A DevOps Engineer at Hopper plays a critical role in powering one of the world’s fastest-growing travel marketplaces. The company processes massive volumes of real-time data, complex financial transactions, and rapid search queries every second. To support this scale, the platform relies on a highly resilient, automated, and scalable cloud-native infrastructure. As a DevOps Engineer, you will not simply manage servers; you will build the core platform that enables software engineering teams to deploy and scale their microservices seamlessly.

The impact of this role is directly felt by millions of users who rely on Hopper to secure the best travel deals. By optimizing deployment pipelines, managing Kubernetes clusters, and architecting service meshes, you directly influence application performance, platform uptime, and developer velocity. You will work closely with software developers and system architects to design infrastructure that is as dynamic as the travel market itself.

At Hopper, the boundary between software engineering and system operations is highly fluid. The hiring team looks for candidates who possess a strong software development mindset. If you enjoy solving complex distributed systems problems, automating away operational toil, and designing infrastructure as code, this role offers an exceptionally challenging and rewarding environment.

The questions you will face during the Hopper interview process are designed to evaluate both your practical coding skills and your systems architecture capabilities. These questions are drawn from real interview experiences of past candidates and represent the core patterns you should prepare for. Rather than memorizing specific answers, focus on understanding the underlying engineering principles behind each scenario.

System Design & Architecture

These questions assess your ability to design scalable, fault-tolerant systems from the ground up, with a focus on real-time data ingestion and processing.

• How would you design a distributed web scraper from scratch to collect high-volume pricing data?
• How do you ensure high availability and rate limiting in a public-facing API gateway?

To succeed in the Hopper interview process, you must approach your preparation with a structured strategy. The hiring team evaluates candidates across several key dimensions, and demonstrating strength in each is essential for securing an offer.

Software Engineering Foundations – Unlike traditional system administration roles, Hopper expects its DevOps Engineers to have strong programming skills. You must be comfortable writing clean, testable code in languages like Python, Go, or Java, and be ready to solve algorithmic problems in real-time.

Systems Architecture & Scalability – You need to demonstrate a deep understanding of distributed systems, network protocols, caching strategies, and data storage. Interviewers will look at how you handle edge cases, scale bottlenecks, and single points of failure in your designs.

Platform Domain Expertise – You must showcase hands-on experience with modern platform tools, specifically Kubernetes, cloud providers (such as GCP or AWS), and infrastructure-as-code tools like Terraform. Knowing how these technologies work under the hood is highly valued.

Communication & Collaboration – Technical competence alone is not enough. You must be able to articulate your thought process clearly, ask clarifying questions when faced with ambiguous requirements, and collaborate productively with your interviewers.

The interview process at Hopper is highly technical, fast-paced, and designed to evaluate your practical engineering capabilities. The company bypasses lengthy theoretical discussions in favor of hands-on coding and real-world system design challenges. Candidates can expect a streamlined but rigorous progression that tests both software development and platform engineering skills.

The process typically begins with a recruiter screen to discuss your background, career goals, and alignment with the company culture. From there, you will move directly into intense technical evaluation rounds. These rounds often involve senior technical leadership, including co-founders and Principal Site Reliability Engineers, ensuring that candidates are evaluated by the very teams they will be supporting.

The timeline above outlines the typical progression from the initial recruiter screen to the final decision. Candidates should use this timeline to pace their preparation, ensuring they are fully ready for live coding and system design by the second stage. While the process is designed to move quickly, the technical depth required at each step remains high.

Live Coding & Problem Solving

The coding assessment is a critical hurdle in the Hopper technical loop. It is designed to test your ability to write clean, functional code to solve a practical problem, rather than testing your memorization of obscure algorithms.

Be ready to go over:

• Data manipulation and parsing – Writing scripts to process complex data formats, such as JSON, CSV, or raw application logs.
• Resource management – Implementing algorithms that optimize resource allocation or handle rate-limiting logic.
• Error handling and edge cases – Ensuring your code is robust, handles unexpected inputs gracefully, and includes proper validation.
• Advanced concepts – Concurrent programming, asynchronous I/O, and writing custom tooling to interact with cloud APIs.

Example scenarios:

• "Write a script that parses a stream of server logs and identifies the top IP addresses causing 5xx errors within a rolling window."
• "Implement a functional rate-limiter class that can be integrated into an API gateway to prevent service abuse."

System Design & Architecture

The system design interview evaluates your ability to architect large-scale, distributed systems that are resilient, scalable, and cost-effective. Interviewers want to see how you approach open-ended, ambiguous problems.

Be ready to go over:

• Distributed data collection – Designing systems that can reliably scrape, ingest, and process data from external sources at scale.
• Microservices orchestration – Scaling containerized workloads, managing service-to-service communication, and implementing service discovery.
• Data storage and caching – Selecting the right database technology (SQL vs. NoSQL) and caching layers (Redis, Memcached) for specific workloads.
• Advanced concepts – Service mesh implementations, multi-region failover strategies, and zero-downtime deployment methodologies.

Example scenarios:

• "Design a highly distributed, parallelized web scraping system that can fetch pricing data from hundreds of travel provider websites concurrently without getting blocked."
• "Architect a deployment pipeline and infrastructure setup that supports canary deployments with automatic rollback capabilities based on real-time Prometheus metrics."