DataHub, the leading open-source AI Context Platform, has bolstered its executive team with two strategic hires: James Mayfield as Vice President of Product and Dinesh Rathi as Vice President of Engineering and India Site Lead. These appointments position the company to accelerate product velocity and scale engineering capacity amid surging demand for trusted data context in the AI era.
As enterprises increasingly depend on comprehensive data context to power reliable AI systems, DataHub continues to invest in world-class product and engineering leadership.
James Mayfield brings deep expertise in data infrastructure and analytics product strategy. With over 18 years of experience, he most recently served as Senior Director of Product Management at dbt Labs following the acquisition of Transform, the metrics platform he co-founded in 2020.
Dinesh Rathi joins with more than 20 years building scalable data platforms and leading global engineering teams. He previously held the same dual role of VP Engineering and India Site Lead at Alteryx and served as Global Head of Engineering at Trifacta.
"James and Dinesh bring exceptional product vision and engineering excellence at a pivotal moment for DataHub," said Swaroop Jagadish, CEO and co-founder of DataHub. "As enterprises increasingly recognize that AI systems require comprehensive data context to operate reliably, our product and engineering capabilities must evolve rapidly."
These strategic hires enhance DataHub’s ability to deliver cutting-edge capabilities to its open-source community and enterprise customers while expanding its global engineering footprint through a new development center in India.
About DataHub
DataHub transforms enterprise data into trusted context, enabling intelligent decision making by humans and AI agents. As the leading AI Data catalog built with a thriving open-source community of 14,000+ members and 3,000+ organizations worldwide, DataHub Cloud unifies AI-powered discovery, governance, and observability —helping enterprises scale data operations efficiently while ensuring data quality, compliance, and AI-readiness across their entire data estate.
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