Netflix is a famously data-driven company. Data is used to make informed decisions on everything from content acquisition to content delivery, and everything in-between. As with any data-driven company, it’s critical that data used by the business is accurate. Or, at worst, that the business has visibility into potential quality issues as soon as they arise. But even in the most mature data warehouses, data quality can be hard. How can we ensure high quality in a cloud-based, internet-scale, modern big data warehouse employing a variety of data engineering technologies? In this talk, Michelle Ufford will share how the Data Engineering & Analytics team at Netflix is doing exactly that. We’ll kick things off with a quick overview of Netflix’s analytics environment, then dig into the architecture of our current data quality solution. We’ll cover what worked, what didn’t work so well, and what we're working on next. We’ll conclude with some tips & lessons learned for ensuring high quality on big data. This talk was presented at DataWorks/Hadoop Summit 2017 on June 13, 2017.