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# Analytics Engineer

- **Company:** Goodhabitz Bekijk Alle Vacatures
- **Location:** Eindhoven, Netherlands
- **Contract:** Permanent contract
- **Skills:** Data Analysis, SQL Databases, Data Layers, Databricks
- **Published:** September 18, 2026
- **Apply:** https://www.careerjet.nl/jobad/nl9a6d4f827c6a8828c48997709a6a0496

## About the Role

* Production-level experience with SQL and dbt (or equivalent), with Git as a working habit

 * Experience with dimensional modelling and semantic layers

 * Experience building a tracking plan or event taxonomy in partnership with engineering

 * A track record of building enforcement processes such as data contracts, CI validation or DoD gates, not just writing standards

 * Comfort translating ambiguous product and business questions into clear, durable measurement definitions

 * Experience with Databricks/Unity Catalog, B2B SaaS multi-surface products, or data observability tooling is a plus

## Description

GoodHabitz is building an activation-first product strategy, and the Product Data function is being built from the ground up. As Analytics Engineer, you'll be the discipline's technical backbone, establishing governed, trustworthy models across the data estate and setting the standards the function will run on. You'll work hands-on in Databricks with dbt, partnering closely with the data engineer on modelling and with product and business stakeholders on what to measure and why. This role reports to the Product Data Lead.
Role responsibilities:

 * Build trust in the data estate. Assess the Databricks estate, define trust levels, and propose owners for each source and table.

 * Model with dbt. Build and version silver-to-gold transformations with dimensional modelling and a semantic layer, designing point-in-time dimensions that preserve history.

 * Own the event taxonomy. Design and roll out one event taxonomy and naming convention, partnering with engineering to govern it as a shared contract.

 * Define shared metrics. Own the metric catalog and sign-off process, and build activation, engagement and retention models leadership can steer by.

 * Enable self-serve. Build semantic models and documentation that let product and business teams answer their own questions.

 * Enforce quality. Set data quality standards, build sign-off into a Definition of Done gate, and monitor instrumentation to catch issues early.


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