9 BlackLine SQL Interview Questions (Updated 2024)
At BlackLine, SQL is typically used for extracting and analyzing financial audit data and analyzing product analytics data related to Blackline's invoicing & financial analytics software. That's the reason behind why BlackLine typically asks SQL problems during interviews for Data Analytics, Data Science, and Data Engineering jobs.
To help you ace the BlackLine SQL interview, here’s 9 BlackLine SQL interview questions – can you answer each one?
9 BlackLine SQL Interview Questions
SQL Question 1: Calculate Monthly Average Ratings Per Product
BlackLine, as an accounting automation software company, can have a vast product suite. This question imagines a scenario where BlackLine wants to understand how their different products are being rated by users on a monthly basis to gain some insights into the perception of their users about different products. The objective is to write a SQL query to calculate the monthly average star rating for each product.
For simplicity, let's assume we only have one table ('product_reviews') containing all the information needed. It includes the review_id, user_id, timestamp of the review, product_id, and the rating (stars) given by the user. The stars are on a scale of 1 to 5.
Here is an example of a product_reviews table:
You should be able to generate output like the following table, which provides the monthly average star rating for each product:
Here is a sample PostgreSQL query which can achieve this:
Answer:
This query starts by creating a WITH clause or CTE (Common Table Expressions) named 'monthly_reviews' that extracts the month from the review timestamp. It then uses this 'monthly_reviews' CTE in the main query to calculate the average star rating for each product on a monthly basis. The results are grouped by the 'month' and 'product_id' to ensure that we get separate average ratings for each product per month. Finally, the results are ordered by 'month' and 'product_id'.
To solve a related window function SQL problem on DataLemur's free online SQL code editor, solve this Google SQL Interview Question:
SQL Question 2: BlackLine Customers and Transactions Analysis
As a data analyst at BlackLine, a cloud software provider delivering financial close solutions, you are tasked with designing a database to analyse how usage of the financial software relates to customer retention. You have one table - - that logs every action the customers make on the platform. Each row records the customer ID, the transaction ID, the type of the transaction and the date the transaction was made.
Your other table - - contains information about the customers of BlackLine, including the customer ID, their joining date, and whether they are still a customer (Churned column - 'Yes' if they've left the platform and 'No' if they are still a customer).
You need to determine if there's a relation between the number of transactions a customer performs on the platform and whether the customer churns or not. Specifically, find out how many transactions on average are performed by churned customers and how many by retained customers.
Your tables are given below:
Sample Input:
| transaction_id | customer_id | transaction_type | transaction_date |
|---|---|---|---|
| 1201 | 321 | "Payment" | 03/05/2021 |
| 2543 | 654 | "Statement review" | 06/08/2021 |
| 3985 | 897 | "Invoice creation" | 09/12/2021 |
| 5012 | 321 | "Balance check" | 01/01/2022 |
| 6054 | 654 | "Payment" | 03/05/2022 |
Sample Input:
| customer_id | join_date | Churned |
|---|---|---|
| 321 | 01/01/2021 | "No" |
| 654 | 01/02/2021 | "Yes" |
| 897 | 01/03/2021 | "No" |
Answer:
This query groups the customers based on their churned status and calculates the average number of transactions per customer for each group. It is achieved by dividing the total number of transactions in each group () by the number of customers in that group (). You can observe if there is a significant difference in software usage between churned and retained customers from the results.
SQL Question 3: What's a cross-join, and why are they used?
A cross-join, also known as a cartesian join, is like a mad scientist's laboratory experiment gone wild. It takes two tables and mixes them together to create a crazy new table with every possible combination of rows from the original tables.
Here's an example:
If you have 20 products and 10 colors, that's 200 rows right there! Cross-joins are great for generating all possible combinations, but they can also create really big tables if you're not careful. Just like a mad scientist, use your powers wisely!
SQL Question 4: Analyzing Click-through-rates at BlackLine
Given the number of ads displayed to users and the number of times those ads were clicked, we would like to calculate the click-through-rate (CTR) for specific products advertised during the month. Each row in the table represents an ad impression (displayed ad), and the column is 1 if the ad was clicked and 0 otherwise. BlackLine, as a financial software company, would like to analyze its CTR to effectively plan their future marketing campaigns.
Example Input:
| ad_id | displayed_date | product_id | user_id | clicks |
|---|---|---|---|---|
| 3891 | 05/05/2022 | 1001 | 485 | 1 |
| 2468 | 05/09/2022 | 1002 | 341 | 0 |
| 7615 | 05/15/2022 | 1001 | 678 | 1 |
| 8865 | 05/17/2022 | 1002 | 254 | 1 |
| 9347 | 05/29/2022 | 1001 | 459 | 0 |
Example Output:
| month | product | CTR |
|---|---|---|
| 5 | 1001 | 0.50 |
| 5 | 1002 | 0.50 |
Answer:
This SQL query extracts and groups data by month and product. It calculates the CTR as the sum of clicks divided by the total number of ads for each product in each month. The function is used to limit the result to two decimal places. The result will show the click-through-rates of the ads by products for each month, which will help BlackLine understand the effectiveness of their ads.
To solve a similar problem about calculating rates, solve this TikTok SQL Interview Question within DataLemur's online SQL code editor:
SQL Question 5: What's the difference between a one-to-one and one-to-many relationship?
In database schema design, a one-to-one relationship between two entities is characterized by each entity being related to a single instance of the other. An example of this is the relationship between a US citizen and their social-security number (SSN) - each citizen has one SSN, and each SSN belongs to one person.
On the other hand, a one-to-many relationship is when one entity can be associated with multiple instances of the other entity. A teacher's relationship with their classes is an example of this - a teacher can teach many classes, but each class is only associated with one teacher.
SQL Question 6: Average Revenue per Product
Assume that BlackLine wants to determine the average revenue per product per month, given a table that records sold items and their respective prices. Below are the sample tables:
Example Input:
| sale_id | product_id | sale_date | quantity | price_per_unit |
|---|---|---|---|---|
| 1 | 10001 | 2022-06-08 | 2 | 1200 |
| 2 | 10002 | 2022-06-18 | 1 | 990 |
| 3 | 10001 | 2022-07-05 | 1 | 1200 |
| 4 | 10003 | 2022-07-01 | 3 | 850 |
| 5 | 10002 | 2022-07-15 | 2 | 990 |
Example Input:
| product_id | product_name |
|---|---|
| 10001 | Product A |
| 10002 | Product B |
| 10003 | Product C |
We would like to find the average revenue for each product on a monthly basis.
Answer:
The PostgreSQL query needed to solve this would look similar to:
This query first joins the and tables on the . It then groups the data by the month of the and the product's name. For each group, it calculates the average of the product of and , giving the average revenue per product for each month. It finally orders the resulting data first by the month, and then by the product's name.
SQL Question 7: What's the difference between window functions and ?
While both and are used to rank rows, the key difference is in how they deal with ties.
RANK(): When there's a tie, 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, 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 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 BlackLine:
To rank these salespeople, we could execute the following query:
The result of this query would be:
| name | deals_closed | rank | dense_rank |
|---|---|---|---|
| Akash | 50 | 1 | 1 |
| Brittany | 50 | 2 | 1 |
| Carlos | 40 | 3 | 2 |
| Dave | 40 | 4 | 3 |
| Eve | 30 | 5 | 3 |
| Farhad | 10 | 6 | 4 |
As you can see, the 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 , gives both Akash and Brittany a rank of 1, since they are tied, then moves on to rank 2 for Carlos.
SQL Question 8: Find BlackLine Employees based on Role and Location
As a team leader at BlackLine, you are in need of direct contact with employees who are part of the "Developer" role and are situated in "Los Angeles, CA" to discuss an important software update. Use SQL queries to return the "full_name" and "email" of these employees.
Example Input:
| employee_id | full_name | role | location | |
|---|---|---|---|---|
| 101 | John Smith | Developer | Los Angeles, CA | johnsmith@blackline.com |
| 102 | Lisa Simpson | Analyst | Los Angeles, CA | lisasimpson@blackline.com |
| 103 | Steve Rogers | Developer | New York, NY | steverogers@blackline.com |
| 104 | Tony Stark | Developer | Los Angeles, CA | tonystark@blackline.com |
| 105 | Bruce Wayne | Marketing | Gotham, NY | brucewayne@blackline.com |
Example Output:
| full_name | |
|---|---|
| John Smith | johnsmith@blackline.com |
| Tony Stark | tonystark@blackline.com |
Answer:
This query selects the and from the table where is "Developer" and is "Los Angeles, CA". It filters the table based on the given conditions to return only the required records. The LIKE keyword in the SQL query allows us to filter out the data based on a specific pattern or string. The AND operator used here allows us to use multiple conditions in a WHERE clause.
SQL Question 9: Determine highest grossing items in different regions
Given two tables, one representing the company's customers and another representing the company's sales, write a SQL query to determine which item has achieved the highest sales in each region.
The table has the following 5 fields:
- : a unique identifier for each customer
- : the first name of the customer
- : the last name of the customer
- : the email used by the customer
- : the region where the customer is located
The table has the following 5 fields:
- : unique identifier for each sale
- : identifier for the item sold
- : identifier for the customer who made the purchase
- : number of items sold in the sale
- : the date the sale was made
Example Input:
| customer_id | first_name | last_name | region | |
|---|---|---|---|---|
| 123 | John | Doe | johndoe@example.com | North |
| 456 | Jane | Smith | janesmith@example.com | South |
| 789 | Bob | Johnson | bobjohnson@example.com | West |
| 112 | Alice | Williams | alicewilliams@example.com | East |
| 113 | Charlie | Brown | charliebrown@example.com | East |
Example Input:
| sale_id | item_id | customer_id | quantity | sale_date |
|---|---|---|---|---|
| 1 | 1001 | 123 | 5 | 2021-01-01 |
| 2 | 2002 | 456 | 3 | 2021-02-02 |
| 3 | 3003 | 789 | 2 | 2021-03-03 |
| 4 | 1001 | 112 | 1 | 2021-04-04 |
| 5 | 3003 | 113 | 4 | 2021-05-05 |
Answer:
The query first joins the customers and sales table using the customer_id. Then it groups the data by item_id and region and calculates the total sales for each item in each region. In the second part of the query, it groups the data by region and selects the item with the highest sales in each region.
Since joins come up so often during SQL interviews, take a stab at this Snapchat SQL Interview question using JOINS:
BlackLine SQL Interview Tips
The best way to prepare for a BlackLine SQL interview is to practice, practice, practice.
In addition to solving the above BlackLine SQL interview questions, you should also solve the 200+ FAANG SQL Questions on DataLemur which come from companies like Google, Uber, and Microsoft.
Each SQL 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 answer and have it checked.
To prep for the BlackLine SQL interview it is also useful to solve SQL questions from other tech companies like:
However, if your SQL skills are weak, forget about jumping right into solving questions – refresh your SQL knowledge with this SQL tutorial for Data Analytics.
This tutorial covers SQL concepts such as aggregate functions like SUM()/COUNT()/AVG() and math functions – both of which come up frequently in SQL job interviews at BlackLine.
BlackLine Data Science Interview Tips
What Do BlackLine Data Science Interviews Cover?
Besides SQL interview questions, the other types of problems to prepare for the BlackLine Data Science Interview are:
- Probability & Stats Questions
- Python or R Coding Questions
- Product-Sense Questions
- ML Modelling Questions
- Resume-Based Behavioral Questions
How To Prepare for BlackLine Data Science Interviews?
To prepare for BlackLine Data Science interviews read the book Ace the Data Science Interview because it's got:
- 201 interview questions sourced from FAANG tech companies
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