Summary of Unlocking analytics engineering at scale

00:00:00 - 00:20:00

This video discusses how DBT Labs was able to scale analytics engineering by implementing standards and principles, automating as much as possible, and sharing the load of data validation, performance testing, and user alignment.

• 00:00:00 This presentation discusses how DBT Labs has been able to unlock analytics engineering at scale, specifically within the context of their own company. The company has over a billion dollars in annual net revenue, and has over a hundred analytics engineers.
• 00:05:00 The video discusses how the company Aritzia grew its analytics engineering team by engaging business in a data transformation. The team used various approaches to solve the problem, including engaging business, creating a data pipeline, and training other users. The team also overcame challenges such as the small team and the company's size. The video ends with a sneak peak of the lessons learned.
• 00:10:00 The video discusses how, in order to scale analytics engineering, the team made changes to their delivery approach, specialized their engineers, and used software engineering practices. The changes helped to improve the speed of delivery and the trust of the team.
• 00:15:00 The speaker discusses how they were able to scale analytics engineering by implementing standards and principles, automating as much as possible, and sharing the load of data validation, performance testing, and user alignment. They also mention that their infrastructure is more scalable.
• 00:20:00 The presenter discusses the challenges of scaling analytics engineering, highlighting the need for a collective vision and more educational programs specifically for this field. They also discuss the importance of early data modeling in the analytics pipeline. Next, Lindsay Murphy and John Kennedy will present how to launch analytics at a startup with DBT.