A recent Expedia Data Analyst report describes two rounds: an online SQL, statistics, and A/B-testing assessment, followed by behavioral discussion and a bookings-data case focused on trends, seasonality, charts, and conclusions.
$93K
Avg. Base Comp
$121K
Avg. Total Comp
2 rounds
Typical Rounds
3-5 weeks
Process Length
A recent Expedia Data Analyst candidate reported a two-round interview process centered on practical analysis. The first round was an online assessment with SQL questions, simple statistics, and A/B testing. Practice writing clear queries and explaining what an experiment result means for a business decision. Check assumptions, make your logic easy to follow, and connect an output to the question it helps answer.
The reported second round combined behavioral discussion with a case using bookings data. The candidate was asked to identify trends over time, including seasonality, and share insights from the data. Rehearse how you would frame an analysis: establish the metric and time period, inspect overall movement, then distinguish recurring seasonal patterns from one-off changes. Explain conclusions in an ordered way so the interviewer can follow the path from evidence to recommendation.
The take-home portion could be completed in Excel or Python and was expected to include graphs and conclusions. Choose charts that make the trend readable, label them clearly, and state the takeaway each visual supports. For the behavioral discussion, prepare concise examples that show how you approached an analytical problem and communicated results to others. The reported experience points to SQL fluency, sound interpretation, and clear communication as useful preparation priorities.
Synthesized from 2 candidate reports by our editorial team.
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2 rounds- The first was an OA with SQL questions and simple stats questions with A/B testing. Then second round was Behavioural and case study where they gave bookings data and you had to share insight on it.
Questions asked: Asked what the trends were over time: seasonality trends. The take-home task could be done on excel or python but they expected you to make graphs and draw conclusions from the data.
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Topics based on recent interview experiences.
Featured question at Expedia, Inc.
How would you assess the validity of the result?
| Question | |
|---|---|
| Random SQL Sample | |
| Completed Shipments | |
| Bagging vs Boosting | |
| Revenue Retention | |
| Significance Time Series | |
| Google Maps Improvement | |
| Target Indices | |
| Xgboost vs Random Forest | |
| Hurdles In Data Projects | |
| Average Commute Time | |
| Lasso vs Ridge | |
| Forecasting New Year Revenue | |
| Count Transactions | |
| Average Ride Duration | |
| Banner Ad Strategy Success | |
| Implementing the Fibonacci Sequence in Three Different Methods | |
| Average Revenue per Customer | |
| Bias vs. Variance Tradeoff | |
| Data Preparation for Imbalanced Data | |
| Check Matching Parentheses | |
| Shoe Demand Seasonality | |
| String Palindromes | |
| Deciding Between Solutions | |
| Increase Search Ads | |
| Client Solution Pushback | |
| Best Performing Advertisers | |
| Boosting Instagram Stories | |
| Your Strengths and Weaknesses | |
| Evaluate News |
Synthesized from candidate reports. Individual experiences may vary.
A recent Data Analyst candidate reported an initial online assessment containing SQL questions and simple statistics questions involving A/B testing. Practice writing straightforward queries, checking your logic, and explaining how a basic experiment result should be interpreted in relation to the business question.
The same candidate reported behavioral discussion as part of the second round. Prepare concise examples from your own background that show how you approached an analytical problem, worked through uncertainty, and communicated findings or conclusions to other people.
In the reported second round, the candidate received bookings data and was asked to share insights, including trends over time and seasonality. The take-home could be completed in Excel or Python and expected graphs plus conclusions, so practice connecting each visual to a clear, evidence-based takeaway.
