At Global-e, SQL does the heavy lifting for analyzing e-commerce transaction data and predicting customer buying trends. So, it shouldn't surprise you that Global-e almost always asks SQL questions during interviews for Data Analyst, Data Science, and BI jobs.
As such, to help you practice for the Global-e SQL interview, we've collected 11 Global-e SQL interview questions – can you answer each one?
Global-e is an ecommerce company and they are interested in analyzing their orders data. Write a SQL query to calculate the Total, Average and Max order amount per country per month. Also, rank the countries by the total order amount for each month.
Here's your sample data:
order_id | user_id | order_date | country | order_amount |
---|---|---|---|---|
10001 | 1 | 2022-01-10 | USA | 120.00 |
10002 | 2 | 2022-01-20 | UK | 200.00 |
10003 | 3 | 2022-01-25 | DE | 80.00 |
10004 | 4 | 2022-01-28 | USA | 150.00 |
10005 | 5 | 2022-02-01 | UK | 250.00 |
10006 | 6 | 2022-02-05 | USA | 300.00 |
10007 | 7 | 2022-02-25 | DE | 240.00 |
So, you need to generate below result:
month | country | total | average | max | rank |
---|---|---|---|---|---|
1 | USA | 270.00 | 135.00 | 150.00 | 1 |
1 | UK | 200.00 | 200.00 | 200.00 | 2 |
1 | DE | 80.00 | 80.00 | 80.00 | 3 |
2 | UK | 250.00 | 250.00 | 250.00 | 1 |
2 | USA | 300.00 | 300.00 | 300.00 | 2 |
2 | DE | 240.00 | 240.00 | 240.00 | 3 |
With this query, you are using windowing functions to aggregate order amount by month and country. You are also ranking the countries by total order amount for each month. The RANK() function is being used to provide a ranking of the countries by total order amount within each month. The ORDER BY clause is ordering the results by month and rank.
To solve a related window function SQL problem on DataLemur's free online SQL code editor, try this Amazon SQL Interview Question:
Global-e is an e-commerce company which operates internationally. They need to better understand their sales patterns based on geographical location. Your task is to create a database design to keep track of their sales and then write a SQL query that will retrieve the total sales amount for each country for the last 12 months.
Consider the following tables: table which stores records of each sale where is the primary key, denotes which product was sold, is the customer who made the purchase, is the price at which the product was sold, and indicates when the sale was made.
table captures user details. is the key and describes the user's geographical location.
sale_id | product_id | user_id | amount | sale_date |
---|---|---|---|---|
2345 | 001 | 129 | 2000 | 12/02/2020 |
1682 | 027 | 483 | 1500 | 13/12/2020 |
9843 | 053 | 226 | 2500 | 20/01/2021 |
7293 | 001 | 847 | 2300 | 10/08/2021 |
3842 | 027 | 483 | 1750 | 12/10/2021 |
user_id | country |
---|---|
129 | USA |
483 | UK |
226 | Canada |
847 | Germany |
Question: Write a SQL query that retrieves the total sales amount for each country for the last 12 months.
This query first joins the and tables based on the . It then filters out the sales records that are older than a year. The remaining records are grouped by country and the total sales amount is summed up for each group.
A is a column or set of columns in a table that references the primary key of another table. It is used to link the two tables together, and to ensure that the data in the foreign key column is valid.
The constraint helps to ensure the integrity of the data in the database by preventing the insertion of rows in the foreign key table that do not have corresponding entries in the primary key table. It also helps to enforce the relationship between the two tables, and can be used to ensure that data is not deleted from the primary key table if there are still references to it in the foreign key table.
For example, if you have a table of Global-e customers and an orders table, the customer_id column in the orders table could be a that references the id column (which is the primary key) in the Global-e customers table.
As a data analyst in the Global-e marketing department, you have been asked to identify customers that are eligible for a new promotional offer. The offer is available only to customers who made their last purchase more than 30 days ago and have total spending greater than $1000. Write a SQL query to identify these customers.
Here is the sample data for customer and purchase tables:
customer_id | first_name | last_name |
---|---|---|
101 | John | Doe |
256 | Jane | Roe |
365 | Jim | Poe |
489 | Jack | Coe |
555 | June | Moe |
purchase_id | customer_id | purchase_date | amount |
---|---|---|---|
911 | 101 | 06/08/2022 00:00:00 | 350 |
9876 | 256 | 06/10/2022 00:00:00 | 1750 |
1358 | 365 | 07/01/2022 00:00:00 | 600 |
7843 | 489 | 07/26/2022 00:00:00 | 1200 |
4317 | 555 | 06/30/2022 00:00:00 | 900 |
This query first groups the purchases table by customer_id to find the last purchase date and total amount spent for each customer. Then it joins this result with the customers table to get the customer details. The WHERE clause puts the conditions that the last purchase made by the customer should be more than 30 days ago and the total amount spent should be over $1000.
The UNIQUE constraint makes sure that all values in a column are distinct. It is often paired with other constraints, like NOT NULL, to ensure that the data follows certain rules.
For example, say you were an analyst on the marketing team at Global-e, and had access to a database on marketing campaigns:
In this example, the UNIQUE constraint is applied to the "campaign_name" field to ensure that each campaign has a unique name. This helps to ensure the integrity of the data in the table and prevents errors that could occur if two campaigns had the same name.
As an analyst for Global-e, a company specializing in international e-commerce solutions, you are asked to gain insights about the performance of your business across different countries. Please write a PostgreSQL query to find the average total sales (in USD) per country for each month based on the transactions in the second quarter (April, May, June) of 2022.
txn_id | date | country | amount_usd |
---|---|---|---|
101 | 04/01/2022 00:00:00 | USA | 150 |
102 | 04/02/2022 00:00:00 | USA | 200 |
103 | 04/03/2022 00:00:00 | France | 100 |
104 | 05/01/2022 00:00:00 | France | 200 |
105 | 05/02/2022 00:00:00 | UK | 150 |
106 | 06/03/2022 00:00:00 | UK | 100 |
month | country | average_sales |
---|---|---|
4 | USA | 175 |
4 | France | 100 |
5 | France | 200 |
5 | UK | 150 |
6 | UK | 100 |
This query first filters the transactions to only include those from the second quarter of 2022. It then groups these transactions by month and country. For each group, it calculates the average total sales in USD, resulting in the average total sales per country for each month. Finally, the results are ordered by month and country.
"The clause in SQL allows you to select records that are unique, eliminating duplicates.
For a tangible example, say you had a table of Global-e employees:
first_name | job_title |
---|---|
Akash | Data Analyst |
Brittany | Data Scientist |
Carlos | Data Engineer |
Diego | Data Engineer |
Eva | Data Analyst |
If you were doing an HR Analytics project and you wanted to get all the unique job titles that currently worked at the company, you would write the following SQL query:
The output would give you 3 distinct job titles at Global-e:
job_title |
---|
Data Analyst |
Data Scientist |
Data Engineer |
As an e-commerce enabler, Global-e helps brands and retailers make sales in international markets. Let's say that your data team stores all this order information in a table named . Each order has a unique , contains multiple items represented as , and contains the total cost incurred for each item denoted as . The helps identify a specific customer, represented by a unique number and the column identifies the country where the order is shipped.
The task for this SQL question is to calculate the average total sales (in USD) per country.
An example orders table could look like:
Example Input:
order_id | customer_id | country | item_count | total_cost |
---|---|---|---|---|
1 | 001 | USA | 3 | 150 |
2 | 002 | UK | 2 | 120 |
3 | 003 | Canada | 1 | 70 |
4 | 004 | USA | 2 | 130 |
5 | 005 | Canada | 4 | 220 |
The expected output should display the average for each . The output of your query should look something like this:
country | average_sales |
---|---|
USA | 140 |
UK | 120 |
Canada | 145 |
The PostgreSQL query to solve this problem is:
The query first groups the records in the table by . The function then calculates the average for each group, effectively giving us the average total sales per country. Note that because the clause is used before the function, the average is calculated for each group (i.e., for each country), rather than across all records in the table.
Given two tables, , and , write a SQL query to calculate and sort the total purchase amount by country.
This SQL query first performs a JOIN operation on the and tables on the column of . After joining these two tables together, we aggregate the data by country and calculate the sum of purchase amounts for each country. The results are finally ordered in descending order based on the total purchase amount.
Because joins come up frequently during SQL interviews, practice this interactive Snapchat SQL Interview question using JOINS:
A cross-join, also referred to as a cartesian join, is a type of JOIN that returns the cross-product of two tables. In a cross-join, each row from the first table is paired with every row from the second table, resulting in a new table with a row for each possible combination of rows from the two input tables.
For example, say you worked on the Marketing Analytics team at Global-e, and were tasked to understand what advertising copy (text) goes best with what advertising creative (the photo that gets used along with the text copy).
Here is an example of a cross-join between a table of ad_copy and a table of ad_creative:
You could this query to generate all possible combinations of ad copy and ad creative to help you create more effective ads for Global-e. Just be careful: if you had 1,000 rows of ad copy and 1,000 different image creatives, the resulting cross-join would have 1 million rows! As a result, it's important to use cross-joins judiciously, and make sure that you're not generating more data than you need to.
As the new Data Analyst for Global-e, your first task is to evaluate the performance metrics of each product. Given a table containing product, units sold, and revenue data for each country, along with a table specifying each product's cost and price, you need to calculate the following for each product:
Remember to round these calculations to two decimal places.
To help you solve the problem, you have been provided with the following sample data:
Given the tables, the base SQL query to calculate these metrics would be as follows:
This query first joins the and tables on the field. It then groups the results by . For each product, it calculates the Gross Profit Margin, the Average Units Sold, and the Net Profit, rounding the results to 2 decimal places.
To practice a very similar question try this interactive Wayfair Y-on-Y Growth Rate Question which is similar for calculating performance metrics based on sales or this Amazon Highest-Grossing Items Question which is similar for calculating product performance based on revenue and cost.
The key to acing a Global-e SQL interview is to practice, practice, and then practice some more! Beyond just solving the above Global-e SQL interview questions, you should also solve the 200+ SQL questions on DataLemur which come from companies like Microsoft, Google, and Facebook.
Each problem on DataLemur has hints to guide you, full answers and most importantly, there's an online SQL code editor so you can right online code up your query and have it checked.
To prep for the Global-e SQL interview you can also be useful to solve interview questions from other tech companies like:
In case your SQL skills are weak, forget about going right into solving questions – strengthen your SQL foundations with this SQL interview tutorial.
This tutorial covers SQL concepts such as aggregate functions like SUM()/COUNT()/AVG() and SUM/AVG window functions – both of which come up routinely in SQL job interviews at Global-e.
Beyond writing SQL queries, the other topics to prepare for the Global-e Data Science Interview are:
The best way to prepare for Global-e Data Science interviews is by reading Ace the Data Science Interview. The book's got: