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# Analytics Engineer

- **Company:** Vivid Resourcing
- **Location:** Rotterdam, Netherlands
- **Experience:** Experienced
- **Contract:** Permanent contract
- **Skills:** Code Review, Continuous Integration, Software Debugging, Python (Programming Language), Software Construction, Transaction Data, Snowflake, Git Flow, Maintaining Code, Streamlit Framework, Databricks
- **Published:** September 3, 2026
- **Apply:** https://www.careerjet.nl/job/nle7a2987adc23d19214571ba3e70f63ef/eaa

## About the Role

* 3-5 years of experience in analytics engineering role with strong data modelling skills
 * Strong SQL and Python skills, with experience applying software engineering best practices: modular code, testing, code review, and Git-based workflows.
 * Experience supporting system migrations, platform consolidations, or modernization programs with analytics.
 * Self-starter, and able to work independently and prioritize based on business impact.
 * Proficient with AI-assisted development tools and able to use them effectively for coding, debugging, and documentation while maintaining code quality.

Nice to have

 * Experience with metric or semantic layers.
 * Background in utilities, energy, billing, or another domain with complex transactional data.

## Description

We are looking for an Analytics Engineer to build and own the analytics foundation for a large-scale system modernization program. You will be responsible for the data models, metric definitions, and dashboards that engineering and analytics teams use to validate releases and track progress. You will work in short cycles with both teams, and your output will feed directly into release decisions.
What you will do

 * Design and own the data models and centralized metric definitions that serve as the single source of truth for analytics.
 * Build and maintain transformation pipelines in dbt on Snowflake and Databricks, including tests, documentation, and CI/CD.
 * Develop dashboards as code in Streamlit, versioned and deployed alongside the rest of the stack.
 * Partner with engineering and analytics stakeholders to turn loosely defined questions into scoped, maintained analytics assets.


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