• Dimensional data modelling for analytics / data warehouses / big data infrastructures
• High proficiency in SQL (any dialect)
• Programming in Python, Java or other language
• Deep understanding of data warehouse technologies
• Hands-on with BI tools such as Looker or Tableau
• Track record using transformation tools such as dbt
• Practical experience with orchestration systems such as Airflow
• Strong communication skills with proven ability to explain technical concepts to non-technical audiences

What You’ll Do –

You’ll own analytics datasets and pipelines for specific product domains with real-world impact. Our analytics engineers have full autonomy to drive the analytics direction for their areas – from defining success metrics to building self-serve capabilities. Join us in combining cutting-edge data engineering with purpose-driven analytics.

Technical Environment 

• Modern Analytics Stack: SQL and Python in dbt on Airflow infrastructure
• Data Warehouse Architecture: Dimensional data modelling for big data infrastructures – Modern distributed data warehouse solutions (Databricks experience a plus)
• BI & Semantic Layer: Looker for semantic models and self-serve analytics
• Orchestration: Airflow for pipeline automation and scheduling