8 Opendoor SQL Interview Questions (Updated 2024)
At Opendoor, SQL is crucial for analyzing customer behavior by tracking website interactions and purchase history to enhance the user experience. It is also used for extracting data on property listings and sales to forecast real estate market trends, which is why Opendoor includes SQL problems during interviews for Data Analytics, Data Science, and Data Engineering roles.
To help prep you for the Opendoor SQL interview, we'll cover 8 Opendoor SQL interview questions – scroll down to start solving them!
8 Opendoor SQL Interview Questions
SQL Question 1: Average Listing Prices in Different Regions
Opendoor is a company that makes it easy for people to buy and sell homes online. They have listings across different regions in the United States, and these listings have different listed prices.
You are asked to write a SQL query to analyze the listing data from the previous quarter. Specifically, they want you to find the average listed price for each region in the previous quarter. In addition, they want this broken down by month to see if there were any trends.
Here's the data you have available in the table:
Example Input:
| listing_id | region_id | listing_date | price |
|---|---|---|---|
| 2115 | SF | 04/15/2022 | 1,200,000 |
| 3217 | LA | 05/22/2022 | 800,000 |
| 9786 | NY | 06/08/2022 | 2,000,000 |
| 5312 | LA | 06/26/2022 | 900,000 |
| 8391 | SF | 07/14/2022 | 1,500,000 |
(Note: The is in USD, and is in the format of MM/DD/YYYY.)
Answer:
The query works by filtering the table for the previous quarter using the clause. Then it groups the results by month (extracted from the using the function) and , calculates the average price for each group, and sorts the results by month and average price in descending order. Please note that in reality the date filter for the previous quarter might be a bit more complex due to different definitions of fiscal quarters.
Pro Tip: Window functions are a frequent SQL interview topic, so practice every window function problem on DataLemur
SQL Question 2: 2nd Largest Salary
Suppose you had a table of Opendoor employee salary data. Write a SQL query to find the 2nd highest salary among all employees.
Opendoor Example Input:
| employee_id | salary |
|---|---|
| 1 | 2500 |
| 2 | 800 |
| 3 | 1000 |
| 4 | 1200 |
Example Output:
| second_highest_salary |
|---|
| 1200 |
Solve this question directly within the browser on DataLemur:
Answer:
You can find a step-by-step solution here: 2nd Highest Salary.
SQL Question 3: How would you improve the performance of a slow SQL query?
Here's some strategies that can generally speed up a slow SQL query:
- Only query the columns you actually need
- Index the columns used in the query
- Use the right kind of JOIN (be explicit about inner vs. outer joins)
- Use the right data types (for example INT vs BIGINT can save you disk space and memory)
While there's an entire industry dedicated to database performance tuning , for Data Analyst and Data Science interviews at Opendoor, knowing the above basics should be more than enough to ace the SQL interview! Data Engineers on the other hand should know more about the command and understand a bit more about a query-execution plan.
SQL Question 4: Fetch Properties Based on Certain Conditions
You are given a database which contains listing information for the properties Opendoor company handles. Using this database, write an SQL query that finds all the properties that are priced above $500,000, located in San Francisco and were put on display for sale after March 1st, 2022. Consider the name of the database as "properties".
Example Input:
| property_id | city | listing_date | price |
|---|---|---|---|
| 101 | San Francisco | 03/02/2022 | $650,000 |
| 102 | Los Angeles | 03/05/2022 | $800,000 |
| 103 | San Francisco | 02/28/2022 | $550,000 |
| 104 | San Francisco | 04/01/2022 | $600,000 |
| 105 | Seattle | 03/02/2022 | $700,000 |
Example Output:
| property_id | city | listing_date | price |
|---|---|---|---|
| 101 | San Francisco | 03/02/2022 | $650,000 |
| 104 | San Francisco | 04/01/2022 | $600,000 |
Answer:
In this query, three conditions are combined with the AND operator in WHERE clause. It filters properties by the price (> $500,000), city (San Francisco), and listing_date (after March 1st, 2022). The '*' in the SELECT statement tells the database to return all columns for the selected rows.
SQL Question 5: What's a stored procedure, and why use one?
Stored procedures are a lot like functions in programming. They're used to encapsulate and organize business logic into one unit of code, and they can accept multiple input parameters and return multiple output values.
For example, if you were a Data Analyst at Opendoor working on a HR analytics project, you might create a stored procedure to calculate the average salary for a given department:
To call this stored procedure and find the average salary for the Data Analytics department you'd write the following query:
SQL Question 6: Average Offer Price for Each Property Type
As an analyst at Opendoor, you're tasked with getting an understanding of the business activity in terms of property types they deal with. More specifically, you're interested in finding out the average offer price made by the company for each property type.
Below are the example tables for and :
Example Input:
| offer_id | property_id | offer_price |
|---|---|---|
| 101 | 2001 | 150000 |
| 102 | 2002 | 250000 |
| 103 | 2001 | 200000 |
| 104 | 2003 | 300000 |
| 105 | 2002 | 280000 |
Example Input:
| property_id | property_type |
|---|---|
| 2001 | Single Family |
| 2002 | Condo |
| 2003 | Single Family |
Answer:
This query is joining the and tables on the field. It then groups the records by from the table and finds the average for each property type from the table.
SQL Question 7: What does it mean to normalize a database?
Normalization involves dividing a large table into smaller, more specific ones and establishing connections between them. This helps to reduce redundancy, creating a database that is more adaptable, scalable, and easy to manage. Additionally, normalization helps to maintain the integrity of the data by minimizing the risk of inconsistencies and anomalies.
SQL Question 8: Finding Real Estate Transactions with Specific Characteristics
Assume you work for Opendoor and you would like to find all real estate transactions completed by customers whose last names begin with the letter "M". The relevant table, , contains columns for , , , , , and .
You are to write an SQL query that will find these records.
Example Input:
| transaction_id | customer_id | customer_last_name | property_id | transaction_date | transaction_amount |
|---|---|---|---|---|---|
| 1001 | A01 | Miller | P01 | 01/22/2022 | $500,000 |
| 1002 | B02 | Smith | P02 | 02/15/2022 | $750,000 |
| 1003 | C03 | Morris | P03 | 03/02/2022 | $600,000 |
| 1004 | D04 | Jones | P04 | 04/18/2022 | $800,000 |
| 1005 | E05 | Mathews | P05 | 05/29/2022 | $700,000 |
Answer:
This SQL query uses the keyword to filter the table so that only records where begins with "M" are returned. The '%' is a wildcard character that matches any sequence of characters following "M" in the field. This means that any last name starting with "M" will meet the criteria.
Preparing For The Opendoor SQL Interview
The best way to prepare for a Opendoor SQL interview is to practice, practice, practice. Besides solving the above Opendoor SQL interview questions, you should also solve the 200+ DataLemur SQL Interview Questions which come from companies like Google, Uber, and Microsoft.
Each SQL question has multiple hints, full answers and crucially, there is an online SQL code editor so you can right in the browser run your SQL query and have it graded.
To prep for the Opendoor SQL interview it is also useful to solve SQL questions from other tech companies like:
In case your SQL coding skills are weak, forget about going right into solving questions – refresh your SQL knowledge with this SQL interview tutorial.
This tutorial covers SQL topics like RANK() window functions and math functions like CEIL()/FLOOR() – both of these show up frequently in Opendoor SQL interviews.
Stay up-to-date with Opendoor’s latest news and discover how they’re transforming the home buying experience!
Opendoor Data Science Interview Tips
What Do Opendoor Data Science Interviews Cover?
In addition to SQL query questions, the other topics covered in the Opendoor Data Science Interview are:
- Probability & Stats Questions
- Coding Questions in Python or R
- Product-Sense Questions
- Machine Learning and Predictive Modeling Questions
- Behavioral & Resume-Based Questions
How To Prepare for Opendoor Data Science Interviews?
To prepare for the Opendoor Data Science interview, make sure you have first have a firm understanding of the company's culture & values – these will be key to acing the behavioral interview. For the technical interviews, prepare by reading Ace the Data Science Interview. The book's got:
- 201 Interview Questions from tech companies like Google & Microsoft
- A Refresher on Product Analytics, SQL & ML
- Great Reviews (1000+ 5-star reviews on Amazon)
Learn how companies like Opendoor use Data Science in Real Estate!