Data teams choose Monte Carlo.

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PagerDuty is the world’s leading digital operations platform for full-stack incident response and on-call management. When migrating to Snowflake, PagerDuty wanted to understand the health of their data through fully automated data observability.

“With Monte Carlo, my team is the first to know when data breaks so that we can manage that incident lifecycle through PagerDuty, in turn allowing us to prevent and resolve data downtime before it impacts the business.”

Manu Raj, Senior Director of Data Platform and Analytics at PagerDuty

Online marketplaces

Manchester-based Auto Trader is the largest digital automotive marketplace in the United Kingdom and Ireland. The company sees 235 million advertising views and 50 million cross-platform visitors per month, with thousands of interactions per minute—all data points the Auto Trader team can analyze and leverage to improve efficiency, customer experience, and, ultimately, revenue.

“Whether it’s custom SQL rules or dbt tests, you have to do that upfront configuration and know in advance what it is you’re going to monitor, and go through the process of setting it up. We wanted something that would effectively get this off the ground and running without us having to put in that effort. The schema checks, the volume checks, the freshness checks that Monte Carlo offers delivers on that.”

Edward Kent, Principal Developer

“By leveraging Monte Carlo, we made data observability a core differentiator that optimized our use of data team resources and enabled the business to make decisions quickly and confidently. We saw immediate time-to-value with full coverage on our entire data set and in dimensions we were not measuring before, giving us back the time and resources we’d otherwise be spending firefighting and digging into data anomalies.”

Teresa Tang, VP of Data

Financial services

The data team at Compass, a $6b real estate company, builds and maintains the analytics pipelines for their entire 13,000-person organization. Keeping tabs on the upstream and downstream dependencies for their Looker dashboards is top-of-mind.

“Monte Carlo’s monitoring feed to Slack gives me comfort that our data is healthy and everything’s working as designed. If something goes wrong, I know we’ll be the first to know and solve.”

Suvayan Roy, Senior Product Manager


Hippo, a leading home insurance company, leverages data-driven insights to streamline the process of buying homeowner’s insurance and modernizing coverage. Hippo uses Monte Carlo to guarantee that the data powering their services is accurate and fresh.

“Monte Carlo ensures data reliability through their robust data observability engine. With Monte Carlo, I know that our data pipelines are in working order and my executives and partners can trust our analytics.”

Neta Gur-Ari Krakover, Senior R&D Manager of Data


Yotpo, a $1b e-commerce unicorn, works with companies across the world to help customer success and sales teams distribute services. The team relies on data to drive intelligent decision making, fuel efficient company operations, and deliver better customer experiences.

“With Monte Carlo, we know exactly what to update when there’s a change in our data, so there’s no downtime and no fire drills. Our decision makers are happier and I can sleep at night.”

Yoav Kamin, Director of Business Performance


Resident is a group of direct-to-consumer brands that make it effortless to find what you need for your home.They trust Monte Carlo to monitor and alert for abnormalities in their data pipelines.

“I only use 3 tabs at work: Gmail, BigQuery, and Monte Carlo. I never make a change to our data infrastructure without checking Monte Carlo first. That way, I can avoid data disasters before they happen.”

Daniel Rimon, Head of Data Engineering

Speedy, no-code onboarding

End-to-end data trust

Automated coverage that scales with your stack

Speedy, no-code onboarding

“Within minutes of deploying Monte Carlo, my team was up and running, and we had full visibility into our data pipelines, from ingestion in BigQuery to analytics in our Looker dashboards.”

End-to-end data observability

“Monte Carlo monitors and alerts for abnormalities in the data life cycle that would otherwise go unnoticed until it’s too late. Monte Carlo’s field-level lineage for Tableau is out of this world.”

Automated coverage that scales with your stack

“If something happens to our upstream production data, we know that Monte Carlo will be able to identify the root cause of this issue in the source system so that we can go back and correct it.”

Data reliability delivered.

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