Bigeye (formerly known as Toro), a San Francisco, CA-based data quality engineering platform, raised $17m in Series A funding.
The round was led by Sequoia Capital with participation from existing investor Costanoa Ventures.
The company intends to use the funds to improve the platform and make it available to more data teams.
Now, Bigeye is applying an engineering approach to data, making it effortless for data teams to measure, improve, and communicate data quality for their organizations.
Led by Kyle Kirwan, CEO and co-founder, and by Egor Gryaznov, CTO and co-founder, Bigeye provides a data quality engineering platform that applies proven engineering concepts from DevOps and Site Reliability Engineering (SRE). The solution automatically instruments datasets and pipelines with data quality metrics, creating actionable alerts driven by anomaly detection techniques that enable data teams to prevent incidents from impacting the business.
Customers like Instacart, Crux Informatics, and Lambda School are already using Bigeye to measure, improve, and communicate data quality on hundreds of datasets with thousands of data quality metrics. The platform will continue to improve by deepening support for the data engineering workflow, enhancing intelligence, and accelerating go-to-market to bring data quality to more data teams.
In addition to the funding, Bigeye is expanding its advisory board to include notable industry figures, like Olivier Pomel, CEO of Datadog; Brad Menezes, who headed the Application Performance Monitoring (APM) product at Datadog; Jai Ranganathan, who led the data platform team at Uber, and Ritesh Agrawal, who led infrastructural data science at Uber.
Customers use Bigeye both in the cloud and on-premises. As part of the continued improvement of the platform, the company has also increased support for Service Level Agreements (SLAs), which help data engineers build trust through transparency with their data users.