Hopper Data Engineer Interview Questions + Guide in 2025

Hopper is an innovative travel technology company on a mission to become the world's best and most enjoyable platform for booking travel, leveraging vast amounts of data and advanced machine learning algorithms.

As a Data Engineer at Hopper, you will play a vital role in designing and building robust, high-quality data environments that provide fast access to critical data. Your responsibilities will include developing and maintaining data pipelines, ensuring data integrity, and enabling the Flights team to make informed, data-driven decisions. This role requires a deep understanding of data-intensive systems and a strong proficiency in coding, as you will be responsible for writing and deploying production code related to data storage and processing. You will work closely with product managers to align data strategies with customer needs and set measurable goals for data integrity.

The ideal candidate will have 3+ years of experience in building and operating high-volume, high-reliability data processing systems and a proven track record of creating scalable solutions. Familiarity with the Google Cloud Platform, ETL processes, and tools such as Airflow, BigQuery, and Terraform is essential. Strong candidates will also possess backend development experience with languages such as Python, Java, or Scala, along with a collaborative spirit to work across multiple teams to tackle complex, abstract problems.

This guide will help you prepare effectively for your interview at Hopper by providing insights into the skills and experiences that are most relevant to the Data Engineer role, as well as the types of competencies and problem-solving approaches they value.

The interview process for a Data Engineer position at Hopper is structured to assess both technical skills and cultural fit. It typically consists of several stages, each designed to evaluate different aspects of a candidate's qualifications and alignment with the company's values.

1. Initial Screening

The process begins with a phone screening, usually lasting around 30-45 minutes. During this call, a recruiter will discuss your background, experience, and motivations for applying to Hopper. Expect to answer questions about your previous work, particularly focusing on your experience with data engineering and relevant technologies. This is also an opportunity for you to learn more about Hopper's culture and the specifics of the role.

2. Technical Assessment

Following the initial screening, candidates are often required to complete a technical assessment, which may be conducted through platforms like HackerRank. This assessment typically lasts about 1-2 hours and includes coding challenges that test your proficiency in SQL, Python, and data structures. The problems may be related to real-world scenarios that Hopper encounters, such as data processing or pipeline construction.

3. Take-Home Assignment

Candidates who perform well in the technical assessment may be asked to complete a take-home assignment. This assignment is more extensive and can take several hours to complete. It often involves analyzing data, proposing solutions to specific problems, and demonstrating your ability to think critically about data-driven decisions. The assignment is designed to reflect the type of work you would be doing at Hopper, and it may require you to present your findings in a follow-up interview.

4. Technical Interviews

After the take-home assignment, candidates typically participate in one or more technical interviews. These interviews are conducted by senior engineers or team leads and focus on your technical skills, problem-solving abilities, and experience with data infrastructure. Expect to encounter questions related to data pipeline design, ETL processes, and the technologies mentioned in the job description, such as Google Cloud Platform, Hadoop, or Spark.

5. Behavioral Interview

In addition to technical assessments, there is usually a behavioral interview component. This interview assesses your cultural fit within the team and the company. You may be asked about your previous experiences working in teams, how you handle challenges, and your approach to collaboration. Be prepared to provide examples that demonstrate your alignment with Hopper's values and mission.

6. Final Interview

The final stage often includes a "bar raiser" interview, which is a more in-depth discussion with a senior leader or executive. This interview may cover a mix of technical and behavioral questions, focusing on your overall fit for the company and your potential contributions to the team.

As you prepare for your interview, consider the specific skills and experiences that will be relevant to the questions you may encounter. Next, let's delve into the types of questions that candidates have faced during the interview process.

Here are some tips to help you excel in your interview.

Understand the Company’s Challenges

Hopper is currently focused on improving its margins and enhancing its data-driven decision-making capabilities. Familiarize yourself with the company's recent challenges and think critically about how you can contribute to solving these issues. Prepare to discuss specific ideas or strategies that could help improve Hopper's profitability, especially in relation to their data infrastructure and product offerings.

Prepare for Technical Assessments

Expect a rigorous technical assessment process that includes coding challenges and take-home assignments. Brush up on your SQL, Python, and data pipeline design skills, as these are crucial for the role. Be ready to demonstrate your ability to build and operate high-volume data processing systems. Practice common data engineering problems, particularly those that involve data manipulation and ETL processes.

Showcase Your Problem-Solving Skills

During the interview, you may be asked to solve real-world problems related to Hopper's products. Approach these questions with a structured mindset. Use the STAR (Situation, Task, Action, Result) method to articulate your thought process clearly. Highlight your analytical skills and how you can leverage data to drive business decisions.

Be Ready for Behavioral Questions

Hopper values a strong sense of ownership and results-oriented mindset. Prepare for behavioral questions that assess your past experiences and how they align with Hopper's culture. Think of examples that demonstrate your ability to work autonomously, collaborate with cross-functional teams, and take initiative in challenging situations.

Engage with the Interviewers

The interview process may involve multiple rounds with different team members. Use this opportunity to engage with your interviewers by asking insightful questions about their work, the team dynamics, and Hopper's future direction. This not only shows your interest in the role but also helps you gauge if the company culture aligns with your values.

Be Cautious with Take-Home Assignments

While take-home assignments are a common part of the interview process, be mindful of the time and effort you invest. Some candidates have reported feeling that these assignments were more about gathering ideas than genuine hiring. If you choose to complete an assignment, ensure it reflects your best work and consider discussing compensation for your time if the assignment is extensive.

Follow Up Professionally

After your interviews, send a thank-you email to express your appreciation for the opportunity to interview. This is a chance to reiterate your interest in the role and briefly mention any key points you may want to clarify or expand upon from your discussions.

By preparing thoroughly and approaching the interview with confidence and curiosity, you can position yourself as a strong candidate for the Data Engineer role at Hopper. Good luck!

In this section, we’ll review the various interview questions that might be asked during a Data Engineer interview at Hopper. The interview process will likely focus on your technical skills, problem-solving abilities, and understanding of data infrastructure and pipelines. Be prepared to discuss your experience with data-intensive systems, coding, and how you can contribute to Hopper's data-driven culture.

Technical Skills

1. Can you describe your experience with building data pipelines?

This question aims to assess your hands-on experience in creating data pipelines and your understanding of the processes involved.

How to Answer

Discuss specific projects where you built data pipelines, the technologies you used, and the challenges you faced. Highlight your role in ensuring data integrity and accessibility.

Example

“In my previous role, I designed and implemented a data pipeline using Apache Airflow and Google Cloud Dataflow. This pipeline processed large volumes of data daily, ensuring that our analytics team had access to real-time insights. I faced challenges with data quality, which I addressed by implementing validation checks at each stage of the pipeline.”

2. What tools and technologies have you used for ETL processes?

This question evaluates your familiarity with ETL tools and your ability to work with data transformation processes.

How to Answer

Mention specific ETL tools you have experience with, such as Apache Airflow, Talend, or Google Cloud Dataflow. Provide examples of how you used these tools in your projects.

Example

“I have extensive experience with Apache Airflow for orchestrating ETL workflows. In my last project, I set up a series of DAGs to automate data extraction from various sources, transform it using Python scripts, and load it into our data warehouse in BigQuery.”

3. How do you ensure data quality and integrity in your projects?

This question assesses your understanding of data governance and quality assurance practices.

How to Answer

Discuss the methods you use to validate data, monitor data quality, and implement best practices for data management.

Example

“I ensure data quality by implementing automated tests that validate data at each stage of the ETL process. I also set up monitoring alerts to notify the team of any anomalies in data patterns, allowing us to address issues proactively.”

4. Describe a challenging data problem you faced and how you solved it.

This question is designed to evaluate your problem-solving skills and ability to handle complex data issues.

How to Answer

Provide a specific example of a data challenge, the steps you took to resolve it, and the outcome of your solution.

Example

“While working on a project, we encountered performance issues with our data processing pipeline due to high data volume. I analyzed the bottlenecks and optimized our queries, implemented partitioning in our data warehouse, and adjusted our ETL schedule to off-peak hours, which improved processing time by 50%.”

Behavioral Questions

5. How do you prioritize tasks when working on multiple projects?

This question assesses your time management and organizational skills.

How to Answer

Explain your approach to prioritizing tasks, including any frameworks or tools you use to manage your workload.

Example

“I prioritize tasks based on their impact on project deadlines and business goals. I use project management tools like Jira to track progress and ensure that I’m focusing on high-priority tasks that align with our team’s objectives.”

6. Can you give an example of how you collaborated with cross-functional teams?

This question evaluates your teamwork and communication skills.

How to Answer

Share a specific instance where you worked with other teams, highlighting your role and the outcome of the collaboration.

Example

“In my last role, I collaborated with the product and analytics teams to define data requirements for a new feature. I facilitated meetings to gather input, ensuring that our data infrastructure could support their needs. This collaboration resulted in a successful feature launch that improved user engagement by 20%.”

Problem-Solving and Analytical Skills

7. How would you approach designing a data model for a new product feature?

This question tests your analytical thinking and understanding of data modeling.

How to Answer

Outline your approach to data modeling, including the steps you would take to gather requirements and design the model.

Example

“I would start by gathering requirements from stakeholders to understand the data needs for the new feature. Then, I would create an entity-relationship diagram to visualize the data relationships and ensure normalization. Finally, I would implement the model in our data warehouse and validate it with sample data.”

8. What strategies do you use for optimizing SQL queries?

This question assesses your SQL skills and understanding of performance optimization.

How to Answer

Discuss specific techniques you use to optimize SQL queries, such as indexing, query restructuring, or using appropriate data types.

Example

“I optimize SQL queries by analyzing execution plans to identify bottlenecks. I often use indexing on frequently queried columns and rewrite complex joins to reduce the number of records processed. This approach has significantly improved query performance in my previous projects.”

9. How do you stay updated with the latest trends and technologies in data engineering?

This question evaluates your commitment to continuous learning and professional development.

How to Answer

Mention the resources you use to stay informed, such as online courses, blogs, or industry conferences.

Example

“I stay updated by following industry blogs like Towards Data Science and attending webinars on data engineering topics. I also participate in online courses on platforms like Coursera to learn about new tools and technologies that can enhance my skill set.”

10. Can you explain a time when you had to learn a new technology quickly?

This question assesses your adaptability and willingness to learn.

How to Answer

Provide an example of a situation where you had to quickly learn a new technology and how you applied it.

Example

“When our team decided to migrate to Google Cloud Platform, I had to quickly learn about BigQuery and Dataflow. I dedicated time to online tutorials and hands-on practice, which allowed me to contribute to the migration project successfully within a few weeks.”