At Skechers USA, SQL does the heavy lifting for analyzing sales patterns for footwear across various global locations. Because of this, Skechers LOVES to ask SQL coding questions during interviews for Data Science and Data Engineering positions.
To help you prep for the Skechers SQL interview, we've curated 10 Skechers USA SQL interview questions in this article.
Skechers, a popular footwear company, identifies its VIP customers as those who purchase frequently and spend a large amount on their orders. As a data analyst, you are tasked to find the top spending customers in the first half of 2022.
For this task, suppose there are two tables - users and orders. The table users stores customer information including their user_id, first name and last name. The table orders contains order details, capturing the user id whom the order belongs to, order id, product id, total amount spent, and the date the purchase was made.
The structures of these tables are like:
Example Input:
user_id | first_name | last_name |
---|---|---|
110 | Alex | Johnson |
134 | Sarah | Brown |
157 | Maddy | Williams |
183 | Mark | Smith |
203 | Joyce | Davis |
Example Input:
order_id | user_id | product_id | amount | date |
---|---|---|---|---|
1 | 110 | 303 | 200 | 01/03/2022 |
2 | 134 | 503 | 250 | 02/05/2022 |
3 | 157 | 203 | 120 | 03/08/2022 |
4 | 110 | 503 | 300 | 02/06/2022 |
5 | 110 | 203 | 200 | 04/01/2022 |
This query first creates a join between the users and orders tables on user_id, then it filters for rows that are from the first half of 2022. It sums the total amount spent by each user in the filtered time frame, then ranks the customers based on the total amount spent. It returns the top 10 customers who spent the most in the first half of 2022.
To solve a super-customer analysis question on DataLemur's free online SQL code editor, try this Microsoft Azure Cloud SQL Interview Question:
You're given a table of Skechers employee and department salaries. Write a SQL query to find departments where the departments average salary is higher than the company's average salary.
Solve this question interactively on DataLemur:
The answer is LONG – 30+ lines of SQL. You can find a detailed solution here: Department vs. Company Salary.
btw skechers hit record sales during their 2024 first quarter financial results of $2.25 billion!!
While knowing this answer is beyond the scope of most Data Analyst & Data Science interviews, Data Engineers at Skechers should be at least aware of SQL vs. NoSQL databases.
Relational databases and non-relational (NoSQL) databases have some key differences, particularly in terms of how data is stored. Whereas relational databases have tables, made up of rows and columns, NoSQL databases use various data models like:
This added flexibility makes NoSQL databases well-suited for handling non-tabular data or data with a constantly changing format. However, this flexibility comes at the cost of ACID compliance, which is a set of properties (atomic, consistent, isolated, and durable) that ensure the reliability and integrity of data in a database. While most relational databases are ACID-compliant, NoSQL databases may not provide the same level of guarantees.
As a data analyst for Skechers, your task is to write a SQL query that finds out the monthly average ratings of each product. For this analysis, consider only the month and year of the and ignore the day. Group the results by the month and product_id, and order the result set by the month and product id.
The 'reviews' table has the following schema:
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 |
In this query, the function is used to get the month and year of . Then, the function is used to get the average review of each product for each month. We use to round the average to two decimal points. The clause groups the results by month-year and . Finally, the clause orders the result set by month and product_id.
For more window function practice, solve this Uber SQL Interview Question on DataLemur's online SQL code editor:
A cross-join, also known as a cartesian join, is a JOIN that produces the cross-product of two tables. In a cross-join, each row from the first table is matched 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.
Suppose you were building a Neural Network ML model, that tried to score the probability of a customer buying a Skechers product. Before you started working in Python and Tensorflow, you might want to do some Exploratory Data Analysis (EDA) in SQL, and generate all pairs of customers and Skechers products.
Here's a cross-join query you could use to find all the combos:
Cross-joins are great for generating all possible combinations, but they can also create really big tables if you're not careful. For example, if you had 10,000 potential customers, and Skechers had 500 different product SKUs, you'd get 5 million rows as a result!!
As an analyst at Skechers, you are tasked with designing a database to keep track of product sales and customer reviews. Hence, two separate tables named and are designed. The table captures the information about , , , , and . On the other hand, table holds , , , , and .
Your task is to write a PostgreSQL query to find out the total units sold and average stars received for each product for each month.
sales_id | product_id | branch_id | sales_date | units |
---|---|---|---|---|
1 | 101 | 1 | 06/08/2021 | 50 |
2 | 102 | 1 | 06/10/2021 | 40 |
3 | 101 | 2 | 06/18/2021 | 60 |
4 | 103 | 1 | 07/26/2021 | 70 |
5 | 101 | 2 | 07/05/2021 | 40 |
review_id | user_id | submit_date | product_id | stars |
---|---|---|---|---|
1 | 101 | 06/08/2021 00:00:00 | 101 | 4 |
2 | 102 | 06/10/2021 00:00:00 | 101 | 4 |
3 | 103 | 06/18/2021 00:00:00 | 101 | 3 |
4 | 104 | 07/26/2021 00:00:00 | 102 | 3 |
5 | 105 | 07/05/2021 00:00:00 | 103 | 2 |
The following SQL query should provide the desired result.
This SQL query first combines the and tables based on and the month of sale/review. It then calculates the total number of units sold () and the average star rating () for each product for each month ().
Normalization can reduce redundancy and improve performance and flexibility in a database.
By dividing larger tables into smaller, more specific ones linked by foreign keys, it is often possible to improve query speed. Note: it is not always the case that query speed will be improved due to costly nature of joins. Still, we think normalization is a good idea because it can also increase flexibility by making it easier to alter individual tables without affecting others, which can be useful for adapting Skechers's database to ever-changing business needs.
Skechers has recently run a series of digital ads, and they are leveraging SQL to compute various metrics to assess their performance. Your task is to calculate the click-through-rate (CTR) for each ad.
The CTR is defined as the total number of clicks that an ad receives, divided by the total number of times that ad is shown (impressions), written as a percentage.
You are given two tables, and .
Write a SQL query that shows the CTR for each ad.
This SQL query starts by joining the impressions and clicks tables on the ad_id. It then computes the CTR for each ad by taking the count of unique click_ids (to avoid counting multiple clicks from the same user) and dividing it by the count of unique impressions (again to avoid counting the same ad shown multiple times to the same user). This results in the percentage of impressions that led to a click for each ad.
To practice a similar SQL problem on DataLemur's free interactive SQL code editor, try this Facebook SQL Interview question:
As an analyst at Skechers, you are tasked to analyze the company's sales data. You are expected to find out the monthly sales of each shoe model. Through analysis, you aim to determine which models are best-sellers and which models may need improvement in terms of sales.
Here are the sample tables:
sale_id | sale_date | model_id | quantity |
---|---|---|---|
101 | 2023-01-15 | 1423 | 5 |
102 | 2023-01-20 | 1678 | 8 |
103 | 2023-02-18 | 1423 | 7 |
104 | 2023-02-25 | 1678 | 4 |
105 | 2023-03-14 | 1423 | 9 |
106 | 2023-03-20 | 1678 | 6 |
month | model | total_sales |
---|---|---|
1 | 1423 | 5 |
1 | 1678 | 8 |
2 | 1423 | 7 |
2 | 1678 | 4 |
3 | 1423 | 9 |
3 | 1678 | 6 |
This query extracts the month from the date column, groups data by month and shoe model, and then sums up the quantity of sales for each model. This information will help Skechers identify which models sell better during which months.
A database index is a data structure that provides a quick lookup of data in a column or columns of a table.
There are several types of indexes that can be used in a database:
The key to acing a Skechers SQL interview is to practice, practice, and then practice some more! In addition to solving the earlier Skechers SQL interview questions, you should also solve the 200+ FAANG SQL Questions on DataLemur which come from companies like Microsoft, Google, and Facebook.
Each interview question has multiple hints, step-by-step solutions and crucially, there is an online SQL coding environment so you can easily right in the browser your query and have it checked.
To prep for the Skechers SQL interview you can also be useful to solve SQL problems from other apparel companies like:
But if your SQL foundations are weak, forget about diving straight into solving questions – go learn SQL with this DataLemur SQL tutorial.
This tutorial covers topics including using wildcards with LIKE and SUM/AVG window functions – both of which pop up frequently in Skechers interviews.
In addition to SQL interview questions, the other topics to practice for the Skechers Data Science Interview are:
The best way to prepare for Skechers Data Science interviews is by reading Ace the Data Science Interview. The book's got: