About Eon

Eon is building the agentic data layer: the modeled, reconciled, governed data foundation that AI agents read from. Enterprises today spend 12 to 18 months wiring pipelines and hand-building semantic models before an agent can answer its first question. Eon builds that foundation automatically as data lands: captured directly from the cloud infrastructure layer, linked across structured and unstructured sources, classified and modeled on ingest, and kept fresh with no pipelines to maintain. Customers run production AI workloads on Eon at petabyte scale today, making Eon one of the fastest-growing cloud infrastructure startups in the market. Founded by the team that built CloudEndure (acquired by AWS) and backed by top-tier investors.

About the role

Eon's FDEs are the front edge of the company. You embed with some of the world's largest enterprises, take a real business problem, and ship working software on their data, often within days of first meeting them. Your demos close deals. Your prototypes become product. Your solutions run in production at Fortune 500 scale.

This is a builder's role with real ownership: you pick up a problem, you own the outcome, and the whole company sees the result.

What you'll do

• Own the technical relationship with customers end to end, from first whiteboard session to production deployment.
• Turn platform primitives into customer-ready solutions in days: live demos, POCs, and production systems on real customer data.
• Design and build data and AI solutions over structured and unstructured data: semantic layers, analytics, GenAI applications, and agent-facing data products.
• Present your work to everyone from staff engineers to the C-suite, and hold the room with both.
• Feed what you learn in the field directly into Product and Engineering. FDEs are the strongest signal in our roadmap.

What we're looking for

• An entrepreneurial mindset: you find the problem, take ownership of the outcome, and don't wait for permission or perfect requirements. Founding or building something from scratch is a strong plus.
• 8+ years of hands-on experience across data engineering, software engineering, solutions engineering, or forward deployed engineering.
• Deep data engineering background: you've built and operated data pipelines and ingestion systems at scale, and you know modern data architectures (lakehouse, open table formats, Iceberg) well enough to know where they break.
• Experience with semantic layers, data modeling, data cataloging, or knowledge and context graphs: making data meaningful and trustworthy for machines, not just queryable.
• Hands-on experience with Generative AI: LLM applications, agents, RAG, text-to-SQL, and AI-powered workflows over enterprise data.