10 JetBlue SQL Interview Questions (Updated 2024)
JetBlue employees write SQL queries often to analyze flight data to optimize routes and query the passenger databases to personalize customer experience. Because of this, JetBlue LOVES to ask SQL questions in interviews for Data Science and Data Engineering positions.
So, to help you prepare, we've collected 10 JetBlue Airways SQL interview questions – can you solve them?
10 JetBlue Airways SQL Interview Questions
SQL Question 1: Identify Most Frequent Flyers
JetBlue wants to identify their most valuable customers - the ones who fly most frequently with the airline. More specifically, they are interested in customers who have flown more than 50 times in the past year. Your task is to write a SQL query that delivers this information.
To perform this task, consider the following table that logs all flights taken by each customer:
Sample Input:
| flight_id | customer_id | flight_date |
|---|---|---|
| 1 | 1001 | 01/01/2021 |
| 2 | 1002 | 01/02/2021 |
| 3 | 1001 | 01/03/2021 |
| 4 | 1003 | 01/04/2021 |
| 5 | 1001 | 01/05/2021 |
| ... | ... | ... |
| 1000 | 1003 | 12/30/2021 |
Answer:
The following PostgreSQL query accomplishes this task:
This query starts by selecting the and a count of the rows (which represents the number of flights) from the table. It only considers flights that took place in the specified date range (in this case, in 2021). The clause then consolidates these rows by , so that the final count represents the total number of flights for each customer in the given year. The clause then filters out customers who have flown 50 times or fewer, leaving only the airline’s most frequent flyers.
To practice a related customer analytics question on DataLemur's free interactive coding environment, try this Microsoft SQL Interview problem:
SQL Question 2: Employees Earning More Than Their Boss
Given a table of JetBlue employee salary data, write a SQL query to find all employees who make more money than their direct boss.
JetBlue Example Input:
| 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 |
Example Output:
| 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.
Test your SQL query for this interview question interactively on DataLemur:
Answer:
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 code above is confusing, you can find a step-by-step solution with hints here: Employees Earning More Than Managers.
SQL Question 3: What's the difference between the and operators?
Both and are used to combine the results of two or more SELECT statements into a single result set.
However, only includes one instance of a duplicate, whereas includes duplicates.
SQL Question 4: Analyze Flight Delays with SQL Window Functions
You've been provided with two tables, and . The table stores data about various flights done by JetBlue, including the departure airport, arrival airport, the scheduled time, and the actual time of the departure and arrival. The table contains information about various airports, including their ID and name.
Your task is to write a SQL query to calculate the average delay (in minutes) for each airport in the table for the month of May 2022, considering only flights that departed from each airport. Assume the flights may have left late but can also lead ahead of schedule.
The delay for a flight is defined as . If the result of this calculation is negative, consider the delay to be zero.
Example Input:
| flight_id | departure_id | arrival_id | scheduled_departure_time | actual_departure_time | scheduled_arrival_time | actual_arrival_time |
|---|---|---|---|---|---|---|
| 1 | 1 | 2 | 2022-05-01 02:00:00 | 2022-05-01 02:15:00 | 2022-05-01 05:00:00 | 2022-05-01 05:10:00 |
| 2 | 2 | 1 | 2022-05-01 11:00:00 | 2022-05-01 11:05:00 | 2022-05-01 14:00:00 | 2022-05-01 14:00:00 |
| 3 | 1 | 3 | 2022-05-02 15:00:00 | 2022-05-02 15:00:00 | 2022-05-02 18:00:00 | 2022-05-02 18:00:00 |
| 4 | 1 | 2 | 2022-05-02 10:00:00 | 2022-05-02 10:30:00 | 2022-05-02 13:00:00 | 2022-05-02 13:30:00 |
| 5 | 3 | 1 | 2022-05-03 08:00:00 | 2022-05-03 07:50:00 | 2022-05-03 11:00:00 | 2022-05-03 10:55:00 |
Example Input:
| airport_id | airport_name |
|---|---|
| 1 | John F. Kennedy International Airport |
| 2 | LaGuardia Airport |
| 3 | Boston Logan International Airport |
Example Output:
| airport | avg_delay_minutes |
|---|---|
| John F. Kennedy International Airport | 15 |
| LaGuardia Airport | 5 |
| Boston Logan International Airport | 0 |
Answer:
This SQL query first generates a table that computes the delay for each flight in minutes. It then averages these delay times for each airport for the relevant month and year using a JOIN on the with the table. The result is sorted in descending order of delay. If no delay is present, the average delay returned will be zero.
p.s. Window functions show up super frequently during SQL interviews, so practice the 27+ window function questions on DataLemur
SQL Question 5: Can you describe the meaning of database normalization in layman's terms?
To normalize a database, tables are divided into smaller, more specialized ones and relationships between them are defined via primary and foreign keys. This minimizes redundancy, making the database more flexible, scalable, and easier to maintain. Normalization also helps to ensure the accuracy of the data by reducing the likelihood of inconsistencies and errors.
SQL Question 6: Filter JetBlue's Customer Records
You are working for JetBlue and your task is to filter out customer records based on specific conditions. Find out all the customers who booked a flight in the past three months, who have accumulated points of over 5000 and who belong to either Gold or Platinum loyalty membership.
Example Input:
| customer_id | booking_date | loyalty_membership | accumulated_points |
|---|---|---|---|
| 4517 | 04/15/2022 00:00:00 | Gold | 7500 |
| 6171 | 07/18/2022 00:00:00 | Silver | 3500 |
| 5293 | 06/30/2022 00:00:00 | Platinum | 6000 |
| 7802 | 09/10/2022 00:00:00 | Gold | 5200 |
| 6352 | 07/26/2022 00:00:00 | Silver | 4500 |
Answer:
In PostgreSQL, your SQL query would be like this:
This query will output the customer ID, booking date, loyalty membership type, and accumulated points of all customers who booked a flight in the past three months, have more than 5000 accumulated points and belong to Gold or Platinum loyalty membership. We use the 'BETWEEN' clause to designate the date range, 'AND' to apply all conditions, and 'IN' to specify the membership types we're interested in.
SQL Question 7: Can you explain the purpose of UNION?
The UNION operator combines the results from multiple SELECT statements into a single result set.
Here's an example of using the UNION operator to combine the results of two SELECT statements that retrieve data from tables of JetBlue's Facebook ads and their Google ads:
This statement would return a combined result set of both Facebook and Google ads that have a spend greater than $200. The result set would include the following columns: ad_id, ad_name, ad_type, impressions, clicks, and spend.
Note that the two SELECT statements within the UNION must have the same number of columns and the columns must have similar data types. In this example, both SELECT statements have the same number of columns and the data types of the columns are all compatible.
SQL Question 8: Average number of passengers on a JetBlue flight
JetBlue is a popular airline company, and it wishes to understand its occupancy rates better for planning and strategic purposes. Write a SQL query to find the average number of passengers on a JetBlue flight in the month of December, 2021. Assume you have access to two tables: and . The table includes data for every JetBlue flight, while the table contains information about the passengers on each flight.
Example Input:
| flight_id | date_time | destination | origin_airport | capacity |
|---|---|---|---|---|
| 8102 | 12/07/2021 06:00:00 | Boston | Los Angeles | 180 |
| 7485 | 12/14/2021 08:30:00 | New York | Chicago | 150 |
| 7321 | 12/21/2021 15:45:00 | Seattle | Boston | 180 |
| 6248 | 12/28/2021 10:00:00 | Chicago | New York | 150 |
Example Input:
| passenger_id | flight_id | name | seat_number |
|---|---|---|---|
| 981 | 8102 | John Doe | A1 |
| 472 | 8102 | Jane Doe | A2 |
| 635 | 7485 | Robert Smith | A1 |
| 748 | 7485 | Emma Johnson | A2 |
| 520 | 7321 | Oliver Jones | A1 |
| 691 | 7321 | Emily Brown | A2 |
| 386 | 6248 | Noah Davis | A1 |
| 597 | 6248 | Lily Miller | A2 |
Answer:
In this query, we join the table and the table on the column to count the number of passengers on each flight occurring in December 2021. We then average these counts over all flights. This gives us the average number of passengers on a flight.
To practice a very similar question try this interactive Facebook Active User Retention Question which is similar for calculating a monthly metric or this Amazon Average Review Ratings Question which is similar for calculating an average metric.
SQL Question 9: Analyzing Click-Through and Conversion Rates for JetBlue Ads
JetBlue, a major airline, frequently places digital advertisements. When customers see these ads, they may click on them to be redirected to JetBlue's website, and some end up making a booking. JetBlue is interested in understanding the click-through rate of their advertisements as well as the conversion rate (a rate from accessing the ad to making a booking).
For this question, we will use two tables: and .
records every unique instance that a user clicks on JetBlue's digital ad.
records bookings made by these users.
Example Input:
| click_id | user_id | ad_id | click_date |
|---|---|---|---|
| 1 | 123 | 5000 | 2022-06-01 |
| 2 | 234 | 5001 | 2022-06-15 |
| 3 | 345 | 5000 | 2022-06-25 |
| 4 | 456 | 5001 | 2022-06-30 |
| 5 | 567 | 5000 | 2022-07-10 |
Example Input:
| booking_id | user_id | booking_date |
|---|---|---|
| 1 | 123 | 2022-06-05 |
| 2 | 234 | 2022-06-16 |
| 3 | 890 | 2022-06-18 |
| 4 | 123 | 2022-07-20 |
| 5 | 234 | 2022-07-22 |
With these tables, calculate the click through rate (CTR) and conversion rate (CONV) for each ad. CTR is calculated as total_number_of_clicks/total_impresions and CONV as total_number_of_bookings/total_clicks.
Answer:
This SQL query calculates the total number of clicks and bookings by ad, and then calculates the CTR and CONV ratios as requested. If an ad has not resulted in a booking, the COALESCE function is used to output 0 instead of NULL in the CONV calculation.
To practice a similar SQL interview question on DataLemur's free online SQL code editor, try this Meta SQL interview question:
SQL Question 10: What's a database view?
Database views are created to provide customized, read-only versions of your data that you can query just like a regular table. So why even use one if they're just like a regular table?
Views are useful for creating a simplified version of your data for specific users, or for hiding sensitive data from certain users while still allowing them to access other data.
How To Prepare for the JetBlue SQL Interview
The best way to prepare for a JetBlue SQL interview is to practice, practice, practice.
Besides solving the above JetBlue SQL interview questions, you should also solve the 200+ FAANG SQL Questions on DataLemur which come from companies like Google, Microsoft and Silicon Valley startups.
Each problem on DataLemur has multiple hints, fully explained answers along with a discussion board to see how others solved it and best of all, there is an online SQL code editor so you can right in the browser run your query and have it checked.
To prep for the JetBlue SQL interview you can also be wise to solve SQL problems from other airlines like:
- Alaska Air SQL questions
- Southwest Airlines SQL questions
- United Airlines SQL questions
- American Airlines SQL questions
In case your SQL query skills are weak, don't worry about going right into solving questions – strengthen your SQL foundations with this free SQL tutorial.
This tutorial covers things like handling missing data (NULLs) and joining multiple tables – both of which show up routinely in SQL job interviews at JetBlue.
JetBlue Airways Data Science Interview Tips
What Do JetBlue Data Science Interviews Cover?
Besides SQL interview questions, the other types of problems covered in the JetBlue Data Science Interview include:
- AB Testing & Experimentation Questions
- Python or R Programming Questions
- Product Metrics Interview Questions
- Machine Learning and Predictive Modeling Questions
- Behavioral & Resume-Based Questions
How To Prepare for JetBlue Data Science Interviews?
To prepare for JetBlue Data Science interviews read the book Ace the Data Science Interview because it's got:
- 201 interview questions sourced from companies like Google, Tesla, & Goldman Sachs
- a refresher on SQL, Product-Sense & ML
- over 1000+ 5-star reviews on Amazon
Also be prepared to talk about current news and updates about the company, check out the Jet Blue news page!