At Chipotle, SQL is used to analyze customer order patterns to optimize their menu offerings based on what people are actually craving. It also helps in managing supply chain data, allowing them to accurately predict what ingredients they will need to keep their kitchens running smoothly, that is why Chipotle asks SQL query questions during interviews for Data Analytics, Data Science, and Data Engineering jobs.
Thus, to help you prep for the Chipotle SQL interview, we've collected 10 Chipotle SQL interview questions in this blog.
Chipotle wants to identify its top customers, or "whale users", who frequently order high amounts. Write a SQL query to identify customers who have made total orders above $500 in the last 12 months.
We will use two tables, and , which have the following schema.
order_id | customer_id | order_date | total_amount |
---|---|---|---|
101 | 1 | 08/10/2021 | 100 |
102 | 2 | 11/15/2021 | 150 |
103 | 3 | 09/20/2021 | 80 |
104 | 1 | 07/25/2022 | 250 |
105 | 1 | 07/27/2022 | 200 |
106 | 3 | 08/01/2022 | 90 |
customer_id | name | |
---|---|---|
1 | John Doe | johndoe@email.com |
2 | Jane Smith | janesmith@email.com |
3 | Robert Paul | robertpaul@email.com |
This SQL query first joins the orders and customers table on . It then limits the data to orders from the last year. It then calculates the total amount spent by each customer within that period. The condition ensures we only return customers who have spent more than $500.
To work on a similar customer analytics SQL question where you can solve it interactively and have your SQL solution instantly graded, try this Walmart SQL Interview Question:
You're given a table of Chipotle employee and department salary information. Write a SQL query to find departments where the departments average salary is higher than the company's average salary.
You can solve this problem and run your code right in DataLemur's online SQL environment:
The solution is LONG – 30+ lines of SQL. You can find a step-by-step solution here: Department Salaries.
To explain the difference between a primary key and foreign key, let's inspect employee data from Chipotle's HR database:
employee_id | first_name | last_name | manager_id |
---|---|---|---|
1 | Aubrey | Graham | 3 |
2 | Marshal | Mathers | 3 |
3 | Dwayne | Carter | 4 |
4 | Shawn | Carter |
In this table, could be a primary key. It is unique for each row in the table and cannot contain null values.
could be a foreign key. It references the of the manager of each employee in the table, establishing a relationship between the employees and their managers. This foreign key allows you to easily query the table to find out who a specific employee's manager is, or to find out which employees report to a specific manager.
It is also possible for a table to have multiple foreign keys that reference different primary keys in other tables. For example, the table could have additional foreign keys for the of the department where each employee works, and the l of the location where each employee is based.
You are provided with the sales data of Chipotle restaurants. The data contains each sale's transaction ID, the date of the transaction, the menu item sold, and the number of units sold in each transaction.
Write a SQL query to find out the top-selling menu item for each month.
transaction_id | trans_date | menu_item | units_sold |
---|---|---|---|
7162 | 01/04/2022 | Chicken Burrito | 12 |
5412 | 01/24/2022 | Steak Burrito | 8 |
1287 | 01/30/2022 | Chicken Burrito | 10 |
4851 | 02/15/2022 | Steak Burrito | 11 |
6592 | 02/21/2022 | Veggie Bowl | 13 |
3918 | 03/08/2022 | Chicken Burrito | 15 |
7145 | 03/22/2022 | Steak Burrito | 10 |
This SQL query groups sales data by month and menu item to calculate the total number of units sold for each menu item in each month. Then, it uses a window function to assign a row number to each row in each partition (i.e., each month), with the ranking ordered by the total number of units sold in descending order. Finally, it only selects the rows where the row number is 1 (i.e., the menu item with the most sales in each month).
month | menu_item | total_units_sold |
---|---|---|
1 | Chicken Burrito | 22 |
2 | Veggie Bowl | 13 |
3 | Chicken Burrito | 15 |
Pro Tip: Window functions are a frequent SQL interview topic, so practice every window function problem on DataLemur
The SQL command merges the results of multiple statements and keeps only those rows that are present in all sets.
For example, say you were doing an HR Analytics project for Chipotle, and had access to Chipotle's employees and contractors data. Assume that some employees were previously contractors, and vice versa, and thus would show up in both tables. You could use operator to find all contractors who also show up in the employees table:
Chipotle is renowned for its build-your-own style burritos, bowls, salads, and tacos. As its Database Engineer, you are to design a database structure to store and handle data about customers, their orders, and ingredients. Furthermore, you've been tasked with writing an SQL query that would provide the top 3 most popular main dish (burrito, bowl, salad, taco) ordered at Chipotle.
The database should contain the following tables;
Additionally, implement the necessary relationships among these tables.
customer_id | first_name | last_name | registration_date | |
---|---|---|---|---|
1001 | John | Doe | john.doe@example.com | 01/01/2022 |
1002 | Jane | Doe | jane.doe@example.com | 02/02/2022 |
order_id | customer_id | order_date |
---|---|---|
5001 | 1001 | 03/01/2022 |
5002 | 1002 | 03/02/2022 |
order_id | main_dish | ingredients |
---|---|---|
5001 | Burrito | Chicken, White Rice, Black Beans, Cheese |
5002 | Bowl | Steak, Brown Rice, Pinto Beans, Guacamole, Cheese, Sour Cream |
ingredient_id | ingredient_name | calories |
---|---|---|
2001 | Chicken | 180 |
2002 | Steak | 220 |
To find the top 3 most popular main dishes:
The above query will get the count of each unique main dish in the table, order them in descending order, and get the top 3. It will provide Chipotle with insights on their most popular main dishes based on order data.
A join in SQL combines rows from two or more tables based on a shared column or set of columns. To demonstrate the difference between a and , say you had a table of Chipotle orders and Chipotle customers.
LEFT JOIN: A retrieves all rows from the left table (in this case, the table) and any matching rows from the right table (the table). If there is no match in the right table, values will be returned for the right table's columns.
RIGHT JOIN: A retrieves all rows from the right table (in this case, the table) and any matching rows from the left table (the table). If there is no match in the left table, values will be returned for the left table's columns.
Below is a hypothetical scenario: You are working at Chipotle as a data analyst and asked to calculate the average price of orders for each day of a specific month. Here's some sample data for the problem:
order_id | order_date | price |
---|---|---|
1 | 01/01/2022 | 15.90 |
2 | 01/01/2022 | 9.50 |
3 | 01/02/2022 | 12.75 |
4 | 01/02/2022 | 17.40 |
5 | 01/02/2022 | 11.20 |
6 | 01/03/2022 | 14.30 |
7 | 01/03/2022 | 10.75 |
order_date | average_price |
---|---|
01/01/2022 | 12.70 |
01/02/2022 | 13.78 |
01/03/2022 | 12.52 |
We can solve this problem in PostgreSQL using the function to compute the average price over the . Here is a SQL query that would return the desired result:
This query is grouping rows by and then for each group calculates the . The result set will contain a row for every unique with the average price of orders for that day.
To practice a very similar question try this interactive Amazon Highest-Grossing Items Question which is similar for calculating top items by sales or this Alibaba Compressed Mean Question which is similar for calculating averages.
In the context of Chipotle, an acclaimed food chain, imagine they're running a variety of digital advertisements on various platforms. They are interested in understanding the effectiveness of these ads in attracting potential customers to their online ordering platform.
Specifically, they want you to calculate the click-through-rate (CTR) for each ad, which is the number of times an ad is clicked divided by the number of impressions (times the ad is shown). Furthermore, they want you to calculate the conversion rate, which is the number of times an ad click results in an actual purchase.
Two tables are presented below - , representing each time an ad is shown, and , representing each time an ad is clicked and potentially leads to a purchase:
impression_id | ad_id | date |
---|---|---|
1 | 101 | 08/01/2022 |
2 | 102 | 08/01/2022 |
3 | 101 | 08/01/2022 |
4 | 103 | 08/01/2022 |
5 | 102 | 08/01/2022 |
click_id | impression_id | purchase |
---|---|---|
1 | 1 | true |
2 | 2 | false |
3 | 1 | true |
4 | 4 | true |
5 | 2 | false |
ad_id | ctr | conversion_rate |
---|---|---|
101 | 66.67 | 100.00 |
102 | 66.67 | 0.00 |
103 | 33.33 | 100.00 |
This query combines the and tables using a on the . The CTR is calculated by dividing the number of clicks by the number of impressions per ad and multiplying by 100 to get a percent. The conversion rate is calculated by dividing the number of purchases ('true' in the column) by the number of clicks per ad, and again multiplying by 100 to get a percent.
To solve a similar SQL problem on DataLemur's free interactive SQL code editor, try this SQL interview question asked by Facebook:
In SQL, a join retrieves rows from multiple tables and combines them into a single result set.
Four JOIN types can be found in SQL. For an example of each one, suppose you had a table of Chipotle orders and Chipotle customers.
INNER JOIN: Rows from both tables are retrieved when there is a match in the shared key or keys. An between the Orders and Customers tables would return only rows where the in the Orders table matches the in the Customers table.
LEFT JOIN: A retrieves all rows from the left table (in this case, the table) and any matching rows from the right table (the table). If there is no match in the right table, values will be returned for the right table's columns.
RIGHT JOIN: A combines all rows from the right table (in this case, the table) and any matching rows from the left table (the table). If there is no match in the left table, values will be displayed for the left table's columns.
FULL OUTER JOIN: A combines all rows from both tables, regardless of whether there is a match in the shared key or keys. If there is no match, values will be displayed for the columns of the non-matching table.
The best way to prepare for a Chipotle SQL interview is to practice, practice, practice. Besides solving the earlier Chipotle SQL interview questions, you should also solve the 200+ DataLemur interview questions which come from companies like Amazon, Microsoft, Meta, and smaller tech companies.
Each interview question has hints to guide you, step-by-step solutions and crucially, there is an online SQL code editor so you can instantly run your SQL query and have it graded.
To prep for the Chipotle SQL interview it is also a great idea to solve SQL problems from other hospitality and restaurant companies like:
But if your SQL foundations are weak, forget about jumping right into solving questions – refresh your SQL knowledge with this free SQL for Data Analytics course.
This tutorial covers things like joining a table to itself and Union vs. UNION ALL – both of which come up routinely in Chipotle interviews.
Beyond writing SQL queries, the other question categories to practice for the Chipotle Data Science Interview are:
To prepare for the Chipotle Data Science interview have a firm understanding of the company's values and company principles – this will be clutch for acing the behavioral interview. For the technical Data Science interviews, prepare by reading Ace the Data Science Interview. The book's got: