9 Jefferies SQL Interview Questions (Updated 2024)

Updated on

June 30, 2024

Data Analysts and Data Engineers at Jefferies write SQL queries almost every single workday. They use SQL for analyzing financial data for trends and patterns, and managing databases for efficient data retrieval and storage. That's why Jefferies asks prospective hires SQL coding interview questions.

Thus, to help you practice, here’s 9 Jefferies Financial Group SQL interview questions – how many can you solve?

Jefferies SQL Interview Questions

9 Jefferies Financial Group SQL Interview Questions

SQL Question 1: Identify Top Trading Customers at Jefferies

Jefferies is a diversified financial services company engaged in investment banking and capital market, asset management and direct investing. Your task is to write a SQL query to identify the top 5 customers from the 'trades' table that have traded the most in terms of trade revenue in the last month.

Example Input:


This query first filters the trades table for records from the last month. Then it groups the trades by 'customer_id', sums the 'revenue' for each customer, and sorts the customers in descending order of their total revenue. Finally, the 'LIMIT 5' clause ensures that only the top 5 customers, in terms of total trading revenue, are returned.

To solve a related customer analytics question on DataLemur's free interactive SQL code editor, try this Microsoft SQL Interview problem: Microsoft SQL Interview Question: Super Cloud Customer

SQL Question 2: Well Paid Employees

Imagine there was a table of Jefferies employee salary data. Write a SQL query to find all employees who earn more than their direct manager.

Jefferies Example Input:

1Emma Thompson38001
2Daniel Rodriguez2230110
3Olivia Smith800018
4Noah Johnson680028
5Sophia Martinez1750110
8William Davis70002NULL
10James Anderson40001NULL

Example Output:

3Olivia 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 question directly within the browser on DataLemur:

Employees Earning More Than Their Manager


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 tough, you can find a step-by-step solution with hints here: Employees Earning More Than Managers.

Jefferies has locations all over the world! See if there is a lcoation near you :)

SQL Question 3: Why should you normalize your database?

Database normalization has several benefits:

  • Reduces Redundancy: Normalization can minimize redundancy by breaking down a larger, general table into smaller, more granular tables. This often reduces the amount of data that needs to be accessed for particular queries, since some duplicated columns can be removed.

  • Improves Data Integrity: Normalization can help to ensure the integrity of the data by minimizing the risk of data inconsistencies and errors. By establishing clear relationships between the tables via primary and foreign keys, and enforcing these constraints, you can have more reliable records and relationships stored in your DB.

  • Improves Performance: Normalization can improve the performance of the database by reducing the amount of data that needs to be stored and accessed in a single table. This results in faster query times and better overall performance.

Jefferies Financial Group SQL Interview Questions

SQL Question 4: Analyzing Trades Performed

As a prospective data analyst for Jefferies, a global investment bank and institution securities firm, you are provided with a dataset containing trades a broker have executed over a period of time.

The table has the following schema:


The company wants to understand the top 3 most traded securities by each broker within each month. Write a SQL query to retrieve the information for this analysis.


In this query, a window function is used to find the total trade volume for each broker within each month for each security. The result is packaged into a Common Table Expression (CTE) named .

Another window function calculates the rank for each security, considering each month and broker separately.

The final query takes this CTE and filters for securities with a rank of three or less, effectively selecting the top three securities for each broker per month.

To solve another window function question on DataLemur's free interactive SQL code editor, try this Google SQL Interview Question: Google SQL Interview Question

SQL Question 5: What's the main difference between the 'BETWEEN' and 'IN' operators in SQL?

is used to select rows that match a range of values, whereas the operator checks for values in a specified list of values.

For example, say you were a Data Analyst at Jefferies and had a table of advertising campaign data.

To find campaigns with between 500and500 and 10k in spend, you could use BETWEEN`:

To find ad campaigns that were run on Facebook and Google's Display Network, you could use :

SQL Question 6: Filter Customers Based on Trading Activity

Assume that you're an analyst at Jefferies. You have a database with customer information, including their monthly trading activities such as: total trades made, total trades won, and total trades lost. Your task is to identify all customers who had a monthly winning trade rate of above 50% and lost 30 or fewer trades in any given month in 2022.

In simple words, you need to filter customers who are relatively more successful in their monthly trading.

Example Input:
Expected Output:


The PostgreSQL query to solve this task is:

This query extracts the month from the trade_date and calculates the winning rate by dividing won trades by total trades. It then filters out the customers having a winning rate above 50% and losing 30 or fewer trades in 2022.

SQL Question 7: What does do, and when would you use this function?

The function allows you to select the first non-NULL value from a list of values. It is often used to replace a NULL value with a default value in order to run aggregate functions like or on a column without being affected by values.

For example, if you conducted a customer satisfaction survey for a company and asked customers to rate their level of agreement with a statement on a scale of 1 to 10, some customers may not have answered all the questions, resulting in NULL values in the survey data:


To replace the NULLs in the column with the default value of 5 which corresponds to neutral on the survey, you could run the following SQL query:

You'd get the following output:


SQL Question 8: Calculate Average Transaction Amount

Jefferies is an investment banking firm. As an analyst in Jefferies, you have been tasked to analyze the transactions data. You are required to calculate the average transaction amount of each stock on a monthly basis. The transactions table contains columns for 'transaction_id', 'transaction_date', 'stock_symbol', and 'transaction_amount'.

Example Input:
Example Output:


This SQL query first uses PostgreSQL's EXTRACT function to get the month of each transaction, and then groups the transactions by both the extracted month and the stock symbol. Finally, it calculates the average transaction amount for each group and orders the output table by month and stock symbol.

To practice a very similar question try this interactive Amazon Average Review Ratings Question which is similar for calculating average and grouping by month or this Stripe Repeated Payments Question which is similar for dealing with transaction data.

SQL Question 9: Average Deal Size by Analyst

As an investment banking firm, Jefferies is interested in the performance of their financial analysts. Specifically, they want to know the average size of the deals managed by each analyst per year.

Deal sizes are measured in billions of dollars.

Example Input:


Example Output:



This SQL statement works by grouping rows by year and analyst. The function is used to get the year part of the . Within each group, it calculates the average deal size. The clause is applied on and , allowing it to show the average deal size for each analyst per year grouped in ascending order.

How To Prepare for the Jefferies SQL Interview

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 Jefferies SQL interview questions, you should also solve the 200+ tricky sql questions on DataLemur which come from companies like Google, Microsoft and Silicon Valley startups. DataLemur Questions

Each exercise has hints to guide you, detailed solutions and best of all, there's an online SQL coding environment so you can easily right in the browser your query and have it graded.

To prep for the Jefferies SQL interview it is also wise to practice interview questions from other banking & finanacial services companies like:

In case your SQL coding skills are weak, forget about going right into solving questions – refresh your SQL knowledge with this DataLemur SQL Tutorial.

Interactive SQL tutorial

This tutorial covers things like working with string/text data and window functions like RANK() and ROW_NUMBER() – both of these come up often during Jefferies SQL interviews.

Jefferies Financial Group Data Science Interview Tips

What Do Jefferies Data Science Interviews Cover?

Beyond writing SQL queries, the other topics covered in the Jefferies Data Science Interview are:

Jefferies Data Scientist

How To Prepare for Jefferies Data Science Interviews?

I'm a bit biased, but I believe the best way to prepare for Jefferies Data Science interviews is to read my book Ace the Data Science Interview.

The book covers 201 data interview questions taken from Facebook, Google, & Amazon. It also has a refresher on Product Analytics, SQL & ML. And finally it's vouched for by the data community, which is why it's got over 1000+ 5-star reviews on Amazon.

Ace the Data Science Interview by Nick Singh Kevin Huo