Expedia Interview Questions — Page 1

Expedia·Machine Learning Engineer·Onsite - System Design / Architecture

Apr 2026

System design round at Expedia for an ML Engineer role, focused almost entirely on building a large-scale event pipeline end to end. It went deep fast and covered a lot of ground.

Design a data pipeline that ingests events from multiple upstream producers, transforms them, and delivers them to analytics-ready storage. Walk through your full architecture.
How would you handle schema evolution in a high-throughput streaming pipeline without breaking downstream consumers?
Walk me through the tradeoffs between stream processing (e.g. Flink) and micro-batch approaches (e.g. Spark Streaming) for this kind of pipeline.
How would you design for backfills when your pipeline has already been running in production for months?
How do you ensure data quality and handle bad or malformed events at scale?
How would you approach observability and lineage for a pipeline like this, and what does a meaningful SLA look like?
What's your strategy for managing cost and scaling as event volume grows by an order of magnitude?

“This was the main question and it basically ate the whole session.”

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Expedia·Software Engineer·Onsite - System Design / Architecture

Apr 2026

Expedia system design round, one big open-ended question about building a middleware connectivity layer for an OTA platform. The scope was wider than I expected and I spent probably too long on the API spec before realizing they also wanted a scaling discussion.

Design a 'Connectivity' service for an OTA-style platform that receives listing-creation requests from external providers, forwards them to an internal validation domain, and returns results back to the provider. Deliverables include a full REST API spec with payloads and status codes, a request lifecycle flowchart, and a scaling strategy.

“This was a lot.”

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Expedia·Machine Learning Engineer·Technical Phone Screen

Apr 2026

One round at Expedia for an ML Engineer role, basically just a walkthrough of my own projects. Pretty low-key as far as technical screens go.

Walk me through a project you've worked on recently. What was your role and what technical decisions did you make?

“They just wanted me to talk through my own work, which sounds easy until you're actually doing it live and second-guessing every detail you mention.”

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Expedia·Product Manager·Onsite - Product Sense / Strategy

Apr 2026

PM interview at Expedia with a classic market sizing question about global hotel bookings. Pretty standard estimation exercise but the scope of it tripped me up a bit.

How many hotel bookings does Expedia handle globally in a year? Walk through your estimate.

“I started from world population and tried to segment by travelers vs non-travelers, then layered in online booking penetration and Expedia's market share.”

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Expedia·Software Engineer·Onsite - Behavioral / Leadership

Apr 2026

Behavioral round at Expedia for a software engineer role. Two questions, both pretty standard but the second one had some meat to it if you came prepared with a real story.

Walk me through how you pick up new technologies or knowledge quickly. Give a specific example.
Tell me about the most challenging project you've worked on. What was your role, what constraints did you face, and what was the outcome? Also, how did you measure success and what would you do differently?

“I had a decent example ready but I rambled a bit trying to make it sound more impressive than it was.”

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Expedia·Machine Learning Engineer·Technical Phone Screen

Apr 2026

ML system design round at Expedia where you pick a product, find the ML-worthy problems in it, and then basically walk through the entire lifecycle from problem framing to deployment. Pretty open-ended, which I was not fully prepared for.

Given a specific product at Expedia, how would you identify which problems within it are actually good candidates for an ML solution?
How would you define the ML task for this product, including inputs, outputs, and how you'd source or construct labels?
What model class and feature signals would you choose, and why?
Walk me through how you'd design the training and evaluation pipeline for this system.
How would you decide on online versus offline metrics, and how would you set up an A/B test to validate the model in production?
What deployment trade-offs would you consider when taking this model to production?

“I jumped straight into listing ML techniques before really justifying why ML was even needed, which felt backwards in hindsight.”

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Expedia·Data Scientist·Technical Phone Screen

Apr 2026

Expedia DS interview for their Search team, focused entirely on a hotel-ranking model case. The whole thing was one long multi-part question that spiraled through KPIs, experimentation, attribution, and profit estimation. Pretty intense, no fluff.

Define a single primary KPI for a hotel-ranking model serving enterprise clients who care about profitable bookings, and list at least three guardrail metrics with specific thresholds.
How would you validate that offline proxy metrics like margin-weighted NDCG correlate with your online KPI? Describe the analysis design, acceptable correlation ranges, and what you'd do if they're misaligned.
Design an A/B test for this ranking model: randomization unit, traffic split, ramp strategy, MDE and sample size calculation, pre-experiment checks, and variance reduction techniques.
How would you detect and prevent metric gaming and novelty effects in this ranking experiment, and what would your rollback criterion be?
Bookings are flat but cancellations rise 15% in the treatment group. Do you ship or rollback? Justify using expected profit impact and confidence intervals.
Estimate the incremental gross profit per search with a 95% confidence interval, and explain how you'd separate true incrementality from attribution.
For paid channels, propose a multi-touch attribution approach and explain how you'd calibrate it against holdout or geo-based tests.

“I went with something like margin-adjusted bookings per search over a 7-day window, which felt right for the client value angle.”

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Expedia·Software Engineer·Onsite - System Design / Architecture

Apr 2026

Expedia system design round focused entirely on building an employee management backend, specifically the org hierarchy parts. Pretty deep dive, they really wanted to see you sweat the storage tradeoffs.

Design a backend service that supports full CRUD operations on employee records and can query an employee's full reporting chain up to the CEO, as well as their direct and indirect reports.
How would you model and store the organizational hierarchy? Walk through the tradeoffs between an adjacency list, closure table, and path enumeration.
How do you handle concurrency when multiple admins are simultaneously updating the same part of the org tree?
What are the read/write tradeoffs at scale for this kind of hierarchical data service?

“I started with the API surface and that was probably the right call since it grounded everything.”

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Expedia·Data Scientist·Technical Phone Screen

May 2026

Technical screen for a DS role at Expedia that went deep into data quality and pipeline validation for hotel search data. The question was essentially one long case study, and it covered a lot of ground fast.

You've inherited an unfamiliar hotel search dataset with almost no documentation. Walk through how you'd systematically explore the schema, verify keys, and understand what tables actually represent.
How would you profile distributions, missingness, and issues around timezones, locale, and currency across a travel dataset?
How do you detect duplicate records and join inflation when linking searches, impressions, clicks, bookings, and cancellations?
How would you validate that events follow the correct sequence (search to impression to click to booking to cancellation), and how do you handle late-arriving data?
Walk through how you'd correctly compute metrics like bookings per search within a 7-day window, margin as price minus cost, and the difference between GMV and contribution margin.
How would you write data quality tests and data contracts covering nullability, uniqueness, referential integrity, and numeric range checks for this kind of pipeline?
What are specific pitfalls in travel data around multi-room bookings, partial cancellations, rebookings, cross-currency FX timing, and adult vs. child guest counts, and how would you detect each?

“Started with information_schema and just ran describe on everything, which felt obvious but they seemed fine with it.”

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Expedia·Software Engineer·Onsite - Coding / Algorithms

May 2026

Expedia coding round for a software engineer role. One algorithmic question, sliding window style, the kind that looks manageable until you start thinking about edge cases and then you're suddenly spiraling.

Given a lowercase string, count all contiguous substrings that contain only vowels (a, e, i, o, u) and include each of the five vowels at least once. Design an O(n) solution using a sliding window or two-pointer approach with constant extra space, and explain how you handle consonants, repeated vowels, and long vowel runs.

“My first instinct was to reach for a frequency map and just slide a window, which is the right skeleton, but I kept fumbling the part where a consonant resets everything.”

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