11 Dayforce SQL Interview Questions (Updated 2025)

At Dayforce, SQL is used for extracting and analyzing HR/payroll data for client insights, and maintaining data accuracy in real-time workforce management solutions. That's why Dayforce LOVES to ask SQL problems during interviews for Data Science and Data Engineering positions.

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

11 Dayforce SQL Interview Questions

SQL Question 1: Calculate Average Employee Salary by Departments using Window functions

Dayforce is a human capital management platform, one of the common datasets might be related to employee salaries and department details. Let's say we have two tables employee and department.

Consider the following tables:

Employee Example Input:

Department Example Input:

The requirement here is that we need to write a SQL query which gives the average salary of each department. The result should be sorted by average salary in descending order.

Answer:

SELECT 
    D.dep_name AS Department, 
    ROUND(AVG(E.salary) OVER (PARTITION BY E.dep_id), 2) AS Average_Salary
FROM 
    Employee E
INNER JOIN 
    Department D ON E.dep_id = D.dep_id
ORDER BY 
    Average_Salary DESC;

This SQL block first joins the Employee and Department tables on the department ID (dep_id). Next, a window function is applied to calculate the average salary of employees partitioned by department ID (dep_id). The ROUND function is used to round the average salary to two decimal points. Finally, the result is ordered by Average_Salary in descending order, so that the department with the highest average salary comes up on top.

p.s. Window functions show up super often during SQL interviews, so practice the 27+ window function questions on DataLemur

SQL Question 2: Design a Database for Dayforce Employee Management System

Dayforce is a global human capital management software company that provides HR, Payroll, Benefits, Workforce, and Talent management solutions for businesses. For the purpose of this question, assume you are asked to design a database to manage employee data for a Dayforce customer company. The key entities would include Employees, Departments, and Roles within the company.

Your database should be capable of tracking the following:

1. Employee data including employee id, name, email, department, and role
2. Department data including department id and department name
3. Role data including role id, role name, and department id

Let's assume some sample data:

**employees** Example Input:

**departments** Example Input:

**roles** Example Input:

Here's a question based on the above:

What is the distribution of employees across different roles in the Engineering department?

Answer:

SELECT 
    roles.role_name,
    COUNT(employees.role_id) as employee_count
FROM 
    employees 
JOIN 
    roles ON employees.role_id = roles.role_id
JOIN 
    departments ON employees.department_id = departments.department_id
WHERE 
    departments.department_name = 'Engineering'
GROUP BY 
    roles.role_name;

The SQL query above joins the employees, roles, and departments tables to identify the roles and employee count within the Engineering department. This information is vital for human resource management and work-force planning.

SQL Question 3: What is denormalization, and in what situations might it be a useful?

Denormalization is the practice of altering a database schema in a way that breaks the normalization rules (1st, 2nd, 3rd normal forms).

For example, in a database that stores Dayforce sales analytics data, you might have separate tables for "customers," "orders," and "products," with foreign key constraints linking the tables together. This helps to ensure the integrity of the data and reduces redundancy, but it can also make queries that involve multiple tables more complex and slower to execute.

By de-normalizing the database, you can combine some of the data from separate tables into a single table, which can reduce the number of joins that are required to retrieve the data you need. This can speed up queries and improve the performance of your database.

However, it's important to carefully consider the trade-offs of de-normalization before making any changes to your database. De-normalization can make it more difficult to maintain the integrity and reliability of your data, and can also increase the risk of data redundancy. It's generally best to use de-normalization as a performance optimization technique only when necessary, and to carefully evaluate the benefits and drawbacks in the context of your specific database and workload.

SQL Question 4: Filtering Customer Records Based on Multiple Conditions

Dayforce has a table CUSTOMERS that stores customer data. The table includes the following columns: customer_id, signup_date, last_purchase_date, total_purchases, is_active, region.

The task is to write an SQL query that filters customers who made their last purchase in the last six months, made a total of 10 or more purchases, are marked as currently active (is_active = true), and are located in the regions 'North America' or 'Europe'.

CUSTOMERS Example Input:

Example Output:

Answer:

SELECT 
    customer_id, 
    signup_date,
    last_purchase_date,
    total_purchases,
    is_active,
    region
FROM 
    CUSTOMERS
WHERE 
    last_purchase_date > current_date - INTERVAL '6 months'
    AND total_purchases >= 10 
    AND is_active = true 
    AND region IN ('North America', 'Europe');

This query would return all customer records that meet the required conditions. The filter is applied using the WHERE clause, which checks that the last purchase date is within the six-month window, the total purchases are 10 or more, the customer is active, and the region is either in North America or Europe. The INTERVAL keyword is used with '6 months' to specify the window of time for the last purchase, and the IN keyword is used to check that the region is within the list specified.

SQL Question 5: Why should you normalize your database?

Database normalization is a good idea to implement because it can reduce redundancy which can improve performance and database flexibility.

By dividing larger tables into smaller, more modular and specific tables which are linked via foreign keys, this can usually improve the speed of some queries because it minimizes the amount of random data the query has to sift through. However, it's not always 100% true that there is a query speed-up, because joins are an expensive operation.

Nonetheless, by reducing redundancy, besides improved performance you also get more database flexibility. By making tables more modular and reducing overlap, you're able to ALTER TABLE more easily, since one table can be changed more effortlessly without affecting others. This makes it easier to adapt the database schema to Dayforce's evolving business needs.

SQL Question 6: Calculate Average Hourly_employee wage

Dayforce, a human resource software company, would like to analyze the average hourly wage of all their hourly employees. Write a SQL query that calculates the average hourly wage from employee data.

employee Example Input:

Example Output:

Answer:

SELECT AVG(hourly_wage) as average_hourly_wage
FROM employee
WHERE employee_type = 'hourly';

The SQL query above calculates the average hourly wage of the hourly employees. It selects only those rows where the employee_type column equals 'hourly', then applies the AVG function to these selected rows' hourly_wage values.

SQL Question 7: In SQL, Are NULL values the same as a zero or blank space?

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A NULL value represents a missing or unknown value. It is different from a zero or a blank space, which are actual values. It is important to handle NULL values properly in SQL because they can cause unexpected results if not treated correctly.

SQL Question 8: Calculating Click-through Conversion Rates

Dayforce, a tech-savvy company, runs various digital product ads and follows the clickstream of its users meticulously. They are interested in analyzing the click-through rates from a user viewing a product to adding that product to the cart.

Given two tables product_views and product_adds, both logged per user session, create a SQL query to calculate the click-through conversion rate, which is defined as the number of sessions where the user added the product to the cart divided by the number of sessions where the user viewed the product.

product_views Example Input:

product_adds Example Input:

Answer:

SELECT 
    V.product_id,
    COUNT(DISTINCT A.session_id)::float / COUNT(DISTINCT V.session_id) AS conversion_rate
FROM
    product_views V
LEFT JOIN
    product_adds A
ON
    A.session_id = V.session_id AND A.product_id = V.product_id 
GROUP BY 
    V.product_id

This SQL query calculates the conversion rate by comparing the number of distinct sessions where a product was added to the cart to the number of distinct sessions where the product was viewed. The LEFT JOIN ensures that all viewed products are considered, even if the product was not added to the cart in any session. The conversion rate is expressed as a float for better precision.

To solve a related SQL problem on DataLemur's free online SQL coding environment, try this Meta SQL interview question:

SQL Question 9: Analyze Customer and Purchase Patterns

Consider two tables in the Dayforce database, customers and purchases. The customers table contains all the information of customers including their ID, name, and date they joined. The purchases table holds the transaction information, including the customer ID, date of purchase, and product name.

Write a SQL query to select all customers' names and the total number of different products they have bought by joining these two tables. Consider only those customers who have made at least one purchase.

customers Example Input:

purchases Example Input:

Answer:

SELECT c.customer_name, COUNT(DISTINCT p.product_name)
FROM customers c
JOIN purchases p
ON c.customer_id = p.customer_id
GROUP BY c.customer_name
HAVING COUNT(p.product_name) > 0;

The SQL query joins the customers and purchases tables based on the common customer_id column, then it aggregates the data by the customer's name. Finally, the HAVING clause is used to filter out the customers who have not made any purchases. The output will include the customer's name along with the total number of different products they have purchased.

Because joins come up so often during SQL interviews, try this interactive Snapchat Join SQL question:

SQL Question 10: What do the EXCEPT / MINUS operators do, and can you give an example?

The MINUS/EXCEPT operator is used to remove to return all rows from the first SELECT statement that are not returned by the second SELECT statement.

Note that EXCEPT is available in PostgreSQL and SQL Server, while MINUS is available in MySQL and Oracle (but don't worry about knowing which DBMS supports which exact commands since Dayforce interviewers aren't trying to trip you up on memorizing SQL syntax).

For a tangible example of EXCEPT in PostgreSQL, suppose you were doing an HR Analytics project for Dayforce, and had access to Dayforce'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 EXCEPT operator to find all contractors who never were a employee using this query:

SELECT first_name, last_name
FROM dayforce_contractors

EXCEPT

SELECT first_name, last_name
FROM dayforce_employees

SQL Question 11: Calculating Weighted Ratings for Products

Given a table of product reviews in which each review includes a rating and a review count, you are to write a query that calculates the average of the ratings for each product, rounds it to 2 decimal places, finds the absolute difference between the maximum and minimum ratings for each product, and finally, calculates the product of the average rating and the total review count for each product - these are the weighted ratings. The product which has the highest weighted rating can be considered as the most popular product.

reviews Example Input:

Example Output:

Answer:

To solve this question and calculate the required fields, we can use standard SQL math functions with PostgreSQL.

SELECT product_id,
       ROUND(AVG(rating), 2) AS avg_rating,
       ABS(MAX(rating) - MIN(rating)) AS abs_diff_max_min,
       ROUND(AVG(rating) * SUM(review_count), 2) AS weighted_rating
FROM reviews
GROUP BY product_id;

Here, the ROUND function rounds the average of the ratings and the multiplied product of the average rating and total review counts to 2 decimal places. The AVG, MAX, MIN, SUM functions compute the average, maximum, minimum, and sum of the rating and review_count respectively for each product. The ABS function calculates the absolute difference between the maximum and minimum ratings. The GROUP BY clause groups the results by product_id.

To practice a very similar question try this interactive Amazon Average Review Ratings Question which is similar for calculating average ratings or this Alibaba Compressed Mean Question which is similar for rounding to decimal places.

Dayforce SQL Interview Tips

The key to acing a Dayforce SQL interview is to practice, practice, and then practice some more! Besides solving the above Dayforce SQL interview questions, you should also solve the 200+ SQL Interview Questions on DataLemur which come from companies like Netflix, Airbnb, and Amazon.

Each exercise has hints to guide you, fully explained answers along with a discussion board to see how others solved it and crucially, there's an interactive SQL code editor so you can easily right in the browser your SQL query answer and have it checked.

To prep for the Dayforce SQL interview it is also useful to practice SQL problems from other tech companies like:

• Workday SQL questions

• Paycom SQL questions

• Paylocity SQL questions

• Paychex SQL questions

But if your SQL coding skills are weak, forget about jumping right into solving questions – refresh your SQL knowledge with this DataLemur SQL tutorial.

This tutorial covers SQL concepts such as rank window functions and LEAD/LAG window functions – both of these come up routinely during Dayforce SQL interviews.

Dayforce Data Science Interview Tips

What Do Dayforce Data Science Interviews Cover?

In addition to SQL interview questions, the other topics covered in the Dayforce Data Science Interview are:

• Probability & Stats Questions
• Coding Questions in Python or R
• Product Analytics Questions
• ML Interview Questions
• Behavioral Based Interview Questions

How To Prepare for Dayforce Data Science Interviews?

The best way to prepare for Dayforce Data Science interviews is by reading Ace the Data Science Interview. The book's got:

• 201 Interview Questions from tech companies like Google & Microsoft
• A Crash Course on SQL, Product-Sense & ML
• Great Reviews (900+ reviews, 4.5-star rating)