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# Data Pipeline Engineer

- **Company:** Wlg
- **Location:** AMSTERDAM, Netherlands
- **Contract:** Permanent contract
- **Skills:** Data Analysis, Microsoft Azure, Configuration Management, Extract Transform Load (ETL), Data Sharing, Data Warehousing, Failover, Graph Database, Linux Commands, NoSQL, Operational Databases, SQL Server Integration Services, Talend, Management of Software Versions, Backup and Restore, Data Logging, C4ISR, Informatica Powercenter, Apache Nifi, Data Pipelines
- **Published:** October 16, 2026
- **Apply:** https://www.careerjet.nl/job/nlf57784bf06ec5fd02c7dea7c40c71d3d/eaa

## About the Role

* Hands-on experience with modern ETL tools such as Apache NiFi, Informatica, Talend or SSIS, ideally on AWS or Azure
 * Data modelling and transformation from heterogeneous sources
 * Experience with data sharing platforms, object stores and relational, NoSQL, graph and vector databases
 * Comfort on the Linux command line and a knack for fixing data anomalies
 * Experience delivering against fixed, deliverable-based contracts in multinational teams
 * Familiarity with NATO intelligence and surveillance architecture and applications, and fluent business English

## Description

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ETL/DWH · The Hague
Data Pipeline Engineer (NiFi, Talend, NoSQL and Graph Databases) for NATO with security clearance
Build the ETL pipelines that bring surveillance and reconnaissance data together - a one-person, deliverable-based engagement in The Hague.
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The data exists. Your job is to get it where it needs to be, reliably.
A multinational defence organisation in The Hague needs a data engineer to design and build the ETL processes that bring imagery and reports from sources on different networks into its systems, and then make those flows controllable, observable and resilient. The engagement is one person, delivered as a set of milestones over the year.
What you would be doing

 * Documenting, configuring and testing the ETL requirements: source-to-target mapping, traceability and data reconciliation
 * Building data models, pipelines and schemas for data coming from many different sources
 * Putting configuration control in place: baselines, versioning, change approval and rollback
 * Setting up monitoring with metrics, alerts, dashboards and logging
 * Designing and testing resilience: backup and restore, failover, recovery time and point objectives
 * Troubleshooting production data flows, and handing over through a training package and on-the-job training for the administrators