Data Analysts and Data Engineers at Kraft Heinz use SQL for analyzing sales data to find market trends, allowing them to adjust their strategies based on consumer preferences. They also manage supply chain data through SQL to maintain efficient inventory control, ensuring that products are available when they are needed, this is why Kraft Heinz ask SQL problems during interviews.
So, to help you study, we've curated 9 Kraft Heinz SQL interview questions – can you answer each one?
As part of Kraft Heinz's product marketing team, you have been asked to analyze the feedback data received from customers. Specifically, the team wants to understand the average ratings of each product on a monthly basis to identify trends or problematic issues that might need to be addressed. You decide to use SQL window functions to solve this problem.
review_id | user_id | submit_date | product_id | stars |
---|---|---|---|---|
6171 | 123 | 2022-06-08 | 50001 | 4 |
7802 | 265 | 2022-06-10 | 69852 | 4 |
5293 | 362 | 2022-06-18 | 50001 | 3 |
6352 | 192 | 2022-07-26 | 69852 | 3 |
4517 | 981 | 2022-07-05 | 69852 | 2 |
month | product_id | avg_stars |
---|---|---|
6 | 50001 | 3.5 |
6 | 69852 | 4.0 |
7 | 69852 | 2.5 |
This SQL query uses window function . It calculates the average () stars of the same product () in the same month (). The clause is used to arrange the output by month and product_id, making it easier to read.
p.s. Window functions show up super often during SQL interviews, so practice the 27+ window function questions on DataLemur
Assume there was a table of Kraft Heinz employee salaries. Write a SQL query to find the employees who earn more than their direct manager.
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 |
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.
Check your SQL query for this question and run your code right in DataLemur's online SQL environment:
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 solution above is confusing, you can find a step-by-step solution here: Employee Salaries Higher Than Their Manager.
To explain the difference between a primary key and foreign key, let's inspect employee data from Kraft Heinz's HR database:
Here’s the table reformatted to match your example:
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, could be a primary key. It is unique for each row in the table and cannot contain null values.
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 also possible for a table to have multiple foreign keys that reference different primary keys in other tables. For example, the table could have additional foreign keys for the of the department where each employee works, and the l of the location where each employee is based.
As a Kraft Heinz data specialist, you have been tasked with managing the company's product inventory. The company wants to keep track of each product’s inventory status over time. Your task is to model the tables, define the relationships, and choose the appropriate columns for these tables.
Furthermore, Kraft Heinz wants to know the inventory status of their products by the end of each month. The inventory status should include product name, quantity of product left in inventory, and whether the inventory level for the product is LOW (less than 10 items), MEDIUM (between 10 and 50 items), or HIGH (more than 50 items).
Assume the following tables:
product_id | product_name |
---|---|
1001 | Kraft Mac & Cheese |
1002 | Heinz Ketchup |
1003 | Planters Peanuts |
inventory_id | product_id | date | quantity |
---|---|---|---|
1 | 1001 | 2022-06-30 | 60 |
2 | 1002 | 2022-06-30 | 15 |
3 | 1003 | 2022-06-30 | 5 |
4 | 1001 | 2022-07-31 | 70 |
5 | 1002 | 2022-07-31 | 20 |
6 | 1003 | 2022-07-31 | 8 |
This query joins the and tables on . It formats the column to show only year and month (). It also creates a new column that categorizes the quantity into 'LOW', 'MEDIUM', or 'HIGH'. This output provides the inventory status by month for each product, which can help Kraft Heinz with managing their product inventory effectively.
A constraint ensures that all values in a column are different. It is often used in conjunction with other constraints, such as NOT NULL, to ensure that the data meets certain conditions.
For example, if you had Kraft Heinz sales leads data stored in a database, here's some constraints you'd use:
In this example, the constraint is applied to the and fields to ensure that each Kraft Heinz lead has a unique email address and phone number. This helps to ensure the integrity of the data in the database and prevents errors that could occur if two leads had the same email address or phone number.
As a data analyst at Kraft Heinz, you have been asked to determine the average selling price of items in each product category for the first quarter of the year 2022. Kraft Heinz has a broad range of products and they would like to understand the average sales price on a per category basis to inform their pricing strategies.
product_id | productname | category |
---|---|---|
101 | Ketchup | Condiments |
102 | Mustard | Condiments |
103 | Mac & Cheese | Prepared Foods |
104 | Almonds | Snacks |
sales_id | product_id | sales_date | selling_price |
---|---|---|---|
201 | 101 | 01/15/2022 | 2.5 |
202 | 102 | 02/18/2022 | 2 |
203 | 101 | 03/30/2022 | 2.5 |
204 | 103 | 01/23/2022 | 3.5 |
205 | 104 | 01/12/2022 | 5 |
206 | 104 | 02/19/2022 | 4.5 |
207 | 104 | 03/29/2022 | 5 |
This SQL query joins the and tables using the field. It then restricts the data to only sales made in the first quarter of 2022 using the function, then it groups rows by product and calculates the average selling price of products by category. The final result would list down product categories along with their average selling price in the specified period.
To practice a very similar question try this interactive Amazon Highest-Grossing Items Question which is similar for calculating metrics for categories or this Amazon Average Review Ratings Question which is similar for aggregating data by time and category.
Both the and window functions are used to access a row at a specific offset from the current row.
However, the function retrieves a value from a row that follows the current row, whereas the function retrieves a value from a row that precedes the current row.
Say you had a table of salary data for Kraft Heinz employees:
name | salary |
---|---|
Amanda | 130000 |
Brandon | 90000 |
Carlita | 80000 |
You could use the function to output the salary of each employee, along with the next highest-paid employee:
This would yield the following output:
name | salary | next_salary |
---|---|---|
Amanda | 130000 | 90000 |
Brandon | 90000 | 80000 |
Carlita | 80000 | NULL |
Swapping for would get you the salary of the person who made just more than you:
name | salary | next_salary |
---|---|---|
Amanda | 130000 | NULL |
Brandon | 90000 | 130000 |
Carlita | 80000 | 90000 |
Given a database of sales for Kraft Heinz, you are asked to determine the average number of sales per product on a monthly basis.
Please see below for the format of the table:
sale_id | date_sale | product_id | units_sold |
---|---|---|---|
4352 | 01/03/2022 | 0180 | 10 |
4563 | 01/10/2022 | 0180 | 15 |
3231 | 01/15/2022 | 8942 | 20 |
6543 | 02/06/2022 | 8942 | 5 |
8793 | 02/12/2022 | 0180 | 7 |
9762 | 03/03/2022 | 0180 | 20 |
2132 | 03/10/2022 | 8942 | 15 |
We want to find the average units sold for each product on a monthly basis.
month | product | avg_units_sold |
---|---|---|
1 | 0180 | 12.5 |
1 | 8942 | 20.0 |
2 | 0180 | 7.0 |
2 | 8942 | 5.0 |
3 | 0180 | 20.0 |
3 | 8942 | 15.0 |
The above query uses the function to extract the month from the column. It then groups the data by product id and month, and finally computes the average units sold per grouping. The result is sorted by month then product for easier readability.
As a data analyst at Kraft Heinz, you are tasked to analyze the transaction amount for each customer and merge it with customer details. The table includes information on each transaction the company has processed and the table contains the details on each customer. Given the following data, write a SQL query to show the total monetary amount a customer has spent, joining both tables.
transaction_id | customer_id | amount_spent |
---|---|---|
1 | 1 | 60.5 |
2 | 2 | 95.7 |
3 | 1 | 50.0 |
4 | 3 | 200.3 |
5 | 2 | 45.2 |
customer_id | first_name | last_name | |
---|---|---|---|
1 | John | Doe | john.doe@example.com |
2 | Jane | Smith | jane.smith@example.com |
3 | Bob | Johnson | bob.johnson@example.com |
This PostgreSQL query first joins the and tables on the column. Then, it groups by the customer details and sums the to get the total monetary amount each customer has spent.
Since join questions come up so often during SQL interviews, try this Snapchat Join SQL question:
The best way to prepare for a SQL interview, besides making sure you have strong SQL fundamentals, is to practice a ton of real SQL questions that were asked in recent job interviews. Beyond just solving the above Kraft Heinz SQL interview questions, you should also solve the 200+ DataLemur interview questions which come from companies like Netflix, Airbnb, and Amazon.
Each exercise has multiple hints, step-by-step solutions and most importantly, there's an online SQL code editor so you can right online code up your SQL query answer and have it graded.
To prep for the Kraft Heinz SQL interview you can also be wise to solve SQL questions from other food and facilities companies like:
However, if your SQL coding skills are weak, don't worry about jumping right into solving questions – go learn SQL with this DataLemur SQL tutorial.
This tutorial covers things like filtering groups with HAVING and window functions – both of these come up often in Kraft Heinz SQL interviews.
Beyond writing SQL queries, the other types of questions covered in the Kraft Heinz Data Science Interview are:
To prepare for Kraft Heinz Data Science interviews read the book Ace the Data Science Interview because it's got:
Don't ignore the behavioral interview – prepare for that using this list of behavioral interview questions for Data Scientists.