Datadog It is a cloud-native monitoring company known for its technology-driven approach, with an interview style that favors practical engineering, and places great importance on system understanding, data structure mastery, and problem modeling skills. Today's interview compiles the real experiences of our coached students, sharing the interview process of Datadog, as well as the English question faces and code explanations of three high-frequency questions, so that you can easily prepare for them in a targeted way.
Brief description of the interview process (SWE positions)
Technical interviews at Datadog typically consist of 3 to 4 rounds.
The first is OA, which mainly examines algorithmic questions, usually using the CodeSignal or Codility platform, and the number of questions is 2~3, which is on the medium-high difficulty level.
After passing the OA, you will enter the first round of technical interview, the interviewer will let you do one or two coding questions on the spot, focusing on your understanding and ability to implement common data structures (e.g. hashmap, heap, stack, queue) and algorithms (DFS, BFS, double pointer, etc.).
Next may be a round of system design interviews, biased bottom some, such as the design of a metrics aggregation service or log collection system, the requirements from the API access, data processing, storage and service monitoring for the whole process design.
Finally, there is the Behavioral Round, which is more geared towards understanding your communication style, conflict resolution skills, and the way you think when working across teams.
Datadog Interview Top 3 High Frequency Questions
1. Merge Intervals
Given a collection of intervals, merge all overlapping intervals.
Input: [[1,3],[2,6],[8,10],[15,18]]
Output: [[1,6],[8,10],[15,18]]
Approach.
Sort the intervals based on the start time. Then iterate through them and merge overlapping ones by comparing to the last interval in the merged list.
def merge(intervals):
intervals.sort(key=lambda x: x[0])
merged = []
for interval in intervals.
if not merged or merged[-1][1] < interval[0]:
merged.append(interval)
else: merged[-1][1] < interval[0]: merged.append(interval)
merged[-1][1] = max(merged[-1][1], interval[1])
return merged
2. LRU Cache
Design a data structure that follows the constraints of a Least Recently Used (LRU) cache.
Approach.
Use an OrderedDict (or a hashmap + doubly linked list) to maintain key-value pairs in access order. On get or putIf over capacity, evict the least recently used entry.
from collections import OrderedDict
class LRUCache.
def __init__(self, capacity).
self.cache = OrderedDict()
self.capacity = capacity
def get(self, key).
if key not in self.cache: return -1
self.cache.move_to_end(key): if key not in self.cache: return -1
self.cache.move_to_end(key): if key not in self.cache: return -1
return self.cache[key]: if key not in self.cache: return -1 self.cache.move_to_end(key)
def put(self, key, value): if key in self.cache: return -1
if key in self.cache: self.cache.move_to_end(key).
self.cache.move_to_end(key)
self.cache[key] = value
if len(self.cache) > self.capacity: if key in self.cache: self.cache.move_to_end(key)
self.cache.popitem(last=False)
3. Find K Closest Elements
Given a sorted array, two integers k and x, return the k closest integers to x in the array.
Approach.
Use binary search to find the left boundary of the result window, then return a subarray of length k. This runs in O(log n + k) time.
def findClosestElements(arr, k, x).
left, right = 0, len(arr) - k
while left arr[mid + k] - x.
left = mid + 1
else: right = mid
right = mid
return arr[left:left + k]
Frequently Asked Questions (FAQ)
Q1: Is Datadog's OA difficult? How does it compare with FAANG?
On the whole, Datadog's OA is more on the practical side of engineering, and the questions are highly targeted. Difficulty between Google OA and Stripe OA, both to realize the details and to understand the system, not a pure brush company, but also will not come out too biased questions.
Q2: What type of candidates does Datadog prefer?
Datadog values engineering background and hands-on skills. If you have experience in backend, monitoring system, metrics/logs, DevOps and other related projects, it will be a big plus; otherwise, you can also emphasize "debugging ability + architectural understanding" through your resume and interview strategy.
Q3: Will you talk about the project in depth during the interview?
It will be, very in-depth. The interviewer would keep pressing for details from the resume project, such as, "How were logs collected? What framework was chosen? Why? How do you monitor it after it goes live?" Be sure to familiarize yourself with the full process of the project.
Q4: Can I pitch even if I haven't done a monitoring/metrics program?
Of course you can! We recommend digging into the "monitorability" and "system engineering" aspects of the project, such as log management, exception handling, interface monitoring, etc.
Q5：Does Datadog support telecommuting?
Yes, some positions are remote-eligible, but internships and New Grad positions give preference to East/Central US candidates. If you are in North America, indicate your time zone and select the closest location when submitting.
Q6: Can I still get help if I'm not prepared?
Sure! We offer:
- High-frequency questions + mock interview
- Coding / System Design real-time assists
- Project Experience Packaging + Technical Highlights
- Behavioral Interview Story Templates + Interview Conversation Optimization
How we helped our students to pass the Datadog interview?
Background Information: Master's Degree in CS｜Backend Internship｜First contact with Datadog｜Targeted Position: SWE New Grad
This student encountered challenges in advancing both OA and VO, and finally got the offer with our assistance in the whole process.
assist strategy
1. OA ghostwriting + simultaneous problem-solving explanations
Datadog's OA is conducted on the CodeSignal platform, and the questions are time-critical and unevenly distributed in difficulty. We remotely complete the OA submissions and synchronize the explanations to ensure high quality code.
Topics include 2 Medium + 2 Hard, covering shortest paths to graphs and matrix state transfers, which are easy to time out when done independently.
2. VO real-time technical assistance (voice + code synchronization)
In the interview, we help students to clarify the solution process through voice and code prompts, adding boundary conditions and test ideas to ensure that the complete solution is demonstrated.
3. Behavioral story templates & rapid response exercises
Behavioral surface templates matching Datadog's cultural keywords (Ownership, Execution, Scale) were rewritten and simulation exercises were conducted to improve the compactness of expression and richness of detail.
In the end, the participant said in the Offer Call, "Without the live voice prompts, I probably wouldn't have been able to even talk about the test points, thank you so much!"
Programhelp Helps You Get Datadog Interviews
Preparing for Datadog OA or technical interview, Programhelp provides one-on-one coaching, interviewing, code writing, VO assistance, OA assistance, and other full-process services to help you get to the bank efficiently and personally.
