The FDE interview loop
FDE interviews test the same three things the job demands: can you build, can you structure an ambiguous customer problem, and can people trust you in front of a customer? Formats differ between companies, so always ask your recruiter for the loop — but most include some version of the rounds below.
Common rounds
| Round | What it tests | How to prepare |
|---|---|---|
| Practical coding | Writing working code quickly, often with real-world data handling | Practise building small end-to-end things: parse data, call an API, transform, output |
| System design with customer constraints | Architecture under enterprise realities | Practise designs with SSO, VPC-only deployment, PII, legacy integrations |
| Decomposition / ambiguous case | Turning a vague customer problem into a plan | Practise the framework below out loud |
| Customer scenario / role play | Communication, expectation-setting, handling pushback | Prepare stories; practise delivering bad news |
| Behavioural | Ownership, ambiguity, collaboration | Build a story bank (see Transitioning from SWE to FDE) |
| Presentation or take-home (sometimes) | Building and explaining a solution | Use the demo structure from Demos that win |
The ambiguous case: a framework
Interviewers give a deliberately vague prompt — "A logistics company wants to use AI to improve their operations. Where do you start?" They are watching how you think. Narrate each step:
- Clarify the goal — "What outcome does the company care about most: cost, speed, customer experience?"
- Understand users and workflow — "Who does the work today, and what does a day look like?"
- Find the pain and size it — "Where is time or money lost? Roughly how much?"
- Check data and constraints — "What data exists, and are there deployment or compliance constraints?"
- Define success — "We'd measure success by X, from a baseline of Y."
- Generate options — including a non-AI option, and compare them on value, feasibility and risk.
- Recommend and plan — pick one, describe a POC with exit criteria, and the path to production.
- Name the risks — data access, adoption, quality, security — and how you'd mitigate each.
Ask clarifying questions, but don't stall. After two or three questions, state your assumptions explicitly and move forward: "I'll assume the main goal is reducing late deliveries — tell me if you'd like me to optimise for something else."
Narrating well
- Think out loud. Silence leaves the interviewer guessing; narration shows your reasoning.
- Signpost. "I'll cover three things: the problem, the options, and a plan."
- Summarise often. Every few minutes, recap where you are and what's next.
- Invite input. "Does that match what you had in mind, or should I go deeper on the data side?"
System design, FDE-style
Start from the user and the outcome, not from boxes and arrows. Then design for the customer's reality: identity and permissions, where data must live, integration with existing systems, evaluation and monitoring, cost at production volume, and a staged rollout. Mention the security review and who has to approve what — it shows you've done this before.
What strong candidates do differently
| Weaker answer | Stronger answer |
|---|---|
| Jumps straight to an architecture | Clarifies the problem and success metric first |
| "We'd use an LLM" | Compares options, including non-AI, and justifies the choice |
| Ignores data quality and access | Treats access and data quality as first-class risks |
| One big launch | POC with exit criteria → pilot → staged production |
| Talks only to the interviewer as an engineer | Adapts language for executives, users and IT |
Practise with a timer and record yourself. You'll quickly hear where you ramble, skip steps or forget to define success.
Close every case with a crisp summary: the problem, the recommended approach, how you'd measure success, and the first two weeks of work. Interviewers often score the last two minutes most heavily.
Senior Full-Stack Engineer · AI & AWS. I turn ambiguous customer problems into production systems — cloud, data and AI.