Forward Deployed Data Engineer
Hybrid (New York City, NY)$110k - $200k + equityFull-timePosted 4mo ago
About the role
Datafold is hiring a Forward Deployed Data Engineer to own end-to-end AI-automated data platform migration projects, combining data engineering, AI tooling, and client-facing delivery.
Responsibilities
- Own 1–4 concurrent migration projects end-to-end: scoping, planning, execution, and customer handoff
- Be the primary customer contact: run weekly check-ins, manage stakeholder expectations, and escalate risks early before they compound
- Configure Datafold's Migration Agent and oversee the migration execution
- Partner with Datafold's engineering team to execute migrations
- Help refine and scale our product and delivery playbook as the team grows
Qualifications
- 3–6 years in data consulting, professional services, or a customer-facing data engineering role
- Excellent communication skills — equally comfortable in an exec check-in and a technical design session
- Strong grasp of the modern data stack: dbt, Snowflake, Databricks, orchestration tools, and major patterns (stored procedures, streaming, incremental processing)
- Extreme ownership mentality — you identify, surface, and fix problems and rally the team to help without being told
- AI power user — using AI every day and always learning and improving on how to use it more effectively
- Exposure to legacy data stack and patterns (ETL, stored procedures, etc.) and data platform migration projects is a strong plus
Skills
Required:Data engineeringdbtSnowflakeDatabricksAI tooling / prompt engineering
Benefits
- Competitive salary + equity
Last updated Jul 8, 2026. Interested? Apply on the company site.
About Datafold
Automating manual and repetitive data engineering tasks with AI
Datafold automates manual work in data engineering. We leverage agentic AI to automate both day-to-day tasks, such as testing and code reviews, and massive one-off projects, such as data platform code migrations. Companies from Perplexity to Disney use Datafold to unlock more value from their data by freeing up their data teams from manual work, accelerating developer velocity, and ensuring data quality.
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