Datadog Software Engineer Interview Questions + Guide in 2024

Datadog is a global SaaS business that enables seamless collaboration and problem-solving across Dev, Ops, and Security teams. With a mission to break down silos and solve complexity in the cloud age, Datadog supports digital transformation, cloud migration, and infrastructure monitoring for organizations of all sizes.

This guide will help you understand the interview process, anticipate common Datadog software engineer interview questions, and position yourself for success. Completing this guide could significantly boost your chances of landing the role, as it is tailored to what Datadog looks for in its candidates.

Typically, interviews at Datadog vary by role and team, but commonly Software Engineer interviews follow a fairly standardized process across these question topics.

Recruiter Call Screening

Once your application catches the eye of the recruiters, you will receive an initial call typically lasting about 30 minutes. This call usually involves discussing your background, skills, and motivation for applying to Datadog. The recruiter will provide you with details about the role and the interview process. In some instances, they may also address your compensation expectations and availability.

Technical Phone Screen

If you pass the initial recruiter screening, the next step is a technical phone screen. This session typically involves solving 1 or 2 coding problems using tools like Coderpad. The questions can range from easy to medium difficulty. This stage is crucial to assess your problem-solving capabilities and coding proficiency in a real-time environment.

Take-Home Assignment

Following the technical phone screen, candidates are often given a take-home project which should ideally be completed within 3-4 hours. This project is designed to simulate real-world problems and allows you to showcase your coding best practices, documentation skills, and ability to deliver a polished, functional solution.

Virtual Onsite Interviews

Successfully completing the take-home assignment leads to an invitation for the virtual onsite interview rounds. These rounds often span across several hours and include multiple interviews such as:

• Live Coding Session: Typically involves solving real-world coding problems in a timed environment.
• System Design Interview: Involves discussing your approach to designing scalable and reliable systems. Focus is often placed on trade-offs, failure scenarios, and deep dives into system components.
• Behavioral Interview: Conducted by a hiring manager, this session usually focuses on discussing your past projects, challenges faced, and how you interact within a team setting.
• Team Matching Session: Once you clear the technical and behavioral interviews, a matching session is scheduled to align you with a team that best fits your skills and interests.

Receiving an Offer

If you successfully navigate through all interview rounds, you will have a debriefing session with the recruiter to receive feedback and discuss the final offer. The recruiter will also facilitate connecting you with potential future team members.

Practice for the Datadog Software Engineer interview with these recently asked interview questions.

1. Create a function n_frequent_words to find the top N frequent words in a paragraph.

Given a paragraph string and an integer N, write a function n_frequent_words that returns the top N frequent words in the posting along with their frequencies.

2. What’s the function run-time?

Given an example paragraph string and an integer N, write a function n_frequent_words that returns the top N frequent words in the posting and the frequencies for each word.

Example:

Input:

posting = """
Herbal sauna uses the healing properties of herbs in combination with distilled water.   
The water evaporates and distributes the effect of the herbs throughout the room.   
A visit to the herbal sauna can cause real miracles, especially for colds. 
"""  
n = 3

Output:

n_frequent_words(posting, N) = [
    ('the', 6), 
    ('herbal', 2), 
    ('sauna', 2),
]

3. What are the Z and t-tests, and when should you use each?

Explain the purpose and differences between Z and t-tests. Describe scenarios where one test is preferred over the other.

4. How would you reformat student test score data for better analysis?

Given two datasets of student test scores, identify drawbacks in their current format. Suggest formatting changes and discuss common issues in “messy” datasets.

5. What metrics would you use to evaluate the value of marketing channels?

Given data on marketing channels and costs for a B2B analytics dashboard company, identify key metrics to determine each channel’s value.

6. How would you determine the next partner card using customer spending data?

With access to customer spending data, outline a method to identify the best partner for a new credit card.

7. How would you investigate if a redesigned email campaign increased conversion rates?

Analyze a scenario where a new email journey increased conversion rates. Determine if the increase is due to the redesign or other factors.

8. How does random forest generate the forest and why use it over logistic regression?

Explain how random forest generates multiple decision trees and why it might be preferred over logistic regression in certain scenarios.

9. When would you use a bagging algorithm versus a boosting algorithm?

Compare two machine learning algorithms and provide examples of tradeoffs between using bagging and boosting algorithms.

10. How would you evaluate and compare two credit risk models for personal loans?

1. Identify the type of model developed by a co-worker for loan approval.
2. Describe how to measure the difference between two credit risk models over time.
3. List metrics to track the success of the new model.

11. What’s the difference between Lasso and Ridge Regression?

Explain the key differences between Lasso and Ridge Regression techniques.

12. What are the key differences between classification models and regression models?

Describe the main differences between classification and regression models in machine learning.

13. How would you design a function to detect anomalies in univariate and bivariate datasets?

If given a univariate dataset, how would you design a function to detect anomalies? What if the data is bivariate?

14. What are the drawbacks of the given student test score data layouts, and how would you reformat them for better analysis?

Assume you have data on student test scores in two layouts (dataset 1 and dataset 2). What are the drawbacks of these layouts? What formatting changes would you make for better analysis? Describe common problems in “messy” datasets.

15. What is the expected churn rate in March for customers who bought a subscription since January 1st?

You noticed that 10% of customers who bought subscriptions in January 2020 canceled before February 1st. Assuming uniform new customer acquisition and a 20% month-over-month decrease in churn, what is the expected churn rate in March for all customers who bought the product since January 1st?

16. How would you explain a p-value to a non-technical person?

Explain what a p-value is in simple terms to someone who is not technical.

17. What are Z and t-tests, and when should you use each?

Describe what Z and t-tests are, their uses, differences, and when to use one over the other.

You should plan to brush up on any technical skills and try as many practice interview questions and mock interviews as possible. A few tips for acing your Datadog Software Engineer interview include:

• Be Prepared for High-Level Technical Discussions: Datadog values candidates who can quickly and effectively solve complex problems. Thus, brush up on system design and coding skills, focusing on scalability and real-world applications.
• Attention to Detail in Take-Home Assignments: Invest quality time in your take-home project to ensure it is well-documented, follows best practices, and addresses all given requirements thoroughly.
• Effective Communication is Key: Throughout the process, clear and concise communication, especially in explaining your thought processes during coding or design interviews, can significantly impact your performance.

What is the average salary for a Data Scientist at Expedia, Inc.?

What is the company culture like at Datadog?

Datadog fosters an inclusive, diverse, and innovative work environment. The company values creativity and collaboration and provides flexibility in working arrangements. They strive to make a positive impact and are committed to employee growth and a strong support system.

How can I prepare for the take-home assignment for the Software Engineer role at Datadog?

To prepare for the take-home assignment, focus on refining your data analysis and presentation skills. Make sure to understand the business context and be ready to discuss your approach and findings during the presentation. Practicing similar tasks on Interview Query can help build the necessary skills.

What are some common responsibilities for a Software Engineer at Datadog?

Responsibilities include applying knowledge in SQL, Python, or R to solve business problems, optimizing processes, and communicating complex analytical concepts clearly. Data Scientists collaborate with cross-functional teams to derive actionable insights and support marketing and capital allocation decisions.

For the Software Engineer position at Datadog, you’ll be expected to solve real-world problems, design scalable systems, and work across teams to deliver innovative solutions. Key skills include experience with languages like Python, Go, or Java, proficiency in system design, and strong problem-solving abilities. The interview process, although rigorous, is designed to be efficient and supportive, reflecting the company’s commitment to a positive candidate experience.

If you want more insights about the company, check out our main Datadog Interview Guide, where we have covered many interview questions that could be asked. Additionally, explore our interview guides for other roles such as data engineer and data analyst to learn more about Datadog’s interview process for different positions.

Good luck with your interview!