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§ Private Profile · New York City, NY, USA
AI-powered data engineering automation platform for technology companies, focused on modern data stack migrations and data quality.
Datafold has raised $22.1M across 2 funding rounds.
Key people at Datafold.
Datafold was founded in 2020 by Alex Morozov (Founder) and Gleb Mezhanskiy (Founder).
Datafold has raised $22.1M in total across 2 funding rounds.
Based in Walnut Creek, California, Datafold develops an artificial intelligence-powered automation platform that assists data engineering teams with complex operational tasks such as database migrations, continuous integration, and code reviews. The enterprise software-as-a-service platform facilitates rapid transitions to modern data stacks like Snowflake by providing automated SQL translation, data quality validation, and column-level lineage tracking for reliable pipeline management. Operating with a workforce of 30 employees, the company currently serves over 50 technology and media organizations across North America and Europe, including notable enterprise customers such as Perplexity and Disney. Backed by early support from Y Combinator, the startup has secured significant capital to scale its operations, including a $20 million round in 2021 and an additional $4 million in venture funding in May 2025. Datafold was founded in 2020 by Gleb Mezhanskiy and Alex Morozov.
Datafold was founded in 2020 by Alex Morozov (Founder) and Gleb Mezhanskiy (Founder).
Datafold has raised $22.1M in total across 2 funding rounds.
Datafold's investors include Sarah Catanzaro, Pete Sonsini, 355 Capital, Andreessen Horowitz, Angel investor, Angel Operator Partners (aop), Astir Ventures, Cedar Capital Group, David Namdar, Family Office, Insight Partners, Kleiner Perkins.
Key people at Datafold.
Datafold has raised $22.1M across 2 funding rounds. Most recently, it raised $20.0M Series A in November 2021.
| Date | Round | Lead Investors | Other Investors | Status |
|---|---|---|---|---|
| Nov 1, 2021 | $20M Series A | Sarah Catanzaro, Pete Sonsini | 355 Capital, Andreessen Horowitz, Angel Investor, Angel Operator Partners (aop), Astir Ventures, Cedar Capital Group, David Namdar, Family Office, Insight Partners, Kleiner Perkins, Race Capital, SeedInvest, Thrive Capital, UpHonest Capital, Chintan Patel, Evan Cheng, Jared Sleeper, Mattia Astori, Miras Mami, Ozzy Akay, Sahin Boydas, Vishal RAO | Announced |
| Nov 19, 2020 | $2.1M Seed | NEA | — | Announced |
Datafold is a data engineering company that automates manual and repetitive tasks using AI, focusing on accelerating data platform migrations, code testing, and data quality monitoring. Its product helps data engineering teams by automating workflows such as testing code changes, reviewing pull requests, and validating data migrations, thereby increasing developer velocity and ensuring data integrity. Datafold serves enterprises and technology companies that rely heavily on data pipelines and analytics, solving the problem of slow, error-prone manual data engineering processes. The company has demonstrated growth momentum by partnering with over 50 technology firms and serving clients like Disney and Perplexity, reflecting strong adoption in the data engineering ecosystem[1][2][3][5].
Founded in 2020 by Gleb Mezhanskiy, who previously built data platforms at Autodesk, Lyft, and Phantom Auto, Datafold emerged from his firsthand experience with poor data quality and observability challenges in data-driven environments. The idea was to create a proactive data quality testing tool that integrates easily with existing data setups, starting with regression testing and evolving into a comprehensive platform for impact analysis and cross-database validation. Early traction came from launching on HackerNews and building features that address real pain points in data engineering workflows, such as column-level lineage and automated code review[2][4].
Datafold rides the growing trend of AI-driven automation in data engineering, addressing the critical need for faster, more reliable data workflows amid increasing data complexity and volume. The timing is favorable due to the widespread adoption of cloud data platforms, the rise of data-driven decision-making, and the shortage of skilled data engineers. Market forces such as the demand for continuous integration/continuous deployment (CI/CD) in data pipelines and the need for proactive data quality monitoring work in Datafold’s favor. By automating tedious tasks, Datafold influences the broader ecosystem by enabling data teams to focus on innovation and strategic initiatives, thus accelerating digital transformation across industries[2][3][6][7].
Looking ahead, Datafold is poised to deepen its AI capabilities, expanding automation in code review and migration processes to further reduce manual toil. Trends such as the increasing complexity of data environments, the push for real-time analytics, and the integration of large language models (LLMs) in data workflows will shape its journey. Datafold’s influence is likely to grow as it helps organizations unlock more value from their data with higher velocity and quality, potentially becoming a foundational platform in the data engineering space. Its continued partnership expansion and AI innovation will be key drivers of its future success[6][7].
This trajectory ties back to Datafold’s mission of empowering data engineers by automating repetitive tasks, enabling them to deliver business value faster and with greater confidence.
