8 Omnicell SQL Interview Questions (Updated 2024)
At Omnicell, SQL is used day-to-day for extracting and analyzing patient medication data, and managing inventory databases for healthcare automation technologies. So, it shouldn't surprise you that Omnicell frequently asks SQL coding questions during interviews for Data Science, Analytics, and & Data Engineering jobs.
So, to help you practice, we've curated 8 Omnicell SQL interview questions – how many can you solve?
8 Omnicell SQL Interview Questions
SQL Question 1: Calculate Monthly Average Rating for Each Product
Omnicell, as a healthcare improvement company, regularly collects indirect feedback about its products from different end users like hospitals, clinics, and pharmacies. This feedback is in the form of star ratings (ranging from 1 to 5) given to each product every time a purchase is made.
The dataset for this query includes a table, which consists of several columns: , , , , and (the rating).
Your task is to write a SQL query that will calculate the monthly average star rating for each product.
Example Input:
| purchase_id | user_id | purchase_date | product_id | stars |
|---|---|---|---|---|
| 1001 | 10 | 2022-01-05 | 20001 | 3 |
| 1002 | 20 | 2022-01-08 | 30001 | 4 |
| 1003 | 30 | 2022-02-02 | 20001 | 5 |
| 1004 | 40 | 2022-02-12 | 30001 | 2 |
| 1005 | 50 | 2022-03-09 | 30001 | 3 |
Example Output:
| month | product_id | avg_stars |
|---|---|---|
| 1 | 20001 | 3.0 |
| 1 | 30001 | 4.0 |
| 2 | 20001 | 5.0 |
| 2 | 30001 | 2.0 |
| 3 | 30001 | 3.0 |
Answer:
This PostgreSQL query extracts the month from the column using . It then groups the records by this computed month and the to calculate the average rating for each product per month using . Records in the result set are ordered by and .
To solve a related window function SQL problem on DataLemur's free online SQL coding environment, try this Amazon SQL question asked in a BI Engineer interview:
SQL Question 2: Department vs. Company Salary
You're given a table of Omnicell employee and department salary information. Write a SQL query to find departments where the departments average salary is higher than the company's average salary.
You can solve this problem directly within the browser on DataLemur:
The solution is LONG – 30+ lines of SQL. You can find a detailed solution here: Department Salaries.
Read about how Healthcare and Data Science are connected.
SQL Question 3: Can you provide an example of two entities that have a one-to-one relationship, and another example of a one-to-many relationship?
In database schema design, a one-to-one relationship is when each entity is associated with only one instance of the other. For instance, a US citizen's relationship with their social-security number (SSN) is one-to-one because each citizen can only have one SSN, and each SSN belongs to one person.
A one-to-many relationship, on the other hand, is when one entity can be associated with multiple instances of the other entity. An example of this is the relationship between a person and their email addresses - one person can have multiple email addresses, but each email address only belongs to one person.
SQL Question 4: Retrieve High Value Customers
At Omnicell, we want to identify our high-value customers to implement a reward program. In our context, a high-value customer is defined as a customer that has made total purchases over $1000 in a single month.
Our records are kept in two tables- and . The table contains information about each purchase including the customer_id, date of purchase and amount spent; while contains personal details of the customers such as their first and last names.
Example Input
| purchase_id | customer_id | purchase_date | amount_spent |
|---|---|---|---|
| 1123 | 345 | 06/10/2022 | 120 |
| 2563 | 456 | 06/14/2022 | 180 |
| 3245 | 678 | 06/22/2022 | 350 |
| 4367 | 345 | 06/24/2022 | 950 |
| 5689 | 456 | 06/29/2022 | 900 |
Example Input
| customer_id | first_name | last_name |
|---|---|---|
| 345 | John | Doe |
| 456 | Jane | Doe |
| 678 | Sam | Smith |
In this SQL query, try to extract the first name, last name, month of purchase, and total amount spent for customers who have made total purchases over $1000 within a single month.
Answer:
This query will join the and tables on the customer_id column, and aggregate the amount_spent by customer and month. It then filters for those whose total spending is over $1000. The function is used to get the month from the purchase_date.
SQL Question 5: Could you explain what a self-join is and provide examples of when it might be used?
A self-join is a type of JOIN where a table is joined to itself. To execute a self-join, you must include the table name twice in the FROM clause and assign a different alias to each instance. You can then join the two copies of the table using a JOIN clause, and use a WHERE clause to specify the relationship between the rows.
For instance, suppose you had website visitor data for Omnicell, exported from the company's Google Analytics account. To assist the web-dev team, you needed to analyze pairs of pages for UX or navigational issues. As part of that analysis, you wanted to generate all pairs of URLs, but needed to exclude pairs where both URLs were the same since that is not a valid pair."
You could use the following self-join:
This query returns the url of each page () along with the url of the page that referred to it (). The self-join is performed using the field, which specifies the id of the page that referred the visitor to the current page, and avoids any pages that referred themself (aka data anomalies).
SQL Question 6: Average Usage Time of Omnicell Machines
You are given a database of all the uses of Omnicell machines within multiple healthcare facilities over a year. For each use, you have the timestamp of when the device was switched on and off. You are tasked with finding out the average usage time of these machines per day.
You may assume that the month, day, start and end times are all within the same day and are correctly ordered.
Example Input:
| usage_id | machine_id | date | start_time | end_time |
|---|---|---|---|---|
| 9012 | 128 | 01/20/2021 | 06:00:00 | 07:00:00 |
| 3205 | 702 | 01/20/2021 | 08:00:00 | 10:30:00 |
| 1019 | 501 | 01/21/2021 | 07:00:00 | 09:00:00 |
| 8401 | 702 | 01/21/2021 | 14:00:00 | 16:30:00 |
| 6701 | 128 | 01/22/2021 | 09:30:00 | 11:30:00 |
Consider that start_time and end_time columns are in TIME(0) format and date is in DATE format in PostgreSQL.
Answer:
This query uses PostgreSQL's function to convert the difference between start_time and end_time into seconds. Then it calculates the average of these differences (i.e., the usage times) per date by using the aggregate function grouped by each date. The result is the average time a machine was used each day in seconds.
To practice a very similar question try this interactive Amazon Server Utilization Time Question which is similar for having to calculate total usage time or this Snapchat Sending vs. Opening Snaps Question which is similar for needing to assess time spent on a device.
SQL Question 7: What is the difference between a correlated subquery and non-correlated subquery?
A non-correlated sub-query is not linked to the outer query and can be run on its own. It does not reference any columns from the outer query and is used to retrieve additional data for the outer query. On the other hand, a correlated sub-query is one that relies on the outer query and cannot be run independently. It uses the outer query to filter or transform data by referencing a column from it, while the outer query uses the results of the inner query.
Here is an example of a non-correlated sub-query:
The sub-query in this case is non-correlated, as it does not reference any columns from the outer query.
Here is an example of a correlated sub-query:
This query selects the and total sales of all Omnicell customers in the sales table whose are greater than the average of their own customer group. The sub-query in this case is correlated with the outer query, as it references the column from the outer query ().
Non-correlated sub-queries are typically faster, as they only have to be executed once, wheareas correlated sub-queries are slower since they have to be re-evaluated for each row of the outer query.
SQL Question 8: Filter Customer Data Based on Email Domain
As a data analyst at Omnicell, you are tasked with gleaning insights from our customer database. One request from the marketing team is to find all customers who are using business email addresses, specifically those from the domain "@omnicell.com".
Example Input:
| customer_id | first_name | last_name | |
|---|---|---|---|
| 001 | John | Doe | johndoe@gmail.com |
| 002 | Jane | Doe | janedoe@omnicell.com |
| 003 | Alice | Smith | alicesmith@hotmail.com |
| 004 | Bob | Johnson | bob.johnson@omnicell.com |
| 005 | Charlie | Brown | charliebrown@yahoo.com |
Answer:
Example Output:
| customer_id | first_name | last_name | |
|---|---|---|---|
| 002 | Jane | Doe | janedoe@omnicell.com |
| 004 | Bob | Johnson | bob.johnson@omnicell.com |
This query uses the keyword and a pattern matching string to filter down to only the customers who have an email address that ends with "@omnicell.com". The '%' character is a wildcard character that matches any sequence of characters. Thus, the condition will be true for any email that ends with "@omnicell.com".
How To Prepare for the Omnicell SQL Interview
Assuming that you've already got basic SQL skills, the next best tip we have to prepare for the Omnicell SQL interview is to solve as many practice SQL interview questions as you can!
Beyond just solving the above Omnicell SQL interview questions, you should also solve the 200+ SQL Interview Questions on DataLemur which come from companies like Google, Facebook, Microsoft and Amazon.
Each exercise has multiple hints, fully explained answers along with a discussion board to see how others solved it and most importantly, there's an interactive coding environment so you can instantly run your SQL query answer and have it executed.
To prep for the Omnicell SQL interview you can also be helpful to solve SQL questions from other tech companies like:
In case your SQL skills are weak, don't worry about diving straight into solving questions – refresh your SQL knowledge with this DataLemur SQL tutorial.
This tutorial covers SQL topics like LEAD window function and RANK vs. DENSE RANK – both of these show up frequently during Omnicell SQL assessments.
Omnicell Data Science Interview Tips
What Do Omnicell Data Science Interviews Cover?
In addition to SQL interview questions, the other types of problems covered in the Omnicell Data Science Interview include:
- Statistics and Probability Questions
- Python or R Coding Questions
- Product Analytics Questions
- Machine Learning Questions
- Resume-Based Behavioral Questions
How To Prepare for Omnicell Data Science Interviews?
I'm a tad biased, but I believe the optimal way to study for Omnicell Data Science interviews is to read my book Ace the Data Science Interview.
The book has 201 data interview questions sourced from tech companies like Google & Microsoft. It also has a crash course covering Product Analytics, 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.