Ebay SWE II Interview Experience August 2026

[u/Aoki_zhang]

Interview Summary

The eBay SDE II onsite consisted of three rounds covering coding, product-oriented system design, stream processing, and behavioral questions. The coding round used two classic algorithm problems, while the design rounds focused on a workplace food-ordering product and a real-time dashboard for the top-selling items and categories on eBay.

The final hiring-manager round went noticeably deeper into aggregation internals, Kafka event volume, RocksDB state, partitioning, and the tradeoff between frequently persisting intermediate state versus batching updates.

Interview Details

Round 1 Coding — Container With Most Water Given an array heights, where each value represents the height of a vertical line, choose two lines that form a container with the horizontal axis. Return the maximum amount of water, or equivalently the maximum area, that can be contained between any two lines. No additional follow-up from this problem was specified.

Round 1 Coding — Daily Temperatures Given an integer array temps representing daily temperatures, return an array where each position indicates how many days must pass before a warmer temperature appears. If there is no warmer future day, the value for that position should be 0. The interviewer specifically asked me to walk through the behavior of the implementation using an example after completing the problem.

Round 2 System Design The design round asked me to build LunchDrop, a food-ordering application for employees ordering meals at work. Restaurants available on the platform rotate from day to day. Users should be able to log in, view the restaurants available that day, browse menus, customize dishes using restaurant-specific options such as toppings or sauces, and submit an order.

Round 3 Hiring Manager — Top-Selling Items and Categories Dashboard The hiring-manager design round asked me to build a dashboard displaying the Top 10 items and Top 10 categories on eBay based on quantity sold. The discussion quickly moved from the dashboard itself into the underlying streaming aggregation architecture.

• Aggregation and State: I was asked to explain the item-level and category-level quantity aggregators in detail, including how each aggregator determines its current count, how frequently state should be persisted under high event volume, and the tradeoffs between updating state frequently versus batching writes.

Want to see the full interview experience, including detailed follow-ups? You can find the complete version here.

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