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# Senior Data Engineer

- **Company:** WhyBrilliant GmbH
- **Location:** Berlin, Germany
- **Experience:** Expert
- **Salary:** €70,000.0 - €90,000.0
- **Contract:** Permanent contract
- **Skills:** Airflow, Amazon Web Services, BigQuery, Cloud Database, Continuous Integration, Information Engineering, Data Infrastructure, Data Systems, Data Warehousing, Python (Programming Language), Workflow Management Systems, Snowflake, Google Bigquery, Data Management, Looker Analytics, Data Pipelines
- **Published:** September 27, 2026
- **Apply:** https://www.careerjet.de/jobad/de6252dfbcaff68f7d23b3bd0c72cfa552

## About the Role

* 5+ years of hands-on data engineering and platform development experience.
 * Advanced proficiency in Python (or Go) and workflow orchestration using Apache Airflow.
 * Deep expertise with DBT and cloud data warehouses like BigQuery and Snowflake.
 * Proven track record building semantic models and dashboards in Looker/LookML.
 * Experienced operating across AWS/GCP with strong CI/CD practices and fluent English.

## Description

Our data ecosystem powers critical decisions across three distinct product squads, requiring top-tier reliability, scale, and modern modeling standards. In this role, you will be the core technical partner translating cross-functional business requirements into resilient, highly observable data pipelines across modern data warehouse environments.
What you'll do
You will own the end-to-end reliability, scalability, and semantic modeling of our entire analytics platform and data infrastructure.

 * Design, build, and maintain scalable data ingestion pipelines and orchestration workflows using Apache Airflow and automated CI/CD.
 * Model, optimize, and manage cloud data warehouses across Google BigQuery and Snowflake.
 * Develop and maintain robust DBT transformations and semantic analytics layers in Looker/LookML.
 * Partner directly with Product Managers, Software Engineers, and Business Analysts across three product squads to deliver resilient data solutions.
 * Implement best practices for data observability, pipeline monitoring, and schema design across AWS and GCP environments.


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