Expedia, Inc. Data Engineer Interview Questions + Guide in 2025

Expedia, Inc. is a leading technology company that powers global travel for everyone, everywhere, by designing innovative solutions that enhance the travel experience.

As a Data Engineer at Expedia, your primary responsibility will be to develop and optimize data pipelines and systems that enable efficient data capture, processing, storage, and distribution. You will play a crucial role in supporting various business units, particularly within the marketing domain, by translating complex marketing concepts into functional specifications and ensuring the seamless flow of data across the organization. This role requires a strong technical background in programming languages such as Python and SQL, as well as expertise in cloud technologies, particularly AWS. You’ll be expected to design and implement scalable and highly-performant distributed systems, contributing to the continuous improvement of data architecture and system performance. Your ability to collaborate with cross-functional teams, along with a proactive mindset to identify areas for improvement, will be essential in driving operational efficiencies.

To excel in this role, you should possess a growth mindset, a passion for problem-solving, and a desire to mentor junior engineers. The ideal candidate will have extensive experience in building data pipelines, understanding data modeling and schema design, and applying best practices in software development. Being part of a diverse and inclusive culture at Expedia means that your unique perspectives will be valued as you work towards creating groundbreaking solutions that empower travelers around the globe.

This guide will provide you with tailored insights and interview preparation strategies to help you stand out in your application for the Data Engineer role at Expedia, ensuring you are well-equipped to showcase your skills and experience effectively.

The interview process for a Data Engineer at Expedia is structured to assess both technical and behavioral competencies, ensuring candidates are well-rounded and fit for the role. The process typically unfolds in several key stages:

1. Initial Screening

The first step involves a brief phone interview with a recruiter. This conversation is designed to gauge your interest in the position and the company, as well as to discuss your background, skills, and career aspirations. The recruiter will also provide insights into the company culture and the specifics of the Data Engineer role.

2. Technical Assessment

Following the initial screening, candidates undergo a technical assessment, which may include a coding challenge focused on Python and SQL. This assessment is typically conducted online and tests your ability to solve intermediate-level problems, including data manipulation and optimization tasks. You may also be asked to demonstrate your understanding of data structures and algorithms.

3. Hiring Manager Interview

After successfully completing the technical assessment, candidates will have a one-on-one interview with the hiring manager. This discussion often revolves around your previous experiences, technical skills, and how you approach problem-solving. Expect questions that explore your familiarity with data engineering principles, cloud technologies (especially AWS), and your ability to work collaboratively within a team.

4. Onsite Interview Loop

The final stage consists of an onsite interview loop, which typically includes multiple rounds with different team members. This may involve: - Technical Rounds: These sessions focus on system design, coding challenges, and real-world problem-solving scenarios. You may be asked to design data pipelines or discuss your approach to building scalable data solutions. - Behavioral Round: This interview assesses your soft skills, teamwork, and cultural fit within the organization. Expect questions that explore how you handle challenges, work with others, and contribute to a positive team environment. - Case Study Presentation: In some instances, candidates may be asked to present a case study or a project they have worked on, demonstrating their technical expertise and communication skills.

Throughout the process, candidates should be prepared for a mix of technical and behavioral questions, as well as practical coding exercises that reflect the day-to-day responsibilities of a Data Engineer at Expedia.

Next, let’s delve into the specific interview questions that candidates have encountered during this process.

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

Understand the Interview Structure

The interview process at Expedia typically includes multiple rounds, starting with a technical coding assessment followed by interviews with hiring managers and team members. Familiarize yourself with the structure, which often consists of system design discussions, coding challenges, and behavioral questions. Knowing what to expect can help you manage your time and responses effectively.

Prepare for Technical Assessments

Given the emphasis on technical skills, particularly in Python and SQL, ensure you are well-versed in these areas. Practice coding problems that reflect intermediate to advanced levels, focusing on data manipulation, optimization, and ETL processes. Be ready to explain your thought process and dry-run your solutions, as interviewers may ask you to walk through your code.

Emphasize System Design Knowledge

Expect questions related to system design, especially low-level design concepts. Brush up on object-oriented programming principles and be prepared to discuss how you would design data pipelines or systems that are scalable and resilient. Demonstrating a solid understanding of architecture and design patterns will set you apart.

Showcase Your Problem-Solving Skills

Expedia values candidates who can tackle complex problems. Be prepared to discuss past experiences where you identified inefficiencies or proposed innovative solutions. Use the STAR (Situation, Task, Action, Result) method to structure your responses, highlighting your analytical skills and ability to work collaboratively with cross-functional teams.

Communicate Clearly and Confidently

Effective communication is crucial, especially when discussing technical concepts. Practice articulating your thoughts clearly and concisely. Be mindful of your audience; tailor your explanations to ensure they are understandable, regardless of the interviewer's technical background. This will demonstrate your ability to bridge the gap between technical and non-technical stakeholders.

Be Ready for Behavioral Questions

Expect behavioral questions that assess your fit within Expedia's culture. Reflect on your past experiences and how they align with the company's values of collaboration, inclusivity, and innovation. Prepare examples that showcase your teamwork, adaptability, and commitment to continuous learning.

Follow Up on Feedback

Many candidates have expressed disappointment over the lack of feedback post-interview. To stand out, consider politely following up with your interviewers or recruiters for feedback, regardless of the outcome. This shows your commitment to personal growth and can leave a positive impression.

Embrace the Company Culture

Expedia prides itself on a diverse and inclusive work environment. Familiarize yourself with their values and be prepared to discuss how you can contribute to this culture. Show enthusiasm for the travel industry and how your skills can help enhance the customer experience.

By following these tailored tips, you can approach your interview with confidence and a clear strategy, increasing your chances of success at Expedia. Good luck!

In this section, we’ll review the various interview questions that might be asked during a Data Engineer interview at Expedia, Inc. The interview process will likely assess your technical skills in data engineering, your understanding of cloud technologies, and your ability to work collaboratively within a team. Be prepared to demonstrate your problem-solving abilities and your experience with data systems and programming languages.

Technical Skills

1. Can you explain the ETL process and how you have implemented it in your previous projects?

Understanding the ETL (Extract, Transform, Load) process is crucial for a Data Engineer, as it is fundamental to data management and integration.

How to Answer

Discuss your experience with ETL processes, including the tools you used, the challenges you faced, and how you overcame them. Highlight specific projects where you successfully implemented ETL.

Example

“In my previous role, I developed an ETL pipeline using Apache Airflow to extract data from various APIs, transform it using Python scripts, and load it into a PostgreSQL database. This process improved data accuracy and reduced processing time by 30%.”

2. What are some best practices for optimizing SQL queries?

Optimizing SQL queries is essential for improving performance and efficiency in data retrieval.

How to Answer

Mention specific techniques you use to optimize SQL queries, such as indexing, query restructuring, and analyzing execution plans. Provide examples of how these practices improved performance in your past work.

Example

“I often use indexing on frequently queried columns and analyze execution plans to identify bottlenecks. For instance, I optimized a slow-running report by restructuring the query and adding indexes, which reduced the execution time from 10 seconds to under 1 second.”

3. Describe your experience with cloud technologies, particularly AWS.

As a Data Engineer at Expedia, familiarity with cloud technologies is vital for building scalable data solutions.

How to Answer

Discuss your experience with AWS services relevant to data engineering, such as S3, Redshift, and Lambda. Provide examples of how you have utilized these services in your projects.

Example

“I have extensive experience with AWS, particularly with S3 for data storage and Redshift for data warehousing. In a recent project, I used S3 to store raw data and set up a Redshift cluster to perform analytics, which allowed our team to generate insights in real-time.”

4. How do you ensure data quality and integrity in your data pipelines?

Data quality is critical for making informed business decisions, and as a Data Engineer, you need to ensure that the data is accurate and reliable.

How to Answer

Explain the methods you use to validate and clean data, such as implementing data quality checks, monitoring data flows, and using automated testing.

Example

“I implement data validation checks at each stage of the ETL process to ensure data integrity. For example, I use assertions to verify that the data types and ranges are correct before loading data into the warehouse, which has significantly reduced errors in our reports.”

Programming and System Design

5. Can you describe a challenging coding problem you faced and how you solved it?

This question assesses your problem-solving skills and coding proficiency.

How to Answer

Choose a specific coding challenge, explain the context, the approach you took to solve it, and the outcome.

Example

“I faced a challenge when I had to process a large dataset that exceeded memory limits. I implemented a solution using Apache Spark to distribute the processing across multiple nodes, which allowed us to handle the data efficiently and reduced processing time by 50%.”

6. What is your experience with distributed data processing frameworks like Spark?

Experience with distributed frameworks is essential for handling large datasets effectively.

How to Answer

Discuss your familiarity with Spark, including specific projects where you used it, and the benefits it provided.

Example

“I have worked with Spark extensively for batch processing. In one project, I used Spark to process terabytes of data from various sources, which allowed us to perform complex transformations and aggregations quickly, resulting in timely insights for our marketing team.”

7. How do you approach system design for a new data pipeline?

This question evaluates your ability to design scalable and efficient data systems.

How to Answer

Outline your approach to system design, including requirements gathering, technology selection, and scalability considerations.

Example

“When designing a new data pipeline, I start by gathering requirements from stakeholders to understand their needs. I then select appropriate technologies, such as AWS for cloud storage and Spark for processing, and ensure the design is scalable by using modular components that can be easily updated as requirements change.”

Behavioral Questions

8. Describe a time when you had to collaborate with cross-functional teams. How did you ensure effective communication?

Collaboration is key in a Data Engineer role, and effective communication is essential for project success.

How to Answer

Share an example of a project where you worked with different teams, highlighting how you facilitated communication and collaboration.

Example

“In a recent project, I collaborated with the marketing and analytics teams to develop a new reporting tool. I scheduled regular check-ins to discuss progress and gather feedback, which helped align our goals and ensured that the final product met everyone’s needs.”

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

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

How to Answer

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

Example

“I regularly attend data engineering meetups and webinars, and I follow industry leaders on platforms like LinkedIn. I also take online courses to learn about new tools and technologies, which helps me stay current and apply best practices in my work.”

10. What motivates you to work in data engineering?

Understanding your motivation can help interviewers gauge your fit within the company culture.

How to Answer

Share your passion for data and how it drives your work, as well as your desire to contribute to impactful projects.

Example

“I am passionate about data because it has the power to drive decision-making and improve business outcomes. I enjoy solving complex problems and finding innovative ways to leverage data to create value for the organization.”