How to Prepare for the Palantir Decomposition Interview

The decomposition interview is Palantir's signature screen for Forward Deployed Software Engineers — and the stage where most otherwise-strong candidates fail. There's no algorithm to memorise and no trick to spot. You're handed a problem like "a hospital network wants to reduce patient wait times — design a system to help" and evaluated on how you turn that fog into an executable technical plan.

Because the interview mirrors the actual FDSE job — walking into a customer's mess and structuring it — preparation genuinely works. Here's the format, a worked example, the failure modes interviewers see constantly, and a two-week prep plan.

What the interview actually tests

Palantir interviewers are scoring four things:

1. Structuring under ambiguity. Can you impose useful shape on a problem that has none? The first ten minutes reveal whether you flail or frame.
2. Data instinct. Nearly every decomposition problem is secretly a data problem. Strong candidates ask early: what data exists, where does it live, how clean is it, who owns it?
3. Technical depth on demand. You choose the altitude, but the interviewer will dive wherever you sound vague. If you say "we'd build a pipeline," expect "what does the schema look like?"
4. Judgment and prioritisation. Real deployments can't build everything. Interviewers reward candidates who identify the 20% of the system that delivers 80% of the value — and say what they'd cut.

The format

• Setup (5 min): the interviewer presents a broad scenario, usually a realistic Palantir-customer shape: logistics, healthcare, energy, defence, finance.
• Your decomposition (30–40 min): you drive. Whiteboard or verbal. The interviewer plays a mix of customer stakeholder and skeptical engineer, injecting constraints as you go ("the data is on-prem", "the ops team won't use dashboards").
• Trade-offs (5–10 min): why this architecture, what breaks at scale, what you'd do with half the time.

A worked example

Prompt: "A national supermarket chain wants to reduce food waste. Design a system to help."

A strong decomposition looks something like:

1. Clarify the goal and the metric (2–3 min). "Waste" meaning expired inventory? Over-ordering? Spoilage in transit? Pick the dominant one — say, expired in-store inventory — and define the success metric: percentage of stock discarded past sell-by date.

2. Map the actors and decisions (3–4 min). Store managers decide daily orders; regional planners set assortments; suppliers set lead times. The system's job is to improve a decision — usually the store-level order quantity. Naming the decision you're improving is the single highest-signal move in the interview.

3. Inventory the data (5 min). Point-of-sale transactions (clean, high volume), inventory snapshots (probably stale), supplier lead times (in a spreadsheet somewhere), weather and local events (external). State your assumptions about quality and access — interviewers reward realism about ancient ERP systems.

4. Sketch the system (10–15 min). Ingestion (batch POS feeds, change-data-capture from the ERP) → canonical data model (products, stores, inventory positions, orders — sketch actual entities) → a demand-forecast model per product-store (start simple: seasonal moving average, upgrade later) → an order-recommendation service → the delivery surface: not a dashboard, but recommendations injected into the ordering tool managers already use.

5. Phase it (5 min). Pilot with 10 stores and the top 100 waste-heavy SKUs; measure against control stores; expand. Say explicitly what you're not building in phase one (transit spoilage, supplier renegotiation).

6. Name the risks. Data quality torpedoes forecasts; store managers ignore recommendations they don't trust (show the "why" behind each suggestion); success at 10 stores may not survive 1,000.

Common failure modes

• Solution-first syndrome. Jumping to "we'd train an ML model" before establishing what decision the model serves. The interview is about the problem, not the tech.
• Staying at PowerPoint altitude. Boxes labelled "Data Layer → AI Layer → Insights" with no schemas, no concrete flows. The interviewer will dig, find air, and mark you down.
• Boiling the ocean. Trying to solve waste, pricing, and supply chain simultaneously. Scope discipline is the FDSE's core survival skill.
• Ignoring the human. Every Palantir deployment lives or dies on whether operators actually use the thing. Candidates who never mention the end user fail a hidden rubric line.
• Not driving. The interviewer wants to see you lead. If they have to drag you through each step, that's the signal.

A two-week preparation plan

Days 1–3: Internalise a framework: goal → metric → decisions → actors → data → system → phasing → risks. Practise stating it fast.

Days 4–10: One problem per day, 40 minutes, out loud, whiteboarding. Generate prompts by combining an industry with an outcome: reduce emergency-room wait times, cut airline fuel costs, detect insurance fraud, speed up mine-to-port logistics. After each, write down where you went vague — that's where the interviewer would have dug.

Days 11–13: Do two or three with a friend playing the hostile stakeholder, injecting constraints mid-flow ("that data is siloed in a vendor system"). Recovering gracefully from constraint injections is heavily weighted.

Day 14: Read about Foundry's actual architecture (ontology, pipelines, operational apps). You don't need Palantir-specific knowledge to pass, but understanding why the platform is shaped the way it is makes your decompositions sound native.

Beyond Palantir

The same interview, lightly disguised, appears across the FDE market — OpenAI, Anthropic, and Databricks all test ambiguous problem structuring in their forward-deployed loops. Prepare once, use everywhere. For the broader loop (coding, behavioural, customer scenarios), see our FDE interview questions guide.

And if you're still weighing whether the role is right for you, start with what an FDE actually does and what they earn — then browse live FDSE openings.