At Soitec, SQL is used frequently for analyzing performance patterns in semiconductor manufacturing and managing supply-chain databases. Unsurprisingly this is why Soitec asks SQL problems during interviews for Data Science, Data Engineering and Data Analytics jobs.
To help you ace the Soitec SQL interview, we'll cover 10 Soitec SQL interview questions – can you solve them?
Soitec is a leading company specialized in designing and manufacturing innovative semiconductor materials. They have a large customer base who purchase their products, and they want to identify the users who frequently purchase their high-cost items.
They would like you to write a SQL query to find their 'whale' users - the top 5 customers who have spent the most money on Soitec's top 10 most expensive products in the last year.
order_id | user_id | order_date | product_id | quantity | unit_price |
---|---|---|---|---|---|
1001 | 101 | 2022-03-15 | 20001 | 5 | 100.00 |
1002 | 202 | 2022-04-17 | 30002 | 2 | 200.00 |
1003 | 303 | 2022-05-01 | 20001 | 1 | 100.00 |
1004 | 101 | 2022-07-20 | 30002 | 3 | 200.00 |
1005 | 505 | 2022-02-09 | 40004 | 1 | 500.00 |
product_id | product_name | product_category |
---|---|---|
20001 | Product A | Category 1 |
30002 | Product B | Category 2 |
40004 | Product C | Category 3 |
In this query, we first identify the top 10 expensive products. Then we calculate the total spending of each user for these high-value products in the past year and select the top 5 users.
To practice a super-customer analysis question on DataLemur's free interactive coding environment, try this Microsoft SQL Interview problem:
Soitec is a French company specializing in producing innovative semiconductor materials. Suppose you work for Soitec and you're doing an analysis on their products. They maintain a table of all their product sales, which includes the date of sale, product_id and the revenue from each sale.
The table "sales" looks like this:
sale_id | sale_date | product_id | revenue |
---|---|---|---|
001 | 2021-08-11 | 101 | 1200 |
002 | 2021-08-11 | 102 | 1600 |
003 | 2021-09-19 | 101 | 1400 |
004 | 2021-10-11 | 102 | 1300 |
005 | 2021-10-12 | 101 | 1500 |
You've been tasked to write a SQL query to calculate the average monthly revenue for each product. This will use window functions to divide the data into partitions for analysis.
Here is the PostgreSQL query that would solve this:
In this query, we're using the function to group the dates by month. Then, we use the function with a window function, partitioning by both the and the to get the average revenue for each product per month. The at the end sorts the output by and , making the results easier to read.
This SQL WINDOW function serves a potential interview question that requires a good understanding of how to use these types of functions to partition data. It will help the Soitec team in making strategic decisions based on product performance over time.
p.s. Window functions show up pretty often during SQL interviews, so practice the 27+ window function questions on DataLemur
Similar to the and / operators, the PostgreSQL INTERSECT operator combines result sets of two or more statements into a single result set. However, only returns the rows that are in BOTH select statements.
For a concrete example, say you were on the Sales Analytics team at Soitec, and had data on sales leads exported from both HubSpot and Salesforce CRMs in two different tables. To write a query to analyze leads created after 2023 started, that show up in both CRMs, you would use the command:
Soitec operates a large number of machinery for semiconductor manufacturing. Records of each machine and their maintenance request history are important for the operation team.
For this exercise, two tables are required: table storing each machine's details and table for logging maintenance requests.
Please design the schema for the tables, and using that design, answer the following question:
This problem is designed to test understanding of JOINs and aggregate functions.
machine_id | type | location |
---|---|---|
1001 | TypeA | FactoryA |
1002 | TypeB | FactoryB |
1003 | TypeA | FactoryC |
1004 | TypeC | FactoryA |
1005 | TypeB | FactoryB |
maintain_id | machine_id | request_date |
---|---|---|
1 | 1001 | 08/25/2022 00:00:00 |
2 | 1002 | 08/10/2022 00:00:00 |
3 | 1002 | 08/15/2022 00:00:00 |
4 | 1001 | 08/20/2022 00:00:00 |
5 | 1001 | 08/25/2022 00:00:00 |
6 | 1001 | 08/30/2022 00:00:00 |
7 | 1002 | 08/15/2022 00:00:00 |
8 | 1004 | 08/12/2022 00:00:00 |
This query first joins the and tables on the column. It then groups the result by and , counts the number of maintenance requests for each machine in the last month, and filters this result to only show machines with more than 3 maintenance requests.
To find records in one table that aren't in another, you can use a and check for values in the right-side table.
Here is an example using two tables, Soitec employees and Soitec managers:
This will return all rows from Soitec employees where there is no matching row in managers based on the column.
You can also use the operator in PostgreSQL and Microsoft SQL Server to return the records that are in the first table but not in the second. Here is an example:
This will retrieve all rows from employees that do not appear in managers. The operator works by retreivingthe rows that are returned by the first query, but not by the second.
Please note that is not supported by all DBMS systems, such as MySQL and Oracle (however, you can use the operator to achieve a similar outcome).
Soitec is an international microelectronics company. They have customers all over the world and a variety of different products they sell. The company is planning a targeted marketing campaign, and they need to segment their customers based on location and total spend on Soitec products.
You are provided with two tables:
table that has information about the customers and their location:
customer_id | first_name | last_name | location |
---|---|---|---|
101 | John | Doe | USA |
102 | Mario | Rossi | Italy |
103 | Juan | Perez | Spain |
104 | Maria | Silva | Brazil |
table that has information about customer orders:
order_id | customer_id | product_name | amount |
---|---|---|---|
1 | 101 | Silicon On Insulator | 500 |
2 | 102 | FD-SOI | 300 |
3 | 103 | FD-SOI | 400 |
4 | 104 | Silicon On Insulator | 300 |
5 | 101 | Silicon On Insulator | 400 |
6 | 102 | FD-SOI | 300 |
7 | 103 | Silicon On Insulator | 500 |
8 | 104 | Silicon On Insulator | 400 |
Write an SQL query that filters the customers based on the following conditions:
This query joins the and tables on the field. It then uses the clause to filter customers who are located in or , and who ordered the product . The and clauses are used to compute for each customer and filter those who spent .
The clause in SQL allows you to select records that are unique, eliminating duplicates.
For example, if you had a table of Soitec employees, and wanted to see what are all the unique job titles that currently worked at the company, you could write the following query:
Your task is to analyze the click-through conversion rates for Soitec, a company selling technological materials. They are particularly interested to know the rate of users who view a product to adding the product to their cart.
Consider the tables and , which represent the users viewing a product and the users adding a product to their cart, respectively. Compute the click-through conversion rate per product, defined as total cart additions divided by the total product views.
view_id | user_id | view_date | product_id |
---|---|---|---|
1000 | 123 | 06/08/2022 | 50001 |
1001 | 265 | 06/10/2022 | 50001 |
1002 | 362 | 06/18/2022 | 69852 |
1003 | 192 | 07/26/2022 | 69852 |
1004 | 981 | 07/05/2022 | 69852 |
addition_id | user_id | addition_date | product_id |
---|---|---|---|
2000 | 123 | 06/08/2022 | 50001 |
2001 | 365 | 06/12/2022 | 50001 |
2002 | 362 | 06/18/2022 | 69852 |
2003 | 198 | 07/26/2022 | 69852 |
2004 | 981 | 07/05/2022 | 69852 |
This PostgreSQL query first creates two CTEs to find the view count and addition count for each product. It then joins these two CTEs on the product_id to compute the click-through rate by dividing the addition_count (from ) by view_count (from ).
To solve a related problem on DataLemur's free interactive SQL code editor, solve this Facebook SQL Interview question:
Suppose Soitec wants to analyze its customer base. Specifically, they want to understand the average number of orders per customer. They have a table that stores information about each customer, and an table that stores information about each order. Each customer can have multiple orders. Can you write a SQL query to join the two tables and calculate the average number of orders per customer?
Here's the sample data:
customer_id | first_name | last_name |
---|---|---|
1 | John | Doe |
2 | Jane | Smith |
3 | Jim | Brown |
4 | Julie | Green |
order_id | customer_id | order_date | product_id | quantity |
---|---|---|---|---|
1 | 1 | 06/01/2022 00:00:00 | 1001 | 2 |
2 | 2 | 06/01/2022 00:00:00 | 1002 | 1 |
3 | 1 | 06/02/2022 00:00:00 | 1001 | 1 |
4 | 2 | 06/03/2022 00:00:00 | 1002 | 2 |
5 | 3 | 06/04/2022 00:00:00 | 1002 | 1 |
Here's how you can solve this problem using PostgreSQL:
This query joins the table and the table on the column. Then it calculates the average (AVG) number of orders per customer. It groups the results by , , and to ensure that the average is calculated separately for each customer.
Since join questions come up routinely during SQL interviews, try this Spotify JOIN SQL question:
For a tangible example of in PostgreSQL, suppose you were doing an HR Analytics project for Soitec, and had access to Soitec's contractors and employees 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 never were a employee using this query:
Note that is available in PostgreSQL and SQL Server, while is the equivalent operator which is available in MySQL and Oracle (but don't worry about knowing which RDBMS supports which exact commands since Soitec interviewers aren't trying to trip you up on memorizing SQL syntax).
The key to acing a Soitec SQL interview is to practice, practice, and then practice some more! Beyond just solving the above Soitec SQL interview questions, you should also solve the 200+ SQL questions from real Data Science & Analytics interviews which come from companies like Facebook, Google, and VC-backed startups.
Each SQL question has hints to guide you, step-by-step solutions and most importantly, there is an online SQL coding environment so you can instantly run your SQL query and have it checked.
To prep for the Soitec SQL interview it is also wise to solve interview questions from other tech companies like:
But if your SQL foundations are weak, forget about jumping right into solving questions – refresh your SQL knowledge with this DataLemur SQL Tutorial.
This tutorial covers topics including using ORDER BY and removing NULLs – both of which pop up routinely during Soitec interviews.
Beyond writing SQL queries, the other topics to prepare for the Soitec Data Science Interview are:
To prepare for Soitec Data Science interviews read the book Ace the Data Science Interview because it's got: