9 Starbucks SQL Interview Questions (Updated 2024)
At Starbucks, SQL is used to analyze customer behavior patterns by analyzing purchase history and preferences, enabling the company to improve its marketing strategies. It also helps manage inventory by tracking sales data in real-time, ensuring that popular items are always in stock, this is the reason why Starbucks asks SQL questions in interviews for Data Science and Data Engineering positions.
So, to help you prep, here's 9 Starbucks SQL interview questions – able to answer them all?
9 Starbucks SQL Interview Questions
SQL Question 1: Analyze Monthly Average Rating for Starbucks Products
You are a data analyst at Starbucks. You are given a dataset of user reviews on different Starbucks products. Your task is to write a SQL query to find the monthly average rating for each product.
Consider the following tables illustrate the dataset:
Example Input:
| review_id | user_id | submit_date | product_id | stars |
|---|---|---|---|---|
| 6171 | 123 | 2022-06-08 | 50001 | 4 |
| 7802 | 265 | 2022-06-10 | 69852 | 4 |
| 5293 | 362 | 2022-06-18 | 50001 | 3 |
| 6352 | 192 | 2022-07-26 | 69852 | 3 |
| 4517 | 981 | 2022-07-05 | 69852 | 2 |
Your result should look like:
Example Output:
| month | product_id | avg_stars |
|---|---|---|
| 6 | 50001 | 3.5 |
| 6 | 69852 | 4.0 |
| 7 | 69852 | 2.5 |
Answer:
This query uses the function to get the month from , function to compute the average rating (), and to group the results by both and . It then sorts the result by and .
Pro Tip: Window functions are a frequent SQL interview topic, so practice every window function problem on DataLemur
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SQL Question 2: 2nd Largest Salary
Assume you had a table of Starbucks employee salary data. Write a SQL query to find the 2nd highest salary among all employees.
Starbucks Example Input:
| employee_id | salary |
|---|---|
| 1 | 2500 |
| 2 | 800 |
| 3 | 1000 |
| 4 | 1200 |
Example Output:
| second_highest_salary |
|---|
| 1200 |
Check your SQL query for this interview question directly within the browser on DataLemur:
Answer:
You can find a step-by-step solution with hints here: 2nd Highest Salary.
SQL Question 3: Can you describe the difference between a correlated and a non-correlated sub-query?
A correlated sub-query is one that relies on the outer query and cannot be run independently. It uses the outer query to filter or transform data by referencing a column from it, while the outer query uses the results of the inner query. On the other hand, a non-correlated sub-query is not linked to the outer query and can be run on its own. It does not reference any columns from the outer query and is used to retrieve additional data for the outer query.
An example correlated sub-query:
This correlated subquery retrieves the names and salaries of Starbucks employees who make more than the average salary for their department. The subquery references the column in the main query's clause () and uses it to filter the rows of the subquery's clause ().
An example non-correlated sub-query:
This non-correlated subquery retrieves the names and salaries of Starbucks employees who make more than the average salary for the Data Analytics department (which honestly should be very few people since Data Analysts are awesome).
Anyways, the subquery is independent of the main query and can be considered a standalone query. Its output (the average salary for the Data Analytics department) is then used in the main query to filter the rows of the table.
SQL Question 4: Filter Customer Data for Specific Criteria
As a data analyst for Starbucks, you have been given access to the database containing all the customer transactions over the past two years. Your task is to identify customers who have made more than 5 purchases, spent more than $500 in overall, and have ordered at least one time from each of Starbuck's drink categories: coffee, tea, and smoothies. We'll use two tables for this scenario - and .
Example Input:
:
| customer_id | customer_name |
|---|---|
| 1001 | John Doe |
| 1002 | Jane Doe |
| 1003 | Jennifer Roe |
| 1004 | Jack Roe |
| 1005 | Jessica Cole |
:
| transaction_id | customer_id | transaction_time | amount ($) | category |
|---|---|---|---|---|
| 2001 | 1001 | 2020-01-03 | 25 | coffee |
| 2002 | 1001 | 2020-02-14 | 50 | tea |
| 2003 | 1002 | 2020-03-06 | 30 | smoothies |
| 2004 | 1003 | 2020-04-09 | 40 | coffee |
| 2005 | 1003 | 2020-05-20 | 45 | smoothies |
| 2006 | 1003 | 2020-06-21 | 70 | tea |
| 2007 | 1003 | 2020-10-26 | 45 | coffee |
| 2008 | 1004 | 2020-03-02 | 55 | smoothies |
| 2009 | 1004 | 2020-04-23 | 60 | coffee |
| 2010 | 1005 | 2020-05-14 | 90 | coffee |
Answer:
This query works by first grouping the table by . Then, using the clause, it checks for the conditions specified in the problem - more than 5 unique transactions, more than $500 spent overall, and at least one transaction in each drink category. The statements in the clauses check for the existence of a transaction in each category.
Finally, the result is joined back to the table using to get the corresponding for the output.
SQL Question 5: What does the keyword do?
The clause is used to remove all duplicate records from a query.
For example, if you had a table of open jobs Starbucks was hiring for, and wanted to see what are all the unique job titles that were currently available at the company, you could write the following query:
SQL Question 6: Calculating Click-Through Rates for Starbucks Email Marketing Campaign
Starbucks regularly sends out promotional emails to its customers. Each email includes a link to a specific product or a promotional deal on Starbucks' website. Clicking the link counts as a "click", and purchasing the said product or deal counts as a "conversion".
For a particular period, you need to calculate the click-through rate (CTR) and click-through conversion rate (CTCR) for each campaign. The CTR is defined as the total number of clicks divided by the total number of emails sent. The CTCR is the total number of conversions divided by the total number of clicks.
Please consider the following schema for and table:
Example Input:
| campaign_id | email_id | sent_date | product_id | click |
|---|---|---|---|---|
| 105 | 101 | 06/08/2022 00:00:00 | 50001 | 1 |
| 106 | 102 | 06/10/2022 00:00:00 | 50001 | 0 |
| 107 | 103 | 06/18/2022 00:00:00 | 69852 | 1 |
| 108 | 104 | 07/26/2022 00:00:00 | 69852 | 1 |
| 109 | 105 | 07/05/2022 00:00:00 | 50001 | 1 |
Example Input:
| transaction_id | email_id | purchase_date | product_id |
|---|---|---|---|
| 501 | 101 | 06/08/2022 01:00:00 | 50001 |
| 502 | 103 | 06/18/2022 02:00:00 | 69852 |
| 503 | 105 | 07/05/2022 01:00:00 | 50001 |
| 504 | 101 | 07/26/2022 02:00:00 | 50001 |
Please note that a '1' under the 'click' column in table represents a click, and '0' represents no click.
Answer:
This query first calculates the total number of emails sent, total clicks and total conversions for each campaign and product using . Then, it calculates the Click-Through Rate (CTR) and Click-Through Conversion Rate (CTCR) by dividing the total clicks by total emails and total conversions by total clicks respectively. The is used to link the conversion transactions to the respective email campaign.
To solve a similar problem about calculating rates, solve this TikTok SQL question on DataLemur's interactive coding environment:
SQL Question 7: What's an index, and what are the different types?
In a database, an index is a data structure that improves the speed of data retrieval operations on a database table at the cost of additional writes and the use of more storage space to maintain the index data structure.
There are several types of indexes that can be used in a database:
- Primary index: a unique identifier for each row in a table and is used to access the row directly.
- Unique index: used to enforce the uniqueness of the indexed columns in a table. It does not allow duplicate values to be inserted into the indexed columns.
- Composite index: is created on multiple columns of a table. It can be used to speed up the search process on the combination of columns.
- Clustered index: determines the physical order of the data in a table. There can be only one clustered index per table.
- Non-clustered index: does NOT determine the physical order of the data in a table. A table can have multiple non-clustered indexes.
SQL Question 8: Average Customer Ratings for Starbucks Drinks Per Month
At Starbucks, there are hundreds of beverages ordered every day. The company collects customer feedback in the form of star rating for each beverage. Can you write a SQL query to determine the average customer rating of each beverage sold by Starbucks per month, using the table? Assume the table has fields for , , , , and .
Sample Input:
| review_id | user_id | submit_date | product_id | stars |
|---|---|---|---|---|
| 001 | 123 | 2022-06-01 | Latte | 5 |
| 002 | 456 | 2022-06-15 | Cappuccino | 4 |
| 003 | 789 | 2022-06-20 | Latte | 3 |
| 004 | 321 | 2022-07-01 | Espresso | 4 |
| 005 | 654 | 2022-07-01 | Latte | 4 |
| 006 | 987 | 2022-07-15 | Cappuccino | 5 |
| 007 | 147 | 2022-07-16 | Espresso | 2 |
Example Output:
| month | product | avg_stars |
|---|---|---|
| 6 | Latte | 4.00 |
| 6 | Cappuccino | 4.00 |
| 7 | Latte | 4.00 |
| 7 | Cappuccino | 5.00 |
| 7 | Espresso | 3.00 |
Answer:
This query first extracts the month from each using the function. It then groups the records by the and . For each group, it takes the average (using the function) of the column (the ratings). This results in one row for each product per month, with the average ratings for that product in that month.
SQL Question 9: Analyze Purchase and Customer Behavior
Assume that Starbucks wants to analyze the purchase behaviors of customers by joining the table with the table. The table contains details of each coffee type purchased by each customer, its price, and the purchase date. The table includes information about each customer's identification number, birthdate, and zip code.
Write an SQL query to find the total expenditure of each zip code customers on 'Latte' for each month of 2021.
Example Input:
| purchase_id | customer_id | coffee_type | price | purchase_date |
|---|---|---|---|---|
| 1000 | 201 | Latte | 5 | 04/20/2021 |
| 1001 | 202 | Espresso | 3 | 02/12/2021 |
| 1002 | 203 | Latte | 4 | 10/15/2021 |
| 1003 | 204 | Cold Brew | 4 | 09/10/2021 |
| 1004 | 201 | Latte | 5 | 08/15/2021 |
Example Input:
| customer_id | birthdate | zip_code |
|---|---|---|
| 201 | 12/18/1982 | 56789 |
| 202 | 11/05/1990 | 56789 |
| 203 | 06/23/1992 | 12345 |
| 204 | 07/30/1988 | 12345 |
Example Output:
| month | zip_code | total_expenditure |
|---|---|---|
| 4 | 56789 | 5 |
| 8 | 56789 | 5 |
| 10 | 12345 | 4 |
Answer:
This query joins the and tables on the attribute. It selects only the rows where is 'Latte' and the year in is 2021. The output provides the total expenditure of customers from each zip code on 'Latte' for each month of 2021.
Since joins come up frequently during SQL interviews, try this SQL join question from Spotify:
Preparing For The Starbucks 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. In addition to solving the earlier Starbucks SQL interview questions, you should also solve the 200+ SQL exercises on DataLemur which come from companies like FAANG tech companies and tech startups.
Each DataLemur SQL question has hints to guide you, detailed solutions and most importantly, there's an online SQL coding environment so you can right in the browser run your SQL query and have it checked.
To prep for the Starbucks SQL interview it is also a great idea to solve interview questions from other hospitality and restaurant companies like:
- Bloomin' Brands SQL questions
- Domino's SQL questions
- Las Vegas Sands SQL questions
- Hilton SQL questions
In case your SQL skills are weak, don't worry about diving straight into solving questions – strengthen your SQL foundations with this DataLemur SQL tutorial.
This tutorial covers things like UNION and using LIKE – both of these show up routinely during SQL job interviews at Starbucks.
Starbucks Data Science Interview Tips
What Do Starbucks Data Science Interviews Cover?
Beyond writing SQL queries, the other question categories to prepare for the Starbucks Data Science Interview include:
- Probability & Statistics Questions
- Python or R Coding Questions
- Product Data Science Interview Questions
- ML Interview Questions
- Behavioral Based Interview Questions
How To Prepare for Starbucks Data Science Interviews?
To prepare for the Starbucks Data Science interview have a deep understanding of the company's values and mission – this will be clutch for acing the behavioral interview. For the technical Data Science interviews, get ready by reading Ace the Data Science Interview. The book's got:
- 201 Interview Questions from FAANG (FB, Apple, Amazon, Netflix, Google)
- A Refresher on Product Analytics, SQL & ML
- Great Reviews (1000+ reviews, 4.5-star rating)