Session Outline
This session makes the case that well designed data tools not only benefit data stakeholders and the business but the very data engineers that build them! At Strava, a small data platform team handles and manages the end-to-end data pipeline and infrastructure. We support a team of analysts, machine learning engineers, marketing specialists, and other stakeholders. We have found that well-designed, self-service data tools have not only vastly improved the stakeholder experience but have also greatly benefited our day-to-day. In this data culture talk, I will dive into the specifics of what these tools look like and more importantly, how they have served Strava’s data platform team in the day to day. Some examples of these tools include a self-service data transformation tool, a data catalog, a set of extensible Airflow operators to ingest data from any API endpoint.
Key Takeaways
- Self-service is the name of the game – Leave it to the experts! Build and maintain exceptional tooling that makes it as easy as possible for domain experts to use data in their day to day.
- Routinely use the stuff you build – This not only helps you understand pain points, but you may find yourself benefiting from your own work!
- Make the routine as seamless as possible – Well designed data tooling is worth the investment. The less friction for the routine stuff, the more your team can focus on larger objectives.
