Forward Deployed Data Engineer
Posted yesterday
The role in short
Embed with clients to build production data pipelines, ensure data quality, and create training/evaluation datasets for AI systems.
Lunon is an AI-native consulting firm in New York City, backed by General Catalyst. Most companies don't need another deck about AI. They need an engineer who can walk in, understand the operation, and ship. That's what we do: we embed engineers inside the business and stay until the software runs without us.
Our open-source research system ranks first on DeepResearch Bench.
The team is young and moves fast. Everyone here has a wide skill set, works with the newest tools as they land, and owns what they ship end to end. Nobody hands you a ticket.
You own the data side of an engagement: get the client's data out of the systems it lives in, make it trustworthy, and turn it into the pipelines and datasets the AI runs on. You work beside a Forward Deployed AI Engineer who owns the models. First up: helping an ERP company make its product AI-native.
- Fully remote, US-based. Lunon is headquartered in New York City.
What you'll do
- Build the pipelines that pull data out of ERP, CRM, and accounting systems and keep it moving: ingestion, processing, and storage.
- Put data quality and lineage controls in place, so anyone can trace a number back to where it came from.
- Build and manage the training and evaluation datasets the models learn from and are judged on.
- Run it in production at enterprise scale. Reliability is yours.
- Document it and hand it off so the client's team can run it on their own.
What you'll need
Experience matters here. We judge it by what you've shipped and kept running, not by a number of years.
- You've built and run data pipelines in production, including when they broke.
- You write Python and SQL well and have worked with a warehouse or lakehouse such as Snowflake, BigQuery, Databricks, or Postgres.
- You've made sense of messy data from systems you didn't design.
- You've built data for an ML or LLM system: training sets, eval sets, or retrieval.
- You use AI coding tools every day and judge the result by the system and its output, not the language.
Nice to have
Apply even if none of these are true.
- You've built on or integrated with an ERP, CRM, or accounting system.
- You've built data quality or lineage tooling other people relied on.
- You've done consulting, agency, or forward-deployed work and know how to be useful in someone else's building on day two.
How we hire
- A 30-minute conversation with the team about what you've shipped and what we're building.
- A technical deep-dive on one system you owned, from the business problem to production.
- A working session on a real client problem with the team.
- References, then an offer. We aim to finish in two weeks.
Beware of recruitment fraud. All official correspondence comes from a lunon.ai email address. We will never ask you to share bank account information, cash a check, or purchase equipment as part of the hiring process.
Prep for this interview
4 questions likely to come up at Lunon, each matched to a line in this posting.
- 1LLMs, RAG and agents
They wrote: “You've built data for an ML or LLM system: training sets, eval sets, or retrieval.”
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: “A technical deep-dive on one system you owned, from the business problem to production.”
A customer says “we want AI in our claims process.” What do you ask in the first meeting?
How to answer
Why they ask: Most FDE work starts this vague. They want to hear you find the real problem before you pick a tool.
- Map the current process: who touches a claim, in what order, with which systems
- Find the slowest or most error-prone step and ask what it costs them
- Agree on one measurable goal (for example time per claim, or error rate)
- Ask what data you can access, and who signs off on security
Tip: Ask about the work, not the technology. Who does it today, how long it takes, what a mistake costs, and how they'd know it got better.
- 3Data and SQL
They wrote: “You've built and run data pipelines in production, including when they broke.”
How would you design a daily pipeline that feeds customer data into our product?
How to answer
Why they ask: Tests basic data engineering: scheduling, failures, re-runs and monitoring.
- Extract with timestamps or change tracking, not full dumps, when possible
- Transform in steps you can re-run without creating duplicates
- Validate row counts and key fields before loading
- Alert a person on failure, with enough detail to fix it
Tip: Make re-running safe (same input, same result) and say how you'd know it failed.
- 4Coding
They wrote: “You write Python and SQL well and have worked with a warehouse or lakehouse such as Snowflake, BigQuery, Databricks, or Postgres.”
Here's a CSV export from a customer. Write a script that cleans it and loads it into our API.
How to answer
Why they ask: Very close to real FDE work. They watch how you handle bad rows and failures.
- Read with a proper CSV parser; check the header first
- Validate each row and collect errors, not crash on the first one
- Send in batches with retries and a clear report at the end
- Write one or two quick tests for the parsing
Tip: Talk through edge cases out loud: encodings, missing fields, duplicates, rate limits.
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