9 Edwards Lifesciences SQL Interview Questions (Updated 2024)
Data Analysts and Data Engineers at Edwards Lifesciences use SQL queries to manage and analyze extensive medical device data, focusing on patient outcomes and device performance metrics. They also rely on SQL to optimize database performance, ensuring that real-time patient monitoring solutions operate smoothly and efficiently, that is why Edwards Lifesciences evaluates jobseekers with SQL interview questions.
Thus, to help prep you for the Edwards Lifesciences SQL interview, here’s 9 Edwards Lifesciences SQL interview questions in this blog.
9 Edwards Lifesciences SQL Interview Questions
SQL Question 1: Analyzing Product Sales and Returns
Edwards Lifesciences is a medical device company primarily focused on products that help treat structural heart disease and critically ill patients. Your task as a data analyst is to investigate two aspects of the company's product sales:
- Calculate the cumulative sales of heart valves by month.
- Analyze the monthly return rate of each product (number of returned units over sold units).
For this task, you are provided with two tables, and .
The table has the following schema:
Example Input:
| order_id | date | product | units_sold |
|---|---|---|---|
| 65431 | 2022-06-01 | Aortic Valve | 30 |
| 65432 | 2022-06-03 | Mitral Valve | 20 |
| 65433 | 2022-06-15 | Aortic Valve | 15 |
| 65434 | 2022-07-02 | Mitral Valve | 35 |
| 65435 | 2022-07-15 | Aortic Valve | 20 |
The table has the following schema:
Example Input:
| return_id | date | product | units_returned |
|---|---|---|---|
| 7651 | 2022-06-05 | Aortic Valve | 10 |
| 7652 | 2022-06-25 | Mitral Valve | 5 |
| 7653 | 2022-07-15 | Aortic Valve | 5 |
| 7654 | 2022-07-20 | Mitral Valve | 10 |
| 7655 | 2022-07-28 | Aortic Valve | 5 |
Calculate the cumulative sales and return rate for each product on a monthly basis.
Answer:
This query first calculates the cumulative sales of each product for each month in the common table expression (CTE). It then calculates the total units returned for each product for each month in the CTE.
The main query then selects the cumulative sales and units returned from both CTEs and calculates the return rate, which is the number of units returned divided by the cumulative sales. The returned data will provide insights into the trend of monthly sales and returns for all products.
p.s. Window functions show up pretty frequently during SQL interviews, so practice the 27+ window function questions on DataLemur
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SQL Question 2: Employees Earning More Than Their Boss
Given a table of Edwards Lifesciences employee salary information, write a SQL query to find employees who make more than their direct boss.
Edwards Lifesciences Example Input:
| employee_id | name | salary | department_id | manager_id |
|---|---|---|---|---|
| 1 | Emma Thompson | 3800 | 1 | |
| 2 | Daniel Rodriguez | 2230 | 1 | 10 |
| 3 | Olivia Smith | 8000 | 1 | 8 |
| 4 | Noah Johnson | 6800 | 2 | 8 |
| 5 | Sophia Martinez | 1750 | 1 | 10 |
| 8 | William Davis | 7000 | 2 | NULL |
| 10 | James Anderson | 4000 | 1 | NULL |
Example Output:
| employee_id | employee_name |
|---|---|
| 3 | Olivia Smith |
This is the output because Olivia Smith earns $8,000, surpassing her manager, William Davis who earns 7,800.
Try this problem interactively on DataLemur:
Answer:
First, we perform a SELF-JOIN where we treat the first table () as the managers' table and the second table () as the employees' table. Then we use a clause to filter the results, ensuring we only get employees whose salaries are higher than their manager's salary.
If the code above is confusing, you can find a detailed solution here: Employee Salaries Higher Than Their Manager.
SQL Question 3: Can you explain what an index is and the various types of indexes?
An index in a database is a data structure that helps to quickly find and access specific records in a table.
For example, if you had a database of Edwards Lifesciences customers, you could create a primary index on the column.
Having a primary index on the column can speed up performance in several ways. For example, if you want to retrieve a specific customer record based on their , the database can use the primary index to quickly locate and retrieve the desired record. The primary index acts like a map, allowing the database to quickly find the location of the desired record without having to search through the entire table.
Additionally, a primary index can also be used to enforce the uniqueness of the column, ensuring that no duplicate values are inserted into the table. This can help to prevent errors and maintain the integrity of the data in the table.
SQL Question 4: Calculate the Average Lifespan of Edwards Lifesciences' Medical Devices
Edwards Lifesciences is a renowned medical equipment manufacturing company. This question will test your ability to utilise the function. You are required to write a SQL query that calculates the average lifespan of Edwards Lifesciences' medical devices.
Assume that you are given a table named which keeps track of each device's , date of manufacturing (), and date of replacement () in format.
Example Input:
| serial_number | manufacture_date | replacement_date |
|---|---|---|
| FW00123 | 2013-06-20 | 2020-07-15 |
| FW00124 | 2014-10-18 | 2021-11-12 |
| FW00125 | 2016-04-21 | 2022-04-20 |
| FW00126 | 2015-01-13 | 2020-06-12 |
| FW00127 | 2017-08-15 | 2022-10-23 |
Answer:
This SQL query calculates the difference in years between the and the for each row in the table using the function. It then calculates the average of these differences using the function, which results in the average lifespan of the medical devices from Edwards Lifesciences.
The function takes the average of the lifespan years of all devices, providing an overall idea of how long these devices usually last. The function extracts the year part from the age interval.
For practicality, the query doesn't include devices that have not yet been replaced, which could skew the average towards a lower lifespan.
The most similar questions are:
- "Average Review Ratings" from Amazon: It also requires the calculation of averages on a particular data set.
- "Compressed Mean" from Alibaba: It involves the calculation of mean which is similar to calculating average.
Here is the markdown with the hyperlinks and reasons:
To practice a very similar question try this interactive Amazon's Average Review Ratings Question which is similar for requiring average calculation on a data set or this Alibaba's Compressed Mean Question which is similar for involving the calculation of mean.
SQL Question 5: Can you provide an example of two entities that have a one-to-one relationship, and another example of a one-to-many relationship?
When designing a database schema, a one-to-one relationship between two entities is characterized by each entity being related to a single instance of the other. An example of this is the relationship between a car and a license plate - each car has one license plate, and each license plate belongs to one car.
On the other hand, a one-to-many relationship is when one entity can be associated with multiple instances of the other entity. For example, a person can have multiple email addresses, but each email address only relates back to one person.
SQL Question 6: Calculate average selling cost per product category
Edwards Lifesciences is a company that primarily deals with medical devices used for heart surgeries. Each product belongs to specific categories like 'Heart Valves', 'Critical Care', etc. You are asked to write a query that groups the sales data by product category and calculates the average sale price per category.
Please consider the below table with sample data.
Example Input:
| sale_id | product_id | sale_date | sale_price | product_category |
|---|---|---|---|---|
| 101 | 50001 | 06/08/2022 | 3000.50 | 'Heart Valves' |
| 102 | 69852 | 06/10/2022 | 4500.00 | 'Critical Care' |
| 103 | 50001 | 06/18/2022 | 3200.75 | 'Heart Valves' |
| 104 | 69852 | 07/26/2022 | 4350.00 | 'Critical Care' |
| 105 | 50001 | 07/05/2022 | 3100.20 | 'Heart Valves' |
Answer:
Example Output:
| product_category | avg_sale_price |
|---|---|
| 'Heart Valves' | 3100.48 |
| 'Critical Care' | 4425.00 |
This query groups the sales by column and for each group, it calculates the average value of the . As such, the output provides an insight into average sale prices across different product categories.
SQL Question 7: What does the constraint do?
A is a field in a table that references the of another table. It creates a link between the two tables and ensures that the data in the field is valid.
Say for example you had sales analytics data from Edwards Lifesciences's CRM (customer-relationship management) tool.
In this example, the table has a foreign key field called that references the field in the table (the primary key). This helps to link the data about each opportunity to the corresponding account information in the table.
This makes sure the insertion of rows in the table that do not have corresponding entries in the table. It also helps to enforce the relationship between the two tables and ensures that data is not deleted from the table if there are still references to it in the table.
SQL Question 8: Join Customer and Products Tables to Analyze Purchases
Assume we have two tables for Edwards Lifesciences: a table, that stores the details of each customer including their id, name, and age, and a table that stores the details of various product purchases, including the product id, the customer id of the buyer, and the purchase date.
Write a SQL query to return all the customers who purchased products in the month of September (09) and display their purchase details. The result should include customer name, product id, and purchase date.
The and tables look like this:
Example Input:
| customer_id | name | age |
|---|---|---|
| 101 | John Doe | 35 |
| 102 | Jane Doe | 30 |
| 103 | Mary Johnson | 40 |
| 104 | James Smith | 45 |
| 105 | Patricia Williams | 50 |
Example Input:
| product_id | customer_id | purchase_date |
|---|---|---|
| 50001 | 101 | 09/01/2022 |
| 69852 | 102 | 09/12/2022 |
| 50001 | 103 | 08/18/2022 |
| 69852 | 104 | 09/26/2022 |
| 69852 | 105 | 07/05/2022 |
Answer:
In this query, we're joining the table with the table based on the . This allows us to pair up each purchase with the customer who made it. We then filter the results to only include purchases made in September, by using the function to pull out the month of each purchase date and compare it to 9. The resulting rows include the customer's name, the product id they purchased, and when they purchased it.
Since join questions come up routinely during SQL interviews, try this interactive Snapchat JOIN SQL interview question:
SQL Question 9: Calculate Product Markup Ratio
As part of the product and sales analysis at Edwards Lifesciences, you are tasked to calculate the price markup ratio of each product sold. The markup ratio is calculated as ([Selling Price] - [Cost Price]) / [Cost Price].
You have access to two tables:
- table provides details about products including their cost price.
- table provides details about the sales made including the selling price of each product sold.
The sample data in the tables is given below:
Example Input:
| product_id | cost_price |
|---|---|
| 50001 | 3000 |
| 69852 | 1500 |
| 89123 | 2000 |
| 74563 | 3700 |
| 98732 | 4600 |
Example Input:
| sale_id | product_id | selling_price |
|---|---|---|
| 6171 | 50001 | 3500 |
| 7802 | 69852 | 2000 |
| 5293 | 89123 | 2500 |
| 6352 | 74563 | 4000 |
| 4517 | 98732 | 5500 |
Write a SQL query to calculate the markup ratio, rounding it to 2 decimal places for each product based on the selling price from the table and the cost price from the table. Be sure to multiply the result by 100 to get a percentage value.
Example Output:
| product_id | markup_ratio |
|---|---|
| 50001 | 16.67 |
| 69852 | 33.33 |
| 89123 | 25.00 |
| 74563 | 8.11 |
| 98732 | 19.57 |
Answer:
This query first joins the and tables on the product_id column. Then, for each resulting row, it subtracts the cost price from the selling price, divides by the cost price, and multiplies by 100 to get the markup ratio as a percentage. This ratio is rounded to 2 decimal places for convenience.
To practice a very similar question try this interactive Amazon Highest-Grossing Items Question which is similar for product sales analysis or this CVS Health Pharmacy Analytics (Part 1) Question which is similar for profit calculations.
How To Prepare for the Edwards Lifesciences SQL Interview
Assuming that you've already got basic SQL skills, the next best tip we have to prepare for the Edwards Lifesciences SQL interview is to solve as many practice SQL interview questions as you can! In addition to solving the above Edwards Lifesciences 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 problem on DataLemur has multiple hints, full answers and crucially, there's an interactive SQL code editor so you can easily right in the browser your query and have it checked.
To prep for the Edwards Lifesciences SQL interview you can also be useful to solve SQL problems from other healthcare and pharmaceutical companies like:
In case your SQL skills are weak, forget about jumping right into solving questions – improve your SQL foundations with this free SQL for Data Analytics course.
This tutorial covers SQL concepts such as filtering data with boolean operators and Subquery vs. CTE – both of which pop up often during SQL interviews at Edwards Lifesciences.
Edwards Lifesciences Data Science Interview Tips
What Do Edwards Lifesciences Data Science Interviews Cover?
Besides SQL interview questions, the other topics covered in the Edwards Lifesciences Data Science Interview include:
- Stats Interview Questions
- Python or R Coding Questions
- Business Sense and Product-Sense Questions
- Machine Learning Questions
- Behavioral Interview Questions centered on Edwards Lifesciences culture and values
How To Prepare for Edwards Lifesciences Data Science Interviews?
To prepare for Edwards Lifesciences Data Science interviews read the book Ace the Data Science Interview because it's got:
- 201 interview questions sourced from companies like Google, Tesla, & Goldman Sachs
- a crash course covering Product Analytics, SQL & ML
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Don't ignore the behavioral interview – prep for it using this Behavioral Interview Guide for Data Scientists.