Forward Deployed Engineer
Posted yesterday
The role in short
Embed with enterprise customers to design, build, and deploy production data and AI solutions, including semantic layers and agent-facing data products.
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.
You build fast with limited guidance, and you're proud of what you ship.
You communicate like an owner: clear, direct, and credible with executive and C-level stakeholders.
Prior FDE experience is a plus, not a requirement.
Prep for this interview
4 questions likely to come up at Eon.io, each matched to a line in this posting.
- 1LLMs, RAG and agents
They wrote: “Hands-on experience with Generative AI: LLM applications, agents, RAG, text-to-SQL, and AI-powered workflows over enterprise data.”
A customer wants a chatbot over their internal documents. How do you build it?
How to answer
Why they ask: RAG is the most common first project. They want the whole path, from messy files to answers people trust.
- Collect and clean the documents; handle permissions per user
- Split them into chunks, embed, and store with source links
- Retrieve, then answer with citations; say “I don't know” when retrieval is weak
- Build a test set of real questions with expected answers before launch
Tip: Spend more time on the data and on testing than on the model.
- 2Discovery and scoping
They wrote: “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.”
Tell me about a time the customer asked for one thing but needed another.
How to answer
Why they ask: Good FDEs spot when a request is a guess at a solution. They want proof you can redirect a customer without losing their trust.
- What they asked for, and the detail that made you doubt it
- How you tested the doubt (shadowed users, looked at logs, ran a quick prototype)
- How you made the case to the customer
- What shipped and what changed for them
Tip: Pick a story where you checked your hunch with data or users before pushing back.
- 3Data and SQL
They wrote: “Experience with semantic layers, data modeling, data cataloging, or knowledge and context graphs: making data meaningful and trustworthy for machines, not just queryable.”
Write a query that finds each customer's first and most recent order.
How to answer
Why they ask: A quick check that you're comfortable with grouping, joins and window functions.
- Clarify the tables and what counts as an order (cancelled ones?)
- Use MIN/MAX with GROUP BY, or ROW_NUMBER() if you need the whole row
- Explain how you'd test it on a few known customers
Tip: Say what you'd check in the data before writing the query: duplicates, time zones, nulls.
- 4Ownership and working with customers
They wrote: “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.”
Tell me about something you shipped that broke in production.
How to answer
Why they ask: Everyone has broken production. They want to hear you own it and change something afterwards.
- What broke and who it affected
- How you found out and what you did in the first hour
- The root cause, said plainly
- What you changed so it can't happen the same way again
Tip: Pick a real one with a cost. Spend most of the answer on what you changed.
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