11 eBay SQL Interview Questions (Updated 2024)

Updated on

January 26, 2024

At eBay, SQL is used all the damn time for analyzing customer behavior trends and managing product inventory databases. That's why eBay made an in-house SQL tool which has over 5,000 employee users. Because of this, it shouldn't surprise you that eBay often asks SQL interview questions during interviews for Data Analyst, Data Science, and BI jobs.

So, in case you're studying for a SQL Interview, here’s 11 eBay SQL interview questions to practice, which are similar to recently asked questions at eBay – able to answer them all?

11 eBay SQL Interview Questions

SQL Question 1: Analyze Average Ratings Over Time for Products

In this question, you are given a dataset of product reviews on eBay. Your task is to write a SQL query to calculate the average product ratings (stars) for each product on a monthly basis.

Example Input:

review_iduser_idsubmit_dateproduct_idstars
10011232022-05-20500014
10022652022-05-22698524
10033622022-05-25500012
10043622022-06-01500013
10059822022-06-03698521
10061922022-06-05500014
10073622022-07-10500015
10081232022-07-15698524
10099822022-07-20698523
10101922022-07-30500012

Example Output:

monthproduct_idavg_stars
5500013.00
5698524.00
6500013.50
6698521.00
7500013.50
7698523.50

The query uses the function to get the month from the date field and groups by both month and product ID to compute the average rating for each product per month.

To solve another question about calculating review ratings, try this Amazon SQL question within DataLemur's interactive SQL code editor:

SQL Question 2: Database Design for eBay Listing and Bidding System

The business case here is simulating the eBay listing and bidding system. We'd like to be able to keep track of users, the products they list for sale, and the bids that other users place on these products. Design the tables to model this system and write a SQL query to fetch the highest current bid for each active listing.

Let's start by identifying the tables we'll need:

2. - Information about the products that are listed for sale.
3. - Information about the bids made by users on the listed products.

1user1
2user2
3user3
Sample Input:
listing_iduser_idproduct_nameactive
11Prod1TRUE
21Prod2TRUE
32Prod3FALSE
43Prod4TRUE
Sample Input:
bid_idlisting_iduser_idamount
112200
213250
32350
431400
542300

Here is the SQL query to fetch the highest bid for each active listing:

This query joins the and table on the and filters out the listings that are not active. The function is used to find the highest bid for listings and they are grouped by the in order to display the highest bid per listing. The result set is ordered in descending order of the .

SQL Question 3: What is a self-join?

A self-join is a type of join in which a table is joined to itself. To perform a self-join, you need to specify the table name twice in the FROM clause, and give each instance of the table a different alias. You can then join the two instances of the table using a JOIN clause, and use a WHERE clause to specify the relationship between the rows.

Self-joins are the go-to technique for any data analysis that involves pairs of the same thing, like identifying pairs of products that are frequently purchased together like in this Walmart SQL interview question.

For another example, say you were doing an HR analytics project and needed to analyze how much all eBay employees in the same department interact with each other. Here's a self-join query you could use to retrieve all pairs of eBay employees who work in the same department:

This query returns all pairs of eBay employees who work in the same department, and excludes pairs where the employee's id is the same (since this would represent the same eBay employee being paired with themselves).

SQL Question 4: Filter and Analyze eBay Customer Activity

In eBay's customer database table 'activities', the 'activity_type' column distinguishes between different types of customer activities such as "VIEW", "BID", and "BUY". The goal of this question is to filter out the customers who have viewed a product after bidding on it, but have not yet purchased it. You need to write a query that returns the user_id, product_id and the timestamp of their last "VIEW" activity. Assume that the 'timestamp' column is of the type 'timestamp', and it records the exact time when the customer made the activity.

Example Input:
activity_iduser_idtimestampproduct_idactivity_type
61711232022-03-18 10:00:001001"VIEW"
78021232022-03-18 09:00:001001"BID"
63521232022-03-19 11:00:001001"VIEW"
45171232022-03-17 00:00:001001"BID"
Expected Output:
user_idproduct_idlast_view
12310012022-03-19 11:00:00

The SQL query for this problem would look like this:

This query first creates a subquery table consisting of "VIEW" activities, but filters out any activity from the customers who have also made a "BID" and have not made a "BUY" activity. Then it groups this table by customer and product, and selects the latest "VIEW" timestamp for each group.

To solve a related problem on DataLemur's free online SQL coding environment, try this Facebook SQL Interview question:

SQL Question 5: What's the purpose of the constraint?

A is a field in a table that references the of another table. It creates a link between the two tables and ensures that the data in the field is valid.

Say for example you had sales analytics data from eBay's CRM (customer-relationship management) tool.

In this example, the table has a foreign key field called that references the "account_id" field in the table (the primary key). This helps to link the data about each opportunity to the corresponding account information in the accounts table.

This makes sure the insertion of rows in the table that do not have corresponding entries in the table. It also helps to enforce the relationship between the two tables and ensures that data is not deleted from the table if there are still references to it in the table.

SQL Question 6: Average Bidding Price Per Category

Given the tables and , write a SQL query to calculate the average bidding price for each category of items on eBay. The table stores all the bids made for an item while the table contains details about the item including its category.

Example Input:
bid_iditem_iduser_idbid_pricebid_time
110112320.002022-10-01
210145625.002022-10-05
310212315.002022-10-01
410378930.002022-10-03
510478940.002022-10-04
Example Input:
item_idcategorydescriptionstart_pricestart_timeend_time
101Books"Harry Potter Book"15.002022-09-302022-10-07
102Toys"Lego Set"10.002022-10-012022-10-08
104Electronics"Smart TV"35.002022-10-032022-10-10

This SQL query first joins the table and the table on . Then, it groups the resulting table by the column of the table. The function is used to calculate the average for each category. The result of this query would be a table that lists each category with its average bidding price.

To practice another question about calculating rates, try this TikTok SQL Interview Question within DataLemur's interactive SQL code editor:

SQL Question 7: Can you describe the difference between a clustered and a non-clustered index?

Here's an example of a clustered index on the column of a table of eBay payments table:

This creates a clustered index on the column, which will determine the physical order of the data rows in the table.

Here is an example of a non-clustered index on the column of the same table:

This will create a non-clustered index on the column, which will not affect the physical order of the data rows in the table.

In terms of query performance, a clustered index is usually faster for searches that return a large number of records, while a non-clustered index is faster for searches that return a small number of records. However, updates to a clustered index are slower, as they require the data rows to be physically rearranged, while updates to a non-clustered index are faster, as they only require the index data structure to be updated.

SQL Question 8: Determining the Click-Through Conversion Rate for eBay

You're a data analyst at eBay and are tasked to calculate the click-through-conversion rate for the previous month. Click-through conversion rate is defined as the number of users that viewed a product and then added that product to their cart, divided by the total number of users that viewed the product.

To start, let's assume we have two tables: and , structured as follows.

Input:
user_idproduct_idview_date
101110106/18/2022 00:00:00
102110206/18/2022 00:00:00
103110106/19/2022 00:00:00
101110106/19/2022 00:00:00
104110307/22/2022 00:00:00
Input:
101110106/18/2022 00:00:00
103110106/19/2022 00:00:00
101110106/19/2022 00:00:00
104110307/23/2022 00:00:00

Since we need to calculate the click-through-conversion rate on a per product basis for the previous month, we can make use of sub-queries and the clause to get desired result.

Here is a PostgreSQL statement:

The rate is calculated as the number of distinct users who added the product to the cart () after a view, divided by the total number of users that viewed the product (). We use to also include the products that were viewed but not added to the cart.

SQL Question 9: Retrieving eBay Seller Information

As a part of the eBay data team, you are often asked to retrieve specific information from seller records. You are required to retrieve all the sellers from the city of 'San Francisco' and their sales records whose name starts with the letter 'A' or 'B'. For the purpose of this exercise, we are only considering the sellers table which has the columns: seller_id, seller_name, city and the sales table which consists of a record of the sales done by these sellers. The sales table has the columns: sale_id, seller_id, sale_date and amount.

Example Input:

seller_idseller_namecity
2001AmySan Francisco
4566BennySan Francisco
3167CharlieLos Angeles
9248AndyLos Angeles
8243BettySan Francisco

Example Input:

sale_idseller_idsale_dateamount
1022200102/01/2022120
3485456602/01/2022115
4761316702/01/2022180
9271924802/01/2022130
8251824302/01/2022110

Below is a PostgreSQL query to solve the problem statement:

This query first joins the sellers and sales tables together to get the combined record of sales by each seller. The WHERE clause then filters out the records where the city is 'San Francisco' and the seller's name begins with either 'A' or 'B'.

SQL Question 10: Give a few ways in SQL that you can identify duplicate records in a table?

One way to find duplicates is to use a clause and then use to find groups

You could also use the operator:

SQL Question 11: Find Total Sales and Average Sale Value Per Product

For this question, let's consider that eBay wants to find the total number of sales and the average sale value for each product. You will have to use both the and tables for this analysis.

Here are the and tables:

Example Input:
sale_idproduct_iduser_idsale_datesale_value
10017811012022-01-06120.0
10029102022022-02-2680.0
10035433032022-03-08250.0
10047811012022-04-16140.0
10059103032022-05-2895.0
Example Input:
product_idproduct_namesupplier_id
781Laptop5401
910Mobile Phone5402
543Television5403

Only consider sales for which sale_value is non-null.

Combine these two tables to provide your analysis.

Here is the PostgreSQL SQL query that can be used to solve this problem:

This SQL query first joins the and tables together based on their common field . The WHERE clause then excludes sales that have a null . Finally, it uses the GROUP BY statement with the aggregating functions COUNT and AVG to find the total sales and average sale value per product.

The result of this query would be a table where each row represents a product from the table, and the columns show the total number of sales and average sale value per product.

How To Prepare for the eBay SQL Interview

The best way to prepare for a SQL interview, besides making sure you have strong SQL fundamentals, is to practice a ton of real SQL questions that were asked in recent job interviews. Besides solving the earlier eBay SQL interview questions, you should also solve the 200+ SQL questions on DataLemur which come from companies like Facebook, Google and unicorn tech startups.

Each SQL question has multiple hints, full answers and most importantly, there's an interactive SQL code editor so you can instantly run your SQL query and have it checked.

To prep for the eBay SQL interview you can also be useful to solve interview questions from other tech companies like:

In case your SQL query skills are weak, don't worry about jumping right into solving questions – strengthen your SQL foundations with this free SQL for Data Analytics course.

This tutorial covers things like RANK vs. DENSE RANK and CASE/WHEN statements – both of these pop up often in eBay interviews.

eBay Data Science Interview Tips

What Do eBay Data Science Interviews Cover?

For the eBay Data Science Interview, besides SQL questions, the other types of questions to prepare for are:

• Statistics and Probability Questions
• Python or R Coding Questions
• Open-Ended Data Case Studies
• ML Modelling Questions
• Behavioral Interview Questions

How To Prepare for eBay Data Science Interviews?

To prepare for eBay Data Science interviews read the book Ace the Data Science Interview because it's got: