Real interview questions asked at Expedia. Practice the most frequently asked questions and land your next role.
Expedia data engineering interviews test your ability across multiple domains. These questions are sourced from real Expedia interview experiences and sorted by frequency. Practice the ones that matter most.
How do you handle disagreements within a team?
Explain the trade-offs between batch and real-time data processing. Provide examples of when each is appropriate.
How do you manage tight project delivery timelines in a team environment?
How do you work under tight deadlines and high pressure?
Why did you leave your last company in just 4 months?
Calculate the average session duration per user for the Expedia website.
Given exchange rates for USD to INR with timestamps: Find the ticket price in rupees for various dates. Use the latest exchange rate based on the timestamp for each date.
How do you manage failed ideas?
Share experiences of quickly adapting to new technologies and tools in evolving project requirements.
Share good and bad experiences with past employers.
What are your expectations from the next job role?
Design a solution to generate unique device names from a list of IoT devices.
Extended the solution to determine the nth largest element in an array.
Given the Infix, Prefix, or Postfix notation of an expression, write the code to compute the final result.
Implement an algorithm to find the longest ordered subsequence of vowels in a given string.
Solve for the Kth smallest element in a Binary Search Tree.
Write code to calculate the power of a given number in minimum time complexity.
Consolidate hotel reviews and create a dashboard. Design a data model for the reviews.
Explain offset management, Sync vs. Async commits, partition assignment strategies and Consumer groups, and handling backpressure in Kafka streams.
Replace each node's value with the next greater value in the list. Addressed edge cases where no greater element exists.
Review Kafka fundamentals and concepts.
Write queries combining Joins and Group By operations.
Conceptualize and design a real-time streaming data pipeline end-to-end.
Explain Apache Spark fundamentals, OOM scenarios and their resolutions, optimization techniques, strategies for optimized joins, and handling data skewness with Key Salting techniques.
How can Docker be used to scale streaming data applications?
Build a banking system architecture from scratch, highlighting critical workflows, scalability, and data management strategies.
Demonstrate system design principles applied to BI solutions.
Design a project architecture visually and explain key components.
Discuss the deployment process for real-time applications using CI/CD pipelines.
Explain project architecture, technical contributions, and value delivered.
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