate in L3 support and on-call rotations.

Lead mechanical due diligence and feasibility for data center sites and deals. Validate cooling topology and equipment strategy (air, liquid, hybrid), assess capacity, vendor lead times, and integration with budgets and schedules. Define mechanical readiness gates, deliver validated scope to Delivery, coordinate with stakeholders and vendors, and help build repeatable evaluation standards for AI/hyperscale infrastructure acquisitions.

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.

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

Design and maintain data warehouse systems, develop ETL pipelines, manage datasets for analytics, and implement real-time data solutions.

Own and operate all PostgreSQL databases across BSS/OSS/NOC and network elements. Build backup/DR, performance tuning, bulk-data pipelines, monitoring (Prometheus/Grafana), HA patterns, schema reviews, data governance (retention, masking), and runbooks. Implement cross-platform integration with vendor network databases and automate bulk loads, migrations, and purges.

The role involves creating and automating infrastructure features for data platforms using Python, with a focus on distributed systems and collaboration within a global team.