Datadog is a leading monitoring and analytics platform for cloud applications. With its rapid growth and strong engineering culture, Datadog has become a coveted workplace for developers and technologists. Landing a job at Datadog requires you to demonstrate your technical skills and problem-solving abilities during the interview.
To help you prepare, here are 10 commonly asked Datadog interview questions along with sample responses:
1. What experience do you have working with monitoring and observability tools?
In my current role, I actively use tools like Prometheus, Grafana, and ELK stack to monitor infrastructure and applications. I’ve set up dashboards, alerts, and data visualizations to gain insights into systems performance. This allows me to troubleshoot and identify issues proactively before they cause outages. I’m excited by the scale and capabilities Datadog offers through its unified platform. My hands-on experience would enable me to quickly get up to speed on the Datadog suite.
2. How would you monitor the health and performance of a web application?
I would start by identifying key metrics like request rates, response times, error rates, and saturation levels of resources like CPU, memory, and I/O. Capturing this timeseries data gives visibility into the app’s performance. I would set up default dashboards in Datadog to visualize trends. By setting alerts on critical metrics, I could get notified for anomalies or failures. Logging key events like errors or slow requests would provide granular diagnostics. I would also monitor connected infrastructure to get context on issues. Taking this full-stack approach helps monitor the web app end-to-end.
3. How would you troubleshoot a performance degradation issue reported by a customer?
First, I would reproduce the issue to understand the customer’s experience. Next, I would leverage Datadog performance monitoring tools to identify abnormal metrics correlated with the degradation. Drilling down into related logs would provide further context into root cause. If needed, I would pull in additional data sources and traces to pinpoint the bottleneck. Finally, I would aggregate my analysis into a report for the customer highlighting the issue, its technical details, and recommended fixes. My aim would be rapid root causing driven by Datadog’s data correlation capabilities.
4. What techniques or tools have you used for monitoring and alerting at scale?
In large-scale environments, I’ve used tools like Prometheus to aggregate metrics from multiple sources and visualize data. For alerting, I’ve found grouping related alerts into a single notification is crucial to avoid overwhelmed on-call staff. Tools like PagerDuty help organize and route alerts efficiently. I also advocate for having playbooks for remediation to speed up incident response. Furthermore, investing in automation reduces alert fatigue. Datadog’s machine learning-powered alerting and intelligent incident response could take these capabilities to the next level.
5. How would you optimize Datadog usage to reduce costs for a customer?
I would start by analyzing logs and metrics to identify usage trends and peak demand signals. This data can pinpoint opportunities to scale down infrastructure during off-peak periods, reducing costs. Eliminating redundant or unnecessary data streams and alerts also improves efficiency. Another approach is Rightsizing to match data granularity, retention, and cardinality with actual needs. Finally, providing user training on best practices for instrumenting metrics and logging helps avoid waste. With its advanced analytics, Datadog is ideally positioned to provide visibility and control over usage costs.
6. What best practices do you follow for application monitoring?
Some best practices I follow include instrumenting key business metrics beyond technical metrics, setting up. Monitoring end-user experience via synthetic checks. Enabling tracing early to get granular visibility. Tagging metrics consistently for better filterability. Making dashboards customizable for different user personas. Building meaningful alerts aligned with business impact. Ensuring observability is embedded across all stages of the product lifecycle. Following DevOps principles and using collaborative tools like Slack. Datadog makes it easy to implement many of these monitoring best practices.
7. How would you troubleshoot a performance issue caused by a distributed application?
Distributed systems introduce challenges with correlated failures and cascading effects. I would leverage distributed tracing to reconstruct request flows across services. This helps localize slowdowns to specific components. Analyzing metrics for each service in conjunction with traces highlights anomalies indicative of a root cause. Logging provides additional context. Isolating the issue may require simulations to pinpoint the chokepoint. Datadog’s intuitive visualizations and APM simplify these complex troubleshooting scenarios for distributed systems.
8. Why are you interested in working at Datadog?
I’m drawn to Datadog’s collaborative culture that values innovation and learning. The opportunity to work on a cutting-edge monitoring platform is extremely appealing as someone passionate about observability. Datadog’s scale and reach within the industry is impressive. I’m excited by the complex challenges involved in operating a global SaaS product as well as the opportunity to deliver customer value. Most of all, I strongly align with Datadog’s charter around diversity, transparency, and community service.
9. How would you improve monitoring for a legacy application not optimized for observability?
For legacy apps, I would focus on retrofitting critical signals like request rates, error counts, and performance data via instrumentation. This provides baseline visibility quickly without needing to modify the app I can then work on tagging and organization to support filtering. Sampling high-cardinality data helps control volume Synthetics and logs augment metrics data. Ultimately, I would drive incremental improvements centered around key user journeys, converting blind spots to visibility with Datadog’s agent and integrations framework.
10. How do you stay updated on the latest developments in observability?
I’m an avid reader of industry publications like the Datadog blog and DevOps-focused sites that provide insights into monitoring best practices. I follow thought leaders on Twitter and LinkedIn who share prescriptive advice. I attend meetups and conferences focused on observability. I’ve also completed courses on topics like metrics, tracing, and logging. Within my own company, I participate in regular knowledge sharing to socialize new techniques. I’m committed to lifelong learning to stay abreast of innovations in this space.
With observability becoming critical for engineering teams, expect questions that assess your hands-on experience and strategic thinking. Demonstrate your user focus, communication abilities, and passion for this domain. Highlight how you deliver robust monitoring today, and your excitement for Datadog’s leading capabilities. With some preparation, you’ll be ready to have a winning interview. Good luck!
Types of Interview Questions to Expect at Datadog
From the first technical phone screen to the onsite coding rounds, you can expect algorithmic questions. However, they probably won’t be exact copies of questions from LeetCode; Datadog has their own question bank. We’ve heard that the questions are a hybrid between practical and LeetCode-style. They might start with something like what you can find on LeetCode and then add more complexity. Datadog themselves recommend practicing medium-level LeetCode questions. You might be asked questions like these:
- Bucketing numbers given specific requirements
- Find the sum of the sizes of all the files in all subdirectories from a root directory.
- Write a buffered file with an interface and a file class.
Below are the technical topics you’re likely to encounter in Datadog interviews. To compile this list, we did two things. First, we spoke to some current and former Datadog engineers. Then we cross-referenced all the anecdotes we heard with Glassdoor data AND our own data-set of mock interviews:
We’ve heard that this round is less broad than it can be at other companies. You won’t be asked to “Design Twitter”, for example. Instead, you might be asked, “Given a service that returns flight deals for the last seven days, design a system that shows relevant flight data to a user and lets them know when there’s a new flight that fits their needs.” ”.
As one of our users said:
It may be a fair interview, but it’s also used to level up, so make sure you know how systems work. We’ve heard of candidates being down-leveled for less-than-flawless performance here.
This interview will be with someone in a leadership role at the company, possibly a director. It will contain some standard behavioral questions but also some technical questions about your past work.
You might be asked to show a simple design of something you built at a previous company. They will want to know why certain design decisions were made so they can see how you affected the project and how much experience you have working with others.
Step 1: Recruiter Call
The 30-minute call with Datadog’s recruiter is pretty standard: they’ll ask you about your past work, why you’re interested in working for Datadog, and what you want to do next.
At this point, it’s very important not to say how much you want to be paid or where you are in the process with other companies. We wrote an in-depth post about negotiating salaries that tells you exactly what to say if recruiters make you name the first number.
Get Ready For the Next Step in Your Career – Prepare For an Interview With Datadog
FAQ
Is a Datadog interview hard?
Why do you want to work for Datadog?
Does Datadog do LeetCode?
How to prepare for a Datadog interview?
Owing to its popularity, it has become one of the main assessment areas in technical and cloud-based platform interviews. You must prepare adequately by anticipating and finding solid answers to the questions you are likely to be asked in a Datadog interview if you have one scheduled.
How long does it take to get a job at Datadog?
The process took 4 weeks. I interviewed at Datadog First they send you a coding assessment that you have to complete in 2 weeks (Leetcode questions), then interview with HR, technical interview (live coding) with an engineer and lastly team fitting interview.
Why would you want to work at Datadog?
Before diving into the interview questions, it’s important to understand why you might want to work at Datadog. Here are a few reasons: Industry-leading company: Datadog is a well-established and highly regarded company in the monitoring and analytics space.
What is Datadog process like?
I interviewed at Datadog Process is similar to big tech: multiple rounds of interviews, algorithmic, system design and culture/values. At first I was impressed by the organization of the process, the recruiters help you prepare for the interview and give you tips.
