Datadog Software Engineer Interview Questions
13+ questions from real Datadog Software Engineer interviews, reported by candidates.
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There are four rounds in total. Because I also have to work, it's scheduled for two consecutive days. The following content requires a score higher than 188. You can already view it. 1. Coding Reverse
Had an opportunity to interview with Datadog for Senior SDE. Don't remember all exact questions, but all of them were mentioned here, highly recommend to go through all posts where company is mentione
Datadog Software Engineer Tech Phone Screen Classic Problems
Two classic problems The following content requires a score higher than 188. You can already view it. 1. Given a `bucket_size` and a `bucket_width`, and a set of input numbers, return the number of nu
Given a paragraph, extract number of words which are repeated and give the output of total number of repetitions. 2. Given a tree, (different class structure was there , but...
Sharing the questions I got for Datadog tech screen and the solution I used in the interview. For both the problems the expectation is to run at least one test...
LeetCode #211: Design Add and Search Words Data Structure. Difficulty: Medium. Topics: String, Depth-First Search, Design, Trie. Asked at Datadog in the last 6 months.
#819 Most Common Word
LeetCode #819: Most Common Word. Difficulty: Easy. Topics: Array, Hash Table, String, Counting. Asked at Datadog in the last 6 months.
#1229 Meeting Scheduler
LeetCode #1229: Meeting Scheduler. Difficulty: Medium. Topics: Array, Two Pointers, Sorting. Asked at Datadog in the last 6 months.
#322 Coin Change
LeetCode #322: Coin Change. Difficulty: Medium. Topics: Array, Dynamic Programming, Breadth-First Search. Asked at Datadog in the last 6 months.
## Problem "Mado" means window in Japanese. Given an `m x n` integer matrix and a window size `k`, compute the sum of every valid `k x k` submatrix. Return all sums in a result matrix of size `(m-k+1) x (n-k+1)`. Optimize beyond the naive O(m * n * k^2) approach. ```python def mado_sum(matrix: list[list[int]], k: int) -> list[list[int]]: ... ``` ``` Input: matrix = [ [1, 2, 3], [4, 5, 6], [7, 8, 9] ] k = 2 Output: [ [12, 16], # top-left 2x2: 1+2+4+5=12; top-right: 2+3+5+6=16 [24, 28] # bot-left: 4+5+7+8=24; bot-right: 5+6+8+9=28 ] ``` ## Follow-ups 1. Describe the 2D prefix sum technique. What is its time and space complexity? 2. How does your approach handle matrices where `k > min(m, n)`? 3. What if instead of sum you need the maximum value in each window? How does your approach change? 4. Extend to a 3D tensor with window size `k x k x k`. How does the prefix sum generalize?
## Problem Simulate a change (otsuri) dispenser that returns optimal coin combinations for a given amount. ## Tags greedy, dynamic_programming
## Problem Calculate the total size of files in a directory tree, recursively summing nested folder sizes. ## Tags recursion, binary_tree, hash_table
## Problem Determine if a string follows the same pattern as a given word pattern using a bijective character-to-word mapping. ## Likely LeetCode equivalent LC 290 - word-pattern ## Tags hash_table, strings
What Datadog Looks for in Software Engineer Interviews
Datadog Software Engineer interviews are calibrated against the level and scope expected of the role. Across 13+ verified candidate reports on LeakCode, the consistent signals interviewers look for: clear problem decomposition before coding, explicit complexity reasoning, structured handling of edge cases, and the ability to articulate trade-offs between two reasonable approaches.
The discriminator between candidates who advance and candidates who do not is rarely the final correctness of the solution. It is the path to the solution: did you ask clarifying questions, did you state your approach before coding, did you handle edge cases without prompting, and did you communicate your reasoning throughout. Reports tagged "no hire" frequently cite a working solution with poor communication; reports tagged "strong hire" cite clear thinking even when the final solution was incomplete.
How To Use This Question Set
Real interview reports are a calibration tool, not a memorization target. Companies update their question pools every 2-4 months; memorizing exact problems risks misleading you when the interviewer uses a variant. The high-leverage use: identify the patterns that appear repeatedly in Datadog Software Engineer reports, practice those patterns on similar (not identical) problems, and use the reports to understand the interviewer's typical follow-up depth.
Filter the questions below by round type, difficulty, and recency. Focus first on reports from the past 6-12 months; older reports may reference questions that have since rotated out of Datadog's pool. Reports tagged with quantified difficulty (e.g., "medium-hard") are higher-signal than reports without difficulty tags.
Round-by-Round Expectations
Datadog Software Engineer loops typically span 4-6 rounds across phone screens and on-site or virtual on-site interviews. The structure varies by company: some run 1 recruiter screen + 1 technical phone + 3-4 on-site rounds; others run 1 recruiter screen + 1 OA + 4-5 on-site rounds. The recruiter screen is logistics and culture-light; the technical phone screen is medium-difficulty coding; the on-site loop covers coding, system design (at L4+ levels), and behavioral rounds.
Each round is designed to surface a specific signal. Coding rounds: correctness, code quality, complexity reasoning, communication. System design rounds: requirements clarification, design judgment, operational thinking. Behavioral rounds: ownership scope, leadership, ambiguity tolerance, conflict navigation. Strong candidates explicitly hit each signal dimension out loud during the round; weak candidates focus only on solving the prompt.
Common Interview Mistakes At This Combination
Reports tagged "no hire" at Datadog Software Engineer commonly cite: jumping into code without clarifying requirements, coding silently for 10+ minutes without verbalizing approach, missing edge cases (empty input, single element, very large input, overflow), and producing a working solution that the candidate cannot explain or refactor when probed. Strong candidates avoid these patterns by following a consistent template: clarify, verbalize approach, code with narration, test with examples.
Behavioral and design rounds have their own failure modes. Behavioral: stories that use "we" instead of "I" diluting individual signal, stories with no quantified outcome, defensiveness when probed about failure. Design: not asking clarifying questions, not stating requirements out loud, designing for a single server when the prompt clearly implies scale, ignoring operational concerns (deployment, monitoring, rollback). These show up in roughly half of Datadog Software Engineer interview retrospectives on LeakCode.
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