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# Analytics Engineer

- **Company:** NGAGE
- **Location:** Rotterdam, Netherlands
- **Experience:** Experienced
- **Salary:** €4,167.0
- **Contract:** Permanent contract
- **Skills:** Data Analysis, Data Warehousing, Looker Analytics
- **Published:** September 24, 2026
- **Apply:** https://www.ngage-it.nl/vacatures/analytics-engineer-influencermarket/

## About the Role

Looker and LookML are important parts of the analytics environment. Previous experience with them is a significant advantage. However, it is not a requirement: if you have experience with another BI tool and are eager to develop your Looker skills, we would still like to hear from you., * At least 2 years of experience as an Analytics Engineer, BI Engineer, Data Engineer or in a similar data-focused role

 * Experience with Looker and LookML is a significant advantage,
 * A structured, proactive way of working and the ambition to grow with the company over the long term

## Description

As an Analytics Engineer, you will take ownership of the layer between source data and the business. You will translate questions from teams such as finance and marketing into clear, documented metrics and data models. You will develop and maintain data models in dbt and BigQuery, including incremental models, testing and documentation. You will also optimise SQL with a focus on performance and query costs. In addition, you will use Python for automation and Git for version control., We are looking for an Analytics Engineer to turn raw data into reliable, actionable insights for an innovative creator platform in Rotterdam. The data warehouse foundation is already in place: Google BigQuery with dbt transformations and Looker for reporting. Your role is to help take the next step-from an environment that works to an analytics platform that finance, marketing and engineering teams trust every day.

You will work at the intersection of data and business. You will create clear metric definitions, build maintainable data models and ensure dashboards remain reliable and useful when underlying data sources or transformations change.


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