Datafold is expanding its AI capabilities with a new role focused on developing their Migration Agent, a tool that automates database migrations using LLMs and proprietary diffing technology. This position represents a growing trend of startups applying AI to specialized data engineering problems, potentially disrupting traditional migration processes that typically require extensive manual work and specialized consultants.
The opportunity: Datafold is hiring a senior software engineer to lead development of their AI-powered data migration tool that promises to reduce migration timelines by 5-10x.
- The role focuses on building the Datafold Migration Agent (DMA), which combines large language models with data diffing technology to automate SQL dialect translation and data reconciliation.
- The successful candidate will design backend systems, collaborate with early customers, and make strategic technical decisions to ensure the product scales effectively.
Company context: Datafold is a Series A startup focused on data quality and observability tools for data engineers.
- Founded in 2020, the company has raised $22M from prominent investors including Y Combinator, NEA, and Amplify Partners.
- The 24-person team serves clients like Disney, FanDuel, Perplexity, Patreon, and Thumbtack while operating fully remote across the US and EU.
Technical requirements: The position requires significant backend engineering experience and specific technical capabilities.
- Candidates need 5+ years of software engineering experience with strong backend skills, with Python proficiency being mandatory.
- Experience building with large language models and JavaScript/TypeScript knowledge are considered advantages but not requirements.
Strategic focus: Datafold’s core philosophy centers on preventing data quality issues before they reach production environments.
- The company believes data quality results from effective engineering workflows rather than reactive testing.
- Their approach emphasizes addressing quality issues during the development phase and leveraging metadata to improve data engineering processes.
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