11 Columbia Sportswear SQL Interview Questions (Updated 2024)
At Columbia Sportswear, SQL is used across the company for sales trends and optimizing inventory management based on customer purchase patterns. That's the reason behind why Columbia Sportswear asks SQL coding questions in interviews for Data Analytics, Data Science, and Data Engineering jobs.
To help you practice for the Columbia Sportswear SQL interview, here's 11 Columbia Sportswear SQL interview questions in this article.
11 Columbia Sportswear SQL Interview Questions
SQL Question 1: Identify Top Purchasing Customers at Columbia Sportswear
Columbia Sportswear wants to award their top customers based on their purchasing behavior throughout the year. As the company's data analyst, you are asked to identify the top 10 customers who have made the most unique purchases in the last year.
For this question, assume a "unique purchase" is a unique combination of customer and product. Multiple purchases of the same product by the same customer count as one unique purchase.
Example Input:
| purchase_id | customer_id | purchase_date | product_id | quantity |
|---|---|---|---|---|
| 8439 | 319 | 06/08/2021 00:00:00 | 50001 | 3 |
| 7042 | 987 | 06/10/2021 00:00:00 | 69852 | 6 |
| 7893 | 319 | 06/11/2021 00:00:00 | 50001 | 1 |
| 3552 | 746 | 07/26/2021 00:00:00 | 69852 | 3 |
| 5678 | 746 | 07/05/2021 00:00:00 | 69852 | 2 |
Answer:
This query first defines the date constraint in WHERE clause, selecting only purchases made in the year 2021. It then groups the results by customers (), and counts the number of unique products () purchased by each of them. The results are then ordered in descending order by the number of unique purchases, and limited to the top 10 customers. With this query, we will be able to identify the top 10 customers of Columbia Sportswear who made the most number of unique purchases in the year 2021.
To practice a related customer analytics question on DataLemur's free online SQL coding environment, try this Microsoft SQL Interview problem:
SQL Question 2: 2nd Highest Salary
Given a table of Columbia Sportswear employee salary information, write a SQL query to find the 2nd highest salary amongst all the employees.
Columbia Sportswear Example Input:
| employee_id | salary |
|---|---|
| 1 | 2500 |
| 2 | 800 |
| 3 | 1000 |
| 4 | 1200 |
Example Output:
| second_highest_salary |
|---|
| 1200 |
Solve this problem interactively on DataLemur:
Answer:
You can find a step-by-step solution here: 2nd Highest Salary.
SQL Question 3: How do and differ when it comes to ranking rows in a result set?
In SQL, both and are used to rank rows within a result set. The key difference between the two functions is how deal with two or more rows having the same value in the ranked column (aka how the break ties).
RANK(): When there's a tie, leaves a gap in the ranking. For example, if three rows are tied for 2nd place, the RANK() function will assign a rank of 2 to the first of these rows, a rank of 3 to the second row, and a rank of 4 to the third row.
DENSE_RANK(): For ties, does not leave a gap in the ranking. Instead, it assigns the same rank to all tied rows, and then makes the next row 1 bigger. Confusing, I know, but here's an example to make it more clear: if three rows are tied for 3rd place, the function will assign a rank of 3 to all three rows, and then assign a rank of 4 to the next row.
SQL Question 4: Analyzing Product Sales and Returns using Window Functions
As a data analyst at Columbia Sportswear, your task is to evaluate the performance of each product by identifying the total sales and returns in the month of purchase, considering the past 6 months of data. Your result should be able to show us, the total sale transactions, total return transactions and the net sales (sales - returns) for each product, for each month.
Here's your sample dataset:
Example Input:
| sale_id | product_id | sale_date | quantity |
|---|---|---|---|
| 1 | 1001 | 02/01/2022 | 10 |
| 2 | 1002 | 02/15/2022 | 5 |
| 3 | 1001 | 03/10/2022 | 20 |
| 4 | 1002 | 03/20/2022 | 15 |
| 5 | 1001 | 04/05/2022 | 10 |
Example Input:
| return_id | sale_id | return_date | quantity |
|---|---|---|---|
| 1 | 1 | 02/15/2022 | 2 |
| 2 | 2 | 02/20/2022 | 1 |
| 3 | 3 | 03/20/2022 | 5 |
| 4 | 4 | 03/30/2022 | 3 |
| 5 | 5 | 04/15/2022 | 2 |
Answer:
This query calculates the total number of sales and returns for each product in each month over the past 6 months, it also calculates the net number of items sold considering the returned quantities. The use of COALESCE is to handle NULL values in the total_returns and total_returned fields, because not all sales operations have returned operations. Notice the JOIN on two conditions - product_id and the month, to ensure the net calculation is accurate for each month.
For more window function practice, solve this Uber SQL Interview Question within DataLemur's online SQL coding environment:
SQL Question 5: Can you describe the different types of joins in SQL?
A join in SQL allows you to retrieve data from multiple tables and combine it into a single set of results.
In SQL, there are four different types of JOINs. To demonstrate each kind, imagine you had two database tables, an advertising campaigns table which had information on Google Ads keywords and how much was bid for each keyword, and a sales table, which has data on how many products were sold and which Google Ads keyword drove that sale.
: An INNER JOIN retrieves rows from both tables where there is a match in the shared key or keys. For example, an INNER JOIN between the table and the table could be performed using the keyword column as the shared key. This would retrieve only the rows where the keyword in the table matches the keyword in the table.
: A LEFT JOIN retrieves all rows from the left table (in this case, the table) and any matching rows from the right table (the Sales table). If there is no match in the right table, values will be returned for the right table's columns.
: A RIGHT JOIN retrieves all rows from the right table (in this case, the Sales 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.
: A FULL OUTER JOIN retrieves 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 returned for the columns of the non-matching table.
Check out Columbia Sportswears upcoming events, they love hiring those who take deep interest in the brand.
SQL Question 6: Product Sales Analysis
For Columbia Sportswear, the business problem could be trying to determine which products are selling the most in different regions. The aim is to optimize inventory in different stores spread across these regions. To tackle this problem, you need to design the database and write an SQL query which would help in finding the products which are selling the most in different regions.
Database Tables:
Let's consider we have three tables:
table stores information about different products. table stores information about sales: which product was sold, in which region, and when. and the table stores information about different regions.
Example Input:
| product_id | product_name |
|---|---|
| 101 | Men's Jacket |
| 102 | Women's Jacket |
| 103 | Kid's Jacket |
Example Input:
| sale_id | product_id | region_id | sale_date | units_sold |
|---|---|---|---|---|
| 1 | 101 | 301 | 2022-07-10 | 20 |
| 2 | 101 | 302 | 2022-07-10 | 15 |
| 3 | 102 | 301 | 2022-07-11 | 30 |
| 4 | 103 | 302 | 2022-07-12 | 50 |
Example Input:
| region_id | region_name |
|---|---|
| 301 | North America |
| 302 | Europe |
Answer:
Given the above table structure and the problem statement, you can use below SQL to extract the list of best selling products in every region:
This query will show you for each region, what are the top selling products sorted by the number of units sold. It joins , and tables and groups the result by and . It also calculates the total units sold for each product in every region. Sorting by in descending order will give you the list of products in order of their sales.
SQL Question 7: What are the benefits of normalizing a database?
Database normalization is helpful because it improves the performance, flexibility, and scalability of a database. Some specific benefits of normalization include:
-
Reducing Redundancy: Normalization helps to minimize redundancy by breaking down a larger general table into smaller, more specific tables. This can often reduce the amount of data that needs to be accessed for particular queries.
-
Improving Data Integrity: Normalization helps to ensure the integrity of the data by minimizing the risk of data inconsistencies and anomalies. It does this by establishing clear relationships between the tables via primary and foreign keys.
-
Increasing Flexibility: Normalization makes it easier to modify the structure of the database, as there is less redundancy, so it allows you to make changes to one table without affecting others. This makes it easier to adapt the database to changing business needs (and change is basically constant at Columbia Sportswear!)
-
Improving Performance: Normalization can improve the performance of the database by reducing the amount of data that needs to be stored and accessed in a single table. This can result in faster query times and better overall performance.
-
Easier Data Modeling: Normalization can make it easier to model the data in a logical and intuitive way, since less random data is commingled together, which improves the overall database design.
SQL Question 8: Filtering Customer Data
Columbia Sportswear wants to analyze customer purchase behavior and target their marketing efforts based on this data. Write a SQL query to find out all customers from United States who have made a purchase of over $1000 in the 'Winter' category last year.
Example Input:
| customer_id | first_name | last_name | country |
|---|---|---|---|
| 2121 | John | Doe | United States |
| 3331 | Jane | Smith | United States |
| 1232 | Alice | Wang | Canada |
| 7642 | Bob | Johnson | United States |
| 3129 | Charlie | Brown | Australia |
Example Input:
| purchase_id | customer_id | category | purchase_date | amount |
|---|---|---|---|---|
| 1001 | 2121 | Winter | 2019-11-20 | $1500 |
| 2002 | 3331 | Fall | 2019-09-07 | $500 |
| 3003 | 1232 | Winter | 2019-12-10 | $1200 |
| 4004 | 7642 | Winter | 2019-12-15 | $1100 |
| 5005 | 2121 | Spring | 2020-03-05 | $700 |
Answer:
This query joins the and tables on the field. It then filters for customers from the United States who made a purchase in the 'Winter' category last year (2019) with an amount greater than $1000. These results will help Columbia Sportswear to identify high-value customers to target for their Winter marketing campaigns.
SQL Question 9: Calculate the average item price for each product category in Columbia Sportswear.
At Columbia Sportswear, the question can be: "What is the average price of items in each product category?"
Example Input:
| product_id | category | price |
|---|---|---|
| 1001 | Jacket | 120.00 |
| 1002 | Jacket | 150.00 |
| 1003 | Boots | 80.00 |
| 1004 | Boots | 120.00 |
| 1005 | Hat | 20.00 |
| 1006 | Hat | 30.00 |
| 1007 | Hat | 25.00 |
Example Output:
| category | average_price |
|---|---|
| Jacket | 135.00 |
| Boots | 100.00 |
| Hat | 25.00 |
Answer:
In this query, we first group the data by the category column, then we calculate the average price of all products within each category using the AVG function from PostgreSQL. This will produce a table with each unique category and its corresponding average price. This information could be useful for Columbia Sportswear to understand the price range for each category of products they offer.
To practice a very similar question try this interactive Amazon Highest-Grossing Items Question which is similar for using category and item aspects of a database or this Walmart Histogram of Users and Purchases Question which is similar for asking for a computation related to items bought.
SQL Question 10: What is normalization?
Normalization involves breaking up your tables into smaller, more specialized ones and using primary and foreign keys to define relationships between them. Not only does this make your database more flexible and scalable, it also makes it easier to maintain. Plus, normalization helps to keep your data accurate by reducing the chance of inconsistencies and errors.
The only downside is now is that your queries will involve more joins, which are slow AF and often a DB performance botteleneck.
SQL Question 11: Average Ratings and Total Reviews for Columbia Sportswear Products
For the Columbia Sportswear Company, their business revolves around different sports products. Suppose the company wants to know the average review ratings and total number of reviews for each product they sell. The goal is to write a SQL query that can obtain the average rating (stars) and the total count of reviews for each product, for every month.
Assume Columbia Sportswear maintains a 'product' table detailing all their products and a 'reviews' table capturing all the customer reviews for their products.
Example Input:
| product_id | product_name |
|---|---|
| 101 | Men's Bugaboot Plus IV Omni-Heat Boot |
| 102 | Women's Benton Springs Full Zip Fleece Jacket |
| 103 | Youth Fast Trek Glove |
Example Input:
| review_id | user_id | submit_date | product_id | stars |
|---|---|---|---|---|
| 1 | 123 | 03/21/2022 | 101 | 4 |
| 2 | 265 | 04/01/2022 | 102 | 5 |
| 3 | 825 | 04/15/2022 | 103 | 3 |
| 4 | 265 | 03/29/2022 | 101 | 5 |
| 5 | 701 | 04/21/2022 | 102 | 4 |
Answer:
The following PostgreSQL query should provide the desired output, which will group the reviews by month and product, then calculating the average star rating and total reviews for each group.
This SQL statement will join the 'reviews' and 'products' tables on the 'product_id' column. It will then group the reviews by the month and the product, and calculate the average star rating and the total reviews for each group in descending order by the average star rating.
Preparing For The Columbia Sportswear SQL Interview
The key to acing a Columbia Sportswear SQL interview is to practice, practice, and then practice some more!
Besides solving the above Columbia Sportswear SQL interview questions, you should also solve the 200+ SQL exercises on DataLemur which come from companies like FAANG and tech startups.
Each interview question has hints to guide you, step-by-step solutions and most importantly, there's an online SQL coding environment so you can right online code up your query and have it checked.
To prep for the Columbia Sportswear SQL interview it is also useful to practice interview questions from other apparel companies like:
However, if your SQL query skills are weak, forget about diving straight into solving questions – refresh your SQL knowledge with this DataLemur SQL Tutorial.
This tutorial covers SQL concepts such as joining a table to itself and math functions – both of these come up frequently in Columbia Sportswear SQL assessments.
Columbia Sportswear Data Science Interview Tips
What Do Columbia Sportswear Data Science Interviews Cover?
In addition to SQL query questions, the other types of problems to practice for the Columbia Sportswear Data Science Interview are:
- Probability & Stats Questions
- Coding Questions in Python or R
- Product Metrics Interview Questions
- ML Interview Questions
- Behavioral Based Interview Questions
How To Prepare for Columbia Sportswear Data Science Interviews?
I'm sorta biased, but I think the optimal way to prep for Columbia Sportswear Data Science interviews is to read the book I wrote: Ace the Data Science Interview.
The book has 201 interview questions taken from Google, Microsoft & tech startups. It also has a crash course covering Stats, SQL & ML. And finally it's helped a TON of people, which is how it's gotten to be an Amazon best-seller with over 1000+ 5-star reviews.