How to set up a Scalable Data Quality Solution

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As companies are continuing to explore new data-driven initiatives, integrating more business applications into their data lake or lakehouse, and implementing new data-focused tooling to power business decisions, the imperative to create reliable data infrastructure becomes paramount. In this demo, we’ll explore Monte Carlo’s approach to identifying where data incidents occur, whether across your entire data stack or down to the field level, so that you can mitigate data downtime to increase trust in your team’s work and provide quality data to your business.

Ethan Post, Sales Engineer at Monte Carlo

Ethan Post is a Sales Engineer at Monte Carlo, a data reliability company, where he helps data teams achieve more trustworthy data with end-to-end data observability. Ethan began his career as BI implementation consultant, building end-to-end data solutions for some of the most recognizable brands in the world. Most recently, he led the PreSales Center of Enablement for an analytics startup where he witnessed firsthand the true cost of poor data quality and observability. He received his MS in Information Systems from Indiana University. In his spare time, he loves cooking, woodworking, and spending time with his wife and two young children.

Brandon Chen, Product Marketing at Monte Carlo

Brandon is currently part of the Product Marketing team at Monte Carlo. Prior to Monte Carlo, Brandon had experience working in both the Sales Engineering and Product Marketing teams at Fivetran. He currently resides in Oakland, CA, and is an avid fisherman in his off-time.

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