11 HelloFresh SQL Interview Questions (Updated 2024)

Updated on

March 2, 2024

At HelloFresh, SQL is typically used for analyzing customer behavior trends and optimizing logistics for fresh food delivery. That's why HelloFresh frequently asks SQL query questions in interviews for Data Science, Data Engineering and Data Analytics jobs.

So, to help you practice for the HelloFresh SQL interview, we've curated 11 HelloFresh SQL interview questions – able to answer them all?

11 HelloFresh SQL Interview Questions

SQL Question 1: Identify the Top Users by Meal Order Quantity

HelloFresh is a meal kit delivery service where customers can order the number of meals they need every week. To understand better about the power users, or VIP users, we want to measure the frequency that a user orders meal kits.

The task is to write a SQL query to find the top 5 users who ordered the meal kits the highest number of times within the month of August 2022.

Assume we have a table with the following schema:

Example Input
1543278908/17/2022 00:00:00600093
1738665408/25/2022 00:00:00701232
1495276108/02/2022 00:00:00600094
1675287608/15/2022 00:00:00701233
1447165408/18/2022 00:00:00701231


Here is the SQL query that solves this problem:

This query starts by filtering out the orders that were placed within the month of August 2022. Then it counts the number of orders grouped by each user. The query finally returns the top 5 users that ordered the most number of times, in a descending order.

To practice a super-customer analysis question on DataLemur's free online SQL code editor, try this Microsoft Azure Cloud SQL Interview Question: Microsoft SQL Interview Question: Super Cloud Customer

SQL Question 2: Calculate the Average Delivery Time for Each Recipe

At HelloFresh, we take our delivery time seriously; both to ensure a good customer experience and to keep the ingredients fresh. We want to determine the average delivery time for each recipe.

For this question, assume we have a "deliveries" table that shows when a recipe was prepared and when it was delivered to the customer. The aim is to write a SQL query to calculate the average delivery time for each recipe.

Example Input:
110Chicken Curry06/07/2022 09:00:0006/07/2022 13:00:00
220Vegetable Stir Fry06/07/2022 09:30:0006/07/2022 13:30:00
310Chicken Curry06/08/2022 09:00:0006/08/2022 12:30:00
430Pasta Bolognese06/09/2022 09:00:0006/09/2022 12:00:00
520Vegetable Stir Fry06/09/2022 09:30:0006/09/2022 13:00:00
Example Output:
10Chicken Curry3.75
20Vegetable Stir Fry4.00
30Pasta Bolognese3.00


The SELECT statement is asking for the recipe_id and recipe_name columns from the "deliveries" table. The AVG clause calculates the average delivery time, which is calculated as the time difference between delivered_time and prepared_time, converted to hours using the EXTRACT(EPOCH FROM INTERVAL) function to get seconds and divided by 3600. This is then grouped by recipe_id and recipe_name, providing the average delivery time for each recipe.

To practice a similar window function question on DataLemur's free online SQL coding environment, solve this Google SQL Interview Question: Google SQL Interview Question

SQL Question 3: Can you explain the purpose of the constraint?

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 HelloFresh's CRM (customer-relationship management) tool.

The FOREIGN KEY constraint ensures that the data in the field of the "opportunities" table is valid, and prevents 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 can be used to ensure that data is not deleted from the accounts table if there are still references to it in the opportunities` table.

HelloFresh SQL Interview Questions

SQL Question 4: Ingredients Inventory Management at HelloFresh

HelloFresh, an international meal-kit company, delivers pre-portioned ingredients and step-by-step recipes directly to customers. They operate a number of warehouses where they store ingredients before they are packed into meal kits.

HelloFresh needs to carefully track their ingredients inventory across these warehouses to ensure that they can fulfill customer orders.

The schema you are provided with contains three tables:

  1. The table tracks unique meal-kit products that HelloFresh offers. The is a unique identifier for each meal-kit product.

  2. The table tracks unique ingredients used in their meal-kits. The is a unique identifier for each.

  3. The table represents a many-to-many relationship between and . Each row represents that an ingredient is used in a certain product, and has a indicating the amount of the ingredient used in that product.

  4. The table keeps track of the quantity of each ingredient available in each warehouse.

The database schema is as follows:

50001'Chicken Alfredo Pasta''Classic chicken Alfredo served over pasta.'
69852'Thai Basil Tofu''Spicy Thai tofu dish served with Jasmine rice.'
25'Chicken Breast''piece'
42'Alfredo Sauce''ml'
37'Thai Basil''leave'
40'Jasmine Rice''g'

Your challenge is to write a SQL query that returns the total number of each product that can currently be made in each warehouse, given the quantities of ingredients available.


This SQL query joins the , and tables through their relationships, using product and ingredient IDs. For each group of warehouses and products, the query calculates the minimum ratio of quantities on hand and the quantities required for the product. This represents the number of product kits that could be made with the available inventory. The final results are sorted by the total number of product kits that can be made in descending order.

SQL Question 5: Why would it make sense to denormalize a database?

Database denormalization is when you add redundancy to a database, and break typical normalization rules (specified by 1st, 2nd, 3rd normal forms). There's a few reasons to denormalize a database:

  • Improved performance: Denormalization often reduces the the number of costly join operations that are needed to retrieve data. This is helpful when the database is being used for OLAP (Online Analytical Processing) use cases, as joins can be expensive and slow.

  • Simplification: Denormalization can also be used to simplify the design of a database by reducing the number of tables and relationships that need to be managed. This can make it easier to understand and maintain the database.

  • Ease of use: Denormalization can also make it easier for users to work with a database by providing them with a more intuitive and straightforward data model.

While denormalization can be a useful tool for improving performance and scalability, it's important to keep in mind that it can make update and delete operations more complex. This is because denormalization can create data duplicates, which can make it harder to maintain data integrity. So, to avoid any headaches, it's a good idea to start with a well-normalized database design and then consider denormalization only if it's absolutely necessary for your specific performance and scalability needs. In other words, denormalization can be a bit of a wild card, so it's best to handle it with care!

SQL Question 6: Filter Active Customers based on their Subscription Status and Orders Quantity

Consider a database for HelloFresh. You are given two tables:

  • Customers table, , where each row represents a unique customer, and
  • Orders table, , where each row represents an order made by a customer.

Table :


Table :


Write a PostgreSQL query that returns the full name and signup date of customers who are currently active and have placed an order of at least 3 meals or more in the past.


This query joins the customers table with the orders table on the field, then filters for only customers who are active () and have placed an order with 3 or more meals in the past (). The result is a list of full names and signup dates of these customers.

SQL Question 7: In the context of databases, what does atomicity, consistency, isolation, and durability mean?

To ensure the reliability and integrity of data, a database management system (DBMS) strives to maintain the ACID properties: Atomicity, Consistency, Isolation, and Durability. To illustrate these concepts, consider the following examples of how ACID properties apply to banking transactions:

  1. Atomicity: A transaction is either completed in full or not at all. For example, if a customer is transferring money between accounts, the transaction should either transfer the entire amount or none at all.
  2. Consistency: A transaction is only allowed to complete if it follows all rules and constraints within the database. For example, if a customer is withdrawing money from an account, the transaction should only be allowed to proceed if there are sufficient funds available. Otherwise, the transaction is rejected.
  3. Isolation: Concurrent transactions are kept separate from each other, so that the changes made by one transaction cannot be seen by another transaction until the first one is complete. This helps prevent conflicts, such as two customers attempting to withdraw money from the same account at the same time.
  4. Durability: Once a transaction has been committed and completed, the changes made by the transaction are permanently stored in the database and will not be lost even if the database or system crashes. For example, if a customer makes a deposit, the transaction should be durable so that the deposit is not lost in the event of a system failure.

SQL Question 8: Average Cost of Meal Per Cuisine

As a part of the Data Analyst team at HelloFresh, your task is to find the average cost of meals provided for different cuisines. The company is particularly interested in knowing if there's a significant price difference among different cuisines. Use the following tables and .

Example Input:
Example Input:
Example Output:


In the solution, we are joining meals and cuisines tables on the common column . After the join, we group the data on the basis of and calculate the average price for each cuisine using the AVG function. The results are aliases as and for readability.

To practice a very similar question try this interactive Amazon Highest-Grossing Items Question which is similar for requiring aggregation for category-like data or this CVS Health Pharmacy Analytics (Part 1) Question which is similar for drawing conclusions from product and profit data.

SQL Question 9: Retrieve Customer Information

HelloFresh has a database of customers, and they would like to retrieve information of customers with email ids that contain 'gmail'. Could you generate a SQL query that performs this operation?

Example Input:


Example Output:



Here is a SQL query that will do this:

This query works by selecting the , , and columns from the table where the contains the term 'gmail'. The '%' character in the LIKE keyword is a wild card that matches any sequence of characters, therefore '%gmail%' will match any email id that contains 'gmail'. This is useful to retrieve information about customers that have a Gmail account.

SQL Question 10: What does do, and when would you use this SQL command?

When using , only rows that are identical in both sets will be returned.

For a concrete example, say you were a Data Analyst supporting the Sales Analytics team at HelloFresh, and data on potential sales leads lived in both Salesforce and Hubspot CRMs. To write a query to analyze leads created before 2023 started, that show up in BOTH CRMs, you would use the command:

SQL Question 11: Analyzing Customer Behavior and Meal Preferences

Using the and tables, your task is to write a query that calculates the most popular meal option sold each month.

Sample Input:
Sample Input:
1057423105/12/2021Chicken Alfredo
2049935003/28/2021Vegetable Lasagna
7396217507/04/2021German Bratwurst
6284987304/18/2021French Coq au Vin
4865322109/01/2021Chicken Caesar Salad

Your query should return the most popular meal option sold each month, for all customers regardless of the country, as the following table:

Example Output:
2021-03Vegetable Lasagna1
2021-04French Coq au Vin1
2021-05Chicken Alfredo1
2021-07German Bratwurst1
2021-09Chicken Caesar Salad1


In this question, we need to join the and tables on their common column, group by the year and month of the purchase, and then count the meals to find out which is most popular each month.

Here's the SQL query for this:

In the above SQL query, takes the purchase date and truncates this to the year-month, so all days within the same month and year are grouped together. simply counts how many of each meal were sold. The at the end ensures that the data is sorted by month first, and then by meal count in descending order, so the most popular meal each month appears first.

Because join questions come up so often during SQL interviews, practice an interactive Spotify JOIN SQL question: SQL join question from Spotify

Preparing For The HelloFresh SQL Interview

The best way to prepare for a HelloFresh SQL interview is to practice, practice, practice. Besides solving the earlier HelloFresh SQL interview questions, you should also solve the 200+ SQL Interview Questions on DataLemur which come from companies like Netflix, Airbnb, and Amazon. DataLemur SQL Interview Questions

Each SQL question has hints to guide you, full answers and best of all, there's an interactive SQL code editor so you can easily right in the browser your SQL query answer and have it executed.

To prep for the HelloFresh SQL interview you can also be helpful to practice SQL problems from other tech companies like:

But if your SQL coding skills are weak, don't worry about diving straight into solving questions – strengthen your SQL foundations with this free SQL for Data Analytics course.

Interactive SQL tutorial

This tutorial covers topics including handling NULLs in SQL and SQL joins with practice exercises – both of which come up routinely in SQL job interviews at HelloFresh.

HelloFresh Data Science Interview Tips

What Do HelloFresh Data Science Interviews Cover?

In addition to SQL interview questions, the other question categories covered in the HelloFresh Data Science Interview are:

  • Statistics and Probability Questions
  • Python or R Programming Questions
  • Product Data Science Interview Questions
  • ML Interview Questions
  • Resume-Based Behavioral Questions

HelloFresh Data Scientist

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