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# Data Engineer - Commercial BI

- **Company:** Westwing GmbH
- **Location:** München, Germany
- **Experience:** Experienced
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Airflow, Data Analysis, Cloud Computing, Code Review, Continuous Integration, Python (Programming Language), Machine Learning, Data Mesh, Standard Sql, Management of Software Versions, Large Language Models, Snowflake, Data Strategy, Git, Data Layers
- **Published:** October 31, 2026
- **Apply:** https://www.adzuna.de/details/5947028412

## About the Role

* 3+ years of experience as a Data Engineer, Analytics Engineer, or in a similar role
 * Strong hands-on experience with dbt (dbt Cloud or dbt Mesh is a plus) and excellent SQL skills, ideally with Snowflake
 * Solid knowledge of data modelling (dimensional, Kimball, layered architectures), data mesh principles and semantic layers
 * Experience with orchestration (e.g. Airflow, dbt Cloud jobs), Git and CI/CD, plus good Python skills
 * A product mindset; experience with commercial data (PIM, pricing, assortment, supplier data) or with preparing data for AI/LLM use cases is a strong plus
 * A can-do attitude, comfort with a fast pace, and fluency in English, If you are looking for a place where ambition meets creativity, where ideas turn into impact fast, and where people are trusted to grow - you have found it.

## Description

As a Data Engineer within the Commercial BI team, you will play a pivotal role in building and maintaining the data foundations for the team with the ultimate goal of enabling data-driven decisions in the department. You will help shape the data strategy within the team and will collaborate across departments to ensure a scalable and consistent data foundation. This position is permanent and based in Munich.
WHAT YOU'LL DO

 * Design, build, and maintain the Commercial domain's data products in dbt, integrating sources such as PIM, ERP, and pricing systems into consumer-ready marts
 * Model core commercial entities (products, prices, suppliers, stock, margins) so that product and price data is consistent across the company
 * Make Commercial data AI-ready by building semantic views and a semantic layer (e.g. dbt Semantic Layer, Snowflake semantic views) with clear metric definitions, so that AI models and agents can query the data accurately
 * Define clear data contracts, model ownership and versioning, so other domains can rely on Commercial data
 * Make sure data is high quality and on time, using dbt tests, freshness checks, and monitoring with clear SLAs
 * Set and enforce analytics engineering best practices (code review, CI/CD, modelling standards) in line with company-wide data mesh governance