Data Analysts & Data Scientists at BorgWarner write SQL queries as a big part of their job. They use SQL to analyze manufacturing performance data and manage supply chain logistics information. That's the reason behind why BorgWarner usually asks SQL coding interview questions.
So, to help you practice, we've curated 10 BorgWarner SQL interview questions – can you solve them?
Given below is a table , which records the date and purchase amount for every purchase made by a BorgWarner customer. A “Power User” for BorgWarner is defined as a user whose total purchase amount within the last 30 days is more than $10,000. Write a SQL query to generate a report that identifies the Power Users.
Given the schema of 'user_activity' table:
Let's say the report_end_date is '2022-09-10'.
This query will return the list of user ids who are classified as 'Power Users'. In the subquery, we first select those records in the table which are within the last 30 days. We use a Group By aggregation query to calculate the total purchase amount for each user during this period. Then we select user_ids from this temporary table() whose total_purchase is greater than 10000. Those user_ids are 'Power Users'.
To practice a super-customer analysis question on DataLemur's free online SQL coding environment, try this Microsoft Azure Cloud SQL Interview Question:
Assume there was a table of BorgWarner employee salary data. Write a SQL query to find the top three highest paid employees within each department.
employee_id | name | salary | department_id |
---|---|---|---|
1 | Emma Thompson | 3800 | 1 |
2 | Daniel Rodriguez | 2230 | 1 |
3 | Olivia Smith | 2000 | 1 |
4 | Noah Johnson | 6800 | 2 |
5 | Sophia Martinez | 1750 | 1 |
8 | William Davis | 6800 | 2 |
10 | James Anderson | 4000 | 1 |
department_id | department_name |
---|---|
1 | Data Analytics |
2 | Data Science |
department_name | name | salary |
---|---|---|
Data Analytics | James Anderson | 4000 |
Data Analytics | Emma Thompson | 3800 |
Data Analytics | Daniel Rodriguez | 2230 |
Data Science | Noah Johnson | 6800 |
Data Science | William Davis | 6800 |
Code your solution to this question directly within the browser on DataLemur:
We use the DENSE_RANK() window function to generate unique ranks for each employee's salary within their department, with higher salaries receiving lower ranks. Then, we wrap this up in a CTE and filter the employees with a ranking of 3 or lower.
If the solution above is confusing, you can find a detailed solution here: Top 3 Department Salaries.
No, in almost all cases, and for all practical purposes, and do NOT produce the same result.
While both are similar, in that they combine two tables, you can think of joins as increasing the width of the resulting table (you'll have more columns in the result set for a left/inner/right join), whereas a union is used to combine rows which increases the height of the result set but keeps the column count the same.
BorgWarner is a global automotive industry components and parts supplier. The dataset we have here represents the monthly sales by product for the last year. The "sales" table includes information about the month of sale, product id, quantity sold and the price at which each product was sold. Your task is to write a SQL query using a window function that calculates the average price at which each product was sold per month.
sale_id | month_of_sale | product_id | quantity_sold | sale_price |
---|---|---|---|---|
4543 | 01/2022 | 10001 | 300 | 2000 |
6793 | 01/2022 | 10002 | 250 | 1500 |
3454 | 02/2022 | 10001 | 350 | 2300 |
5234 | 02/2022 | 10002 | 400 | 1800 |
2745 | 03/2022 | 10001 | 200 | 2100 |
8643 | 03/2022 | 10002 | 240 | 1600 |
This PostgreSQL query uses a window function to calculate the average sale price of each product per month.
The clause creates a 'window' of rows for each group defined by the (i.e., each combination of month and product), and then calculates the average sale price within each of these windows.
This results in a table showing the month of sale, product ID, and the average sale price for that product during that month. This information can be very valuable for analyzing sales trends and product performance.
p.s. Window functions show up super often during SQL interviews, so practice the 27+ window function questions on DataLemur
A full outer join returns all rows from both tables, including any unmatched rows, whereas an inner join only returns rows that match the join condition between the two tables.
For a tangible example, suppose you had a table of BorgWarner orders and BorgWarner customers.
Here's a SQL inner join using the orders and customers tables:
This query will return rows from the orders and customers tables that have matching values. Only rows with matching values will be included in the results.
Here is an example of a using the orders and customers tables:
This query will return all rows from both the orders and customers tables, including any rows that do not have matching values. Any rows with null values for either table will be included in the results.
Fun fact: BorgWarner was recognized as one of America's best climate leaders in 2024!
BorgWarner, an automotive industry company, has different parts that it sources from multiple suppliers. These parts are used in various automotive projects. In order to better track and analyze this, you are tasked with creating and querying a database to answer the following:
The relevant tables are , and :
part_id | name | project |
---|---|---|
1 | Engine | Project A |
2 | Gear | Project B |
3 | Brake | Project C |
supplier_id | name |
---|---|
101 | Supplier 1 |
102 | Supplier 2 |
103 | Supplier 3 |
supplier_id | part_id |
---|---|
101 | 1 |
101 | 2 |
102 | 2 |
102 | 3 |
103 | 1 |
Query 1: To find out which parts have the most suppliers.
Query 2: To find out which supplier provides the most unique parts.
First Query
Second Query
The first query joins the and tables and groups the result by , and then calculates the count of suppliers for each part. It finally orders the data in descending order of supplier_count to show the parts with the most suppliers at the top.
The second query joins the and tables. It groups the result by , and then calculates the count of unique parts supplied by each supplier. It finally orders the data in descending order of parts_count, so that the supplier supplying the most unique parts comes first.
The UNION operator combines the results from multiple SELECT statements into a single result set.
Here's an example of using the UNION operator to combine the results of two SELECT statements that retrieve data from tables of BorgWarner's Facebook ads and their Google ads:
This statement would return a combined result set of both Facebook and Google ads that have a spend greater than $200. The result set would include the following columns: ad_id, ad_name, ad_type, impressions, clicks, and spend.
Note that the two SELECT statements within the UNION must have the same number of columns and the columns must have similar data types. In this example, both SELECT statements have the same number of columns and the data types of the columns are all compatible.
At BorgWarner, your job is to analyze the product sales data. Can you write a SQL query to find the average quantity sold of each type of product?
product_id | product_name | sale_date | quantity_sold |
---|---|---|---|
201 | CV Joint | 03/15/2022 | 520 |
202 | Hybrid System | 03/15/2022 | 400 |
203 | Turbo Charger | 03/16/2022 | 350 |
202 | Hybrid System | 03/16/2022 | 300 |
204 | Electric Motor | 03/17/2022 | 250 |
This query groups the sales table by product_name and then for each group (product_name), it calculates the average of quantity_sold.
product_name | avg_quantity_sold |
---|---|
CV Joint | 520.00 |
Hybrid System | 350.00 |
Turbo Charger | 350.00 |
Electric Motor | 250.00 |
This output indicates the average quantity sold for each type of product. For instance, the average quantity sold for the Hybrid System is 350.
To practice a very similar question try this interactive Amazon Highest-Grossing Items Question which is similar for analyzing sales data of products or this Wayfair Y-on-Y Growth Rate Question which is similar for tracking and analyzing product sales growth.
BorgWarner, a preeminent product leader in the powertrain segment, has launched a series of digitally advertised products. As a data analyst, your task is to calculate and analyze the click-through-rates (CTR) from seeing an advertisement to clicking on it, and the conversion rates from clicking on a product advertisement to adding it to the cart.
You are given two tables and . The table shows all the user clicks on the digital advertisements and the table includes the information about the products added to the cart after the user clicked them.
click_id | user_id | click_date | product_id |
---|---|---|---|
5437 | 458 | 12/08/2021 00:00:00 | 3001 |
7843 | 785 | 12/09/2021 00:00:00 | 4001 |
5613 | 321 | 12/18/2021 00:00:00 | 2001 |
9265 | 985 | 12/19/2021 00:00:00 | 3001 |
8417 | 322 | 12/20/2021 00:00:00 | 1001 |
conversion_id | user_id | conversion_date | product_id |
---|---|---|---|
8741 | 458 | 12/08/2021 00:00:00 | 3001 |
9835 | 985 | 12/19/2021 00:00:00 | 3001 |
7624 | 845 | 12/20/2021 00:00:00 | 2001 |
6928 | 658 | 12/22/2021 00:00:00 | 1001 |
7205 | 845 | 12/15/2021 00:00:00 | 4001 |
The above query first calculates the count of clicks and conversions by product in CTEs. Then it performs a LEFT JOIN between the clicks and conversions tables on product_id. CTR and conversion rates are calculated per product. It's assumed we know the total number of impressions (10000 in this case). The conversion rate is the ratio of conversions to clicks by product.
To practice another question about calculating rates, try this TikTok SQL Interview Question on DataLemur's interactive coding environment:
A foreign key is a field in a database table that serves as a reference to the primary key of another table, allowing for the creation of a relationship between the two tables.
For a concrete example, let's inspect employee data from BorgWarner'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, is the primary key, and is used to uniquely identify each row.
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 possible for a table to have multiple foreign keys that reference primary keys in different tables. For instance, the table could have additional foreign keys for the of the department where an employee works and the of the employee's location.
The best way to prepare for a BorgWarner SQL interview is to practice, practice, practice. Besides solving the above BorgWarner SQL interview questions, you should also solve the 200+ SQL exercises on DataLemur which come from companies like Microsoft, Google, Amazon, and tech startups.
Each SQL question has multiple hints, detailed solutions and most importantly, there is an interactive coding environment so you can easily right in the browser your SQL query and have it graded.
To prep for the BorgWarner SQL interview you can also be helpful to solve SQL questions from other automotive companies like:
But if your SQL query skills are weak, don't worry about diving straight into solving questions – go learn SQL with this interactive SQL tutorial.
This tutorial covers SQL concepts such as creating pairs via SELF-JOINs and manipulating string/text data – both of which come up frequently during BorgWarner SQL assessments.
Besides SQL interview questions, the other question categories covered in the BorgWarner Data Science Interview are:
To prepare for BorgWarner Data Science interviews read the book Ace the Data Science Interview because it's got: