Retool·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

Jun 2026

Summary

Retool software engineer interview that was basically a two-part stock trading problem. The twist was the 'exactly' constraint instead of the usual 'at most', which sounds minor but genuinely changes how you think about the DP states.

Questions Asked (2)

Given an array of daily stock prices, complete exactly one buy followed by one sell (sell day must be strictly after buy day). Return the maximum profit, or indicate that no valid transaction exists.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

The linear scan approach came to me pretty fast, track the running minimum and compute profit at each step.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem constraints and edge cases, then propose a brute-force solution before optimizing to a single-pass O(n) approach that tracks the minimum price seen so far and the maximum profit. Explain the trade-offs between time and space complexity, and walk through a concrete example to validate the solution.

Pro tip: Mention that you can solve it in one pass without extra space, and discuss how you'd handle edge cases like decreasing prices or empty arrays—this shows you think about robustness and efficiency, which interviewers value.

1. Clarify requirements and edge cases

Ask about input size, whether prices can be negative or zero, and confirm that buy must occur before sell. Discuss what to return if no profit is possible (e.g., 0 or -1).

2. Propose a brute-force baseline

Describe the O(n^2) solution of checking every buy-sell pair, and note its inefficiency for large inputs. This sets the stage for optimization.

3. Derive an optimal O(n) approach

Explain that you can track the minimum price seen so far and compute the profit if selling today. Update the maximum profit accordingly, achieving O(n) time and O(1) space.

4. Walk through an example and edge cases

Use a sample array like [7,1,5,3,6,4] to demonstrate the algorithm, and test edge cases such as strictly decreasing prices or a single-element array.

5. Discuss trade-offs and potential follow-ups

Compare the brute-force and optimal solutions in terms of time and space complexity. Mention variations like multiple transactions or allowing short selling if relevant.

Key Points to Mention

Time complexity: O(n) for the optimal solution, O(n^2) for brute-force.
Space complexity: O(1) for the optimal solution.
The importance of tracking the minimum price seen so far.
Handling edge cases: empty array, single element, strictly decreasing prices.
Return value when no profit is possible (e.g., 0).
Potential follow-up: multiple transactions (LeetCode 122) or other variations.

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.

Extend the problem to exactly two non-overlapping buy-sell transactions. Both transactions are required, not optional. Return the maximum combined profit across both.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

This is where things got messy for me.

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AI HintsAI Generated

Suggested Approach

Clarify that both transactions are mandatory and non-overlapping, then present an O(n) dynamic programming solution using four state variables: first buy, first sell, second buy, second sell. Walk through the state transitions and explain how initializing the second buy/sell to negative infinity enforces the requirement that both transactions occur.

Pro tip: Emphasize that the mandatory nature of both transactions is the key twist—many candidates incorrectly allow skipping the second transaction. Show how initializing secondBuy and secondSell to -Infinity naturally enforces this constraint without extra checks.

1. Clarify constraints and edge cases

Confirm that both transactions are required and cannot overlap, and discuss edge cases like fewer than 4 prices or all decreasing prices where the answer may be negative.

2. Define DP states

Define four states: firstBuy (max profit after first buy), firstSell (max profit after first sell), secondBuy (max profit after second buy), secondSell (max profit after second sell).

3. Derive state transitions

For each price p, update: firstBuy = max(firstBuy, -p); firstSell = max(firstSell, firstBuy + p); secondBuy = max(secondBuy, firstSell - p); secondSell = max(secondSell, secondBuy + p).

4. Initialize states to enforce mandatory transactions

Set firstBuy and firstSell to -Infinity, secondBuy and secondSell to -Infinity, and update in order for each price. This ensures both transactions are completed.

5. Return result and analyze complexity

Return secondSell as the maximum combined profit. Explain that the algorithm runs in O(n) time and O(1) space, and discuss trade-offs versus a divide-and-conquer approach.

Key Points to Mention

Both transactions are mandatory, so the answer may be negative if all prices decrease.
Non-overlapping means the second buy must occur after the first sell.
Four-variable DP tracks the best profit at each stage with O(1) space.
Initializing secondBuy and secondSell to -Infinity enforces the mandatory second transaction.
Time complexity O(n) and space complexity O(1) are optimal for this problem.
Alternative approaches like divide-and-conquer or prefix/suffix arrays have O(n) time but O(n) space.

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.