Forecasting demand for thousands of fashion items across 20 brands is hard. Doing it with siloed, inconsistent data is nearly impossible. At Bestseller, we solved this by building a self-service data analytics platform grounded in strong opinions held loosely: clear engineering standards balanced with flexibility.

With a team of just three engineers, we built, rebuilt, and now operate a platform serving more than 1000 users across data engineering, data science and commercial roles.

This talk shares how we applied DevOps principles and software engineering practices to Terraform, Snowflake, dbt and Airflow to create reusable data products at scale. You’ll learn the architectural decisions that worked, the ones that didn’t, and how maintaining firm-but-flexible opinions helped us survive fast growth and changing requirements.