Barr Moses

Barr Moses is the co-founder and chief executive of Monte Carlo, the San Francisco-based company that popularised the term "data observability" and built a platform to detect, resolve and prevent broken or unreliable data pipelines. She co-founded the company in 2019 with Lior Gavish, drawing on her own experience wrestling with data quality problems in earlier operating roles.

Before starting Monte Carlo, Moses held customer-facing leadership roles at data and analytics companies, work that exposed her repeatedly to the costly downstream consequences of bad data: broken dashboards, mistrusted reports and stalled machine learning projects. She has described this recurring pain point as the direct inspiration for Monte Carlo's founding thesis, that data reliability needed the same rigorous, automated monitoring that software engineering teams had already applied to application uptime.

Under Moses's leadership, Monte Carlo grew quickly to become a category leader in data and, more recently, AI observability, raising substantial venture funding from investors including Accel, ICONIQ Growth, GGV Capital and Redpoint Ventures. She co-authored "Data Quality Fundamentals" (O'Reilly), a widely cited reference for practitioners, and has been a prolific writer and speaker on data reliability, helping establish observability as a standard layer of the modern data stack alongside storage, transformation and integration.

As the generative AI boom has pushed enterprises to deploy autonomous agents in production, Moses has repositioned Monte Carlo around monitoring and troubleshooting AI agents and the data underpinning them, framing trustworthy AI as fundamentally a data reliability problem. She has become a frequent commentator in trade press and at industry events such as Databricks' Data + AI Summit on the risks of deploying AI without adequate visibility into the data feeding it.

Moses's recognition includes being named a Top 25 Data Management and Analytics Executive and a top Woman in AI by VentureBeat, reflecting her standing as one of the most prominent female founders in enterprise data infrastructure. Her advocacy has helped push data and AI reliability from a back-office engineering concern to a topic discussed at the executive and board level.