How data engineering is powering Pinterest’s global platform

With more than 460 million ‘Pinners’ using its platform each month, Pinterest is powered by its data architecture, ML and experimentation platforms

Pinterest is the visual inspiration platform people around the world use to shop for products personalised to their taste, find ideas to do offline and discover the most inspiring creators. 

Beginning as a tool to help people collect the things they were passionate about online, today more than 460 million people come to the platform every month to explore and experience billions of ideas. 

Central to powering this platform is data engineering on a vast scale, as Dr. Dave Burgess, VP of Data Engineering at Pinterest, explains: “In Data Engineering we create and run reliable and efficient planet-scale data platforms and services to accelerate innovation at Pinterest and sustain our business. We do everything from online data systems, to logging data, big data and stream processing platforms, analytics and experimentation platforms, machine learning (ML) platforms, and the Pinterest Developer Platform for external developers to build applications using Pinterest APIs.”

Since joining the business four years ago, Burgess has overseen the replacement of many of Pinterest’s data engineering systems with the latest in open-source software. “We’ve also built machine learning and experimentation platforms on top of our data platform, increased ML Engineering velocity by 10x and run hundreds of new experiments every week,” he adds. 

“We’ve also democratised our data so that everyone in the company can use it to make decisions, build applications, and experiment. All of this has significantly improved our agility, developer productivity, and products for our customers.”

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