8 Bloomberg SQL Interview Questions (Updated 2024)

At Bloomberg, SQL is used to query large financial databases, including those containing real-time market data, historical prices, and company fundamentals, for necessary data. It is also used to to perform data manipulation tasks, such as data cleaning and feature engineering, for predictive financial model building, which is why Bloomberg asks SQL problems in interviews for Data Science, Analytics, and Data Engineering jobs.

Thus, to help you practice, here’s 8 Bloomberg SQL interview questions – can you solve them?

8 Bloomberg SQL Interview Questions

SQL Question 1: Identify Most Frequent Terminal Users

Bloomberg is renowned for its terminal systems, which provide high-end professionals in business, finance, journalism, and government with real-time data, news, and analytics. Your task is to identify those users who are quite active, and by "active," we mean users who have had a large number of sessions on the terminal during the last 12 months.

For simplicity, we will consider a user as "power user" or "VIP user" if that particular user has exceeded 1000 sessions in the last 12 months.

sessions Example Input:

Example Output:

Answer:

SELECT user_id, COUNT(session_id) AS number_of_sessions
FROM sessions
WHERE start_time BETWEEN NOW() - INTERVAL '1 year' AND NOW()
GROUP BY user_id
HAVING COUNT(session_id) > 1000;

This SQL command reads through the "sessions" table, and for each user, it counts the number of sessions they've had in the last year. It then filters to show only those users who've had more than 1000 sessions within that timeframe.

To solve a related super-user data analysis question on DataLemur's free interactive coding environment, try this Microsoft Teams Power User SQL Interview Question:

SQL Question 2: 2nd Largest Salary

Given a table of Bloomberg employee salary information, write a SQL query to find the 2nd highest salary amongst all the employees.

Bloomberg employees Example Input:

Example Output:

You can solve this question interactively on DataLemur:

Answer:

SELECT MAX(salary) AS second_highest_salary
FROM employee
WHERE salary < (
    SELECT MAX(salary)
    FROM employee
);

You can find a detailed solution here: 2nd Highest Salary.

SQL Question 3: Why are foreign key's important in databases?

A foreign key is a field in a database table that links to the primary key of another table, establishing a connection between the two tables.

To demonstrate this concept, let's analyze Bloomberg's marketing analytics database which stores data from Google Ads campaigns:

bloomberg_ads_data:

campaign_id is a foreign key. It references the campaign_id of the Google Ads campaign that each ad belongs to, establishing a relationship between the ads and their campaigns. This foreign key allows you to easily query the table to find out which ads belong to a specific campaign, or to find out which campaigns a specific ad belongs to.

It is also possible for a table to have multiple foreign keys that reference different primary keys in other tables. For example, the bloomberg_ads_data table could have additional foreign keys for the ad_group_id of the ad group that each ad belongs to, and the account_id of the Google Ads account that the campaigns belong to.

SQL Question 4: Calculate Monthly Average Rating Per Product

Bloomberg maintains a dataset of customer reviews for its various products. Every time a customer leaves a review, a record is created with this information: the unique ID of the review (review_id), the unique ID of the user leaving the review (user_id), the date the review was submitted (submit_date), the unique ID of the product being reviewed (product_id), and the star rating the product received (stars).

Your task is to write a SQL query that calculates the average rating for each product on a monthly basis. The output should include the month, the product ID and the average star rating for that product in that month.

reviews Example Input:

Answer:

SELECT 
    EXTRACT(MONTH FROM submit_date) AS mth, 
    product_id as product, 
    AVG(stars) OVER (PARTITION BY EXTRACT(MONTH FROM submit_date), product_id) as avg_stars
FROM 
    reviews;

In this SQL query, we're making use of the AVG and EXTRACT functions, along with a window function. EXTRACT(MONTH FROM submit_date) returns the month part of the date in the submit_date column. AVG(stars) OVER (PARTITION BY EXTRACT(MONTH FROM submit_date), product_id) calculates the average stars for each combination of month and product. The final result is a list of each month, product ID and the average star rating for that product in that month.

To solve another window function question on DataLemur's free online SQL code editor, solve this Amazon BI Engineer interview question:

SQL Question 5: Could you describe a self-join and provide a scenario in which it would be used?

A self-join is a type of JOIN where a table is joined to itself. To execute a self-join, you must include the table name twice in the FROM clause and assign a different alias to each instance. You can then join the two copies of the table using a JOIN clause, and use a WHERE clause to specify the relationship between the rows.

One common use case for self-joins is to compare data within a single table. For example, you might use a self-join to compare the salaries of employees within the same department, or to identify pairs of products that are frequently purchased together (like in this real Walmart SQL interview question)[https://datalemur.com/questions/frequently-purchased-pairs].

For a more concrete example, imagine you had website visitor data for Bloomberg, exported from the company's Google Analytics account. To help the web-dev team, you needed to analyze pairs of pages for UX or navigational issues. As part of that analysis, you wanted to produce all pairs of URLs, but needed to exclude pairs where both URLs were the same since that is not a valid pair.

You could use the following self-join:

SELECT page1.url AS page_url, page2.url AS referred_from
FROM google_analytics AS page1
JOIN google_analytics AS page2 ON page1.referrer_id = page2.id
WHERE page1.id <> page2.id;

This query retrieves the url of each page (page1.url) along with the url of the page that referred to it (page2.url). The self-join is performed using the referrer_id field, which identifies the id of the page that referred the visitor to the current page, and excludes any pages that referred to themselves (i.e., data anomalies since pages shouldn't refer to themself).

SQL Question 6: Filtering Bloomberg Customer Data

Bloomberg maintains a database of all their customers and their activities. The customers table contains information about all subscribed customers and their profile, while the subscriptions table records the details of the customers' exchanges, subscriptions start and end dates and status of their subscriptions.

customers Example Input:

subscriptions Example Input:

You need to generate a report of all currently subscribed customers from the USA who have subscriptions to the NYSE that started in the last 30 days. Write a PostgreSQL query to accomplish this task.

Answer:

SELECT c.customer_id, c.customer_name, c.city, c.country, s.exchange_name, s.start_date, s.end_date
FROM customers c
JOIN subscriptions s ON c.customer_id = s.customer_id
WHERE c.country = 'USA'
AND s.exchange_name = 'NYSE'
AND s.subscription_status = 'Active'
AND s.start_date >= (CURRENT_DATE - INTERVAL '30 days');

This query joins the customers and subscriptions tables on the customer_id. Then, the WHERE clause filters for customers from the USA who have active subscriptions to the NYSE that started in the last 30 days, inclusively. The result is a list of matching customers along with their city, country, exchange name, and the start and end dates of their subscriptions.

SQL Question 7: Can you explain the difference between the RANK() and DENSE_RANK() window functions in SQL?

While both RANK() and DENSE_RANK() are used to rank rows, the key difference is in how they deal with ties.

RANK(): When there's a tie, RANK() leaves a gap in the ranking. For example, if three rows are tied for 2nd place, the RANK() function will assign a rank of 2 to the first of these rows, a rank of 3 to the 2nd row in the tie, and a rank of 4 to the the 3rd tie.

DENSE_RANK(): For ties, DENSE_RANK() does not leave a gap in the ranking. Instead, it assigns the same rank to all tied rows, and then makes the next row 1 bigger. Confusing, I know, but here's an example to make it more clear: if three rows are tied for 3rd place, the DENSE_RANK() function will assign a rank of 3 to all three rows, and then assign a rank of 4 to the next row.

Suppose we had data on how many deals different salespeople at Bloomberg closed, and wanted to rank the salespeople.

WITH data AS (
  SELECT 'Akash' AS name, 50 AS deals_closed UNION ALL
  SELECT 'Brittany', 50 UNION ALL
  SELECT 'Carlos', 40 UNION ALL
  SELECT 'Dave', 30 UNION ALL
  SELECT 'Eve', 30 UNION ALL
  SELECT 'Frank', 20
)
SELECT name, deals_closed,
  RANK() OVER (ORDER BY deals_closed DESC) as rank,
  DENSE_RANK() OVER (ORDER BY deals_closed DESC) as dense_rank
FROM bloomberg_sales;

The result of this query would be:

As you can see, the RANK() function assigns a rank of 1 to the first row (Akash), a rank of 2 to the second row (Brittany), and so on. In contrast, the DENSE_RANK(), gives both Akash and Brittany a rank of 1, since they are tied, then moves on to rank 2 for Carlos.

SQL Question 8: Average Volume of Stocks Traded on Bloomberg

As an Analyst at Bloomberg, you are tasked with finding out the average volume of each stock traded per day. To simplify, assume volume of a stock is the number of shares that exchange hands during a trading session as per that stock. Use the "stock_trade" table to solve this.

Here is an example of how the stock_trade table looks like:

stock_trade Example Input:

Answer:

In PostgreSQL, you can find the average volume of each stock traded per day by executing the following SQL command:

SELECT stock_symbol, trade_date, AVG(volume) as avg_volume
FROM stock_trade
GROUP BY stock_symbol, trade_trade;

This SQL statement above first groups the data by the stock symbol and trade date. Then it finds the average volume of each group. The resulting table returned by the SELECT statement would show the stock_symbol, trade_date and the average volume of each stock traded per day.

It's important to note that aggregate functions like AVG ignore NULL values.

To practice a very similar question try this interactive Robinhood Cities With Completed Trades Question which is similar for analyzing trading data or this Stripe Repeated Payments Question which is similar for < dealing with transactional data.

Bloomberg SQL Interview Tips

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. Besides solving the above Bloomberg SQL interview questions, you should also solve the 200+ DataLemur SQL Interview Questions which come from companies like Microsoft, Google, and Meta.

Each interview question has multiple hints, step-by-step solutions and best of all, there's an interactive SQL code editor so you can instantly run your SQL query and have it executed.

To prep for the Bloomberg SQL interview it is also a great idea to practice SQL problems from other finance companies like:

• Moody's SQL questions
• TransUnion SQL questions
• Dun & Bradstreet SQL questions
• Fiserv SQL questions

Explore how Bloomberg is harnessing the power of Artificial Intelligence to revolutionize the world of finance!

But if your SQL query skills are weak, forget about diving straight into solving questions – improve your SQL foundations with this free SQL tutorial.

This tutorial covers things like SUM/AVG window functions and cleaning text data – both of which show up often during SQL job interviews at Bloomberg.

Bloomberg Data Science Interview Tips

What Do Bloomberg Data Science Interviews Cover?

In addition to SQL query questions, the other types of questions covered in the Bloomberg Data Science Interview are:

• Probability & Stats Questions
• Python or R Programming Questions
• Product-Sense Questions
• ML Interview Questions
• Behavioral Interview Questions centered on Bloomberg culture and values

How To Prepare for Bloomberg Data Science Interviews?

To prepare for Bloomberg Data Science interviews read the book Ace the Data Science Interview because it's got:

• 201 interview questions sourced from FAANG (FB, Apple, Amazon, Netflix, Google)
• a crash course on Python, SQL & ML
• over 1000+ 5-star reviews on Amazon

Don't ignore the behavioral interview – prep for that using this guide on behavioral interview questions.