9 Lightspeed POS SQL Interview Questions (Updated 2024)

At Lightspeed POS, SQL is used all the damn time for analyzing customer transactions for effective trend prediction, and for optimizing inventory management based on sales and customer preferences. That's why Lightspeed POS often tests SQL questions during interviews for Data Science and Data Engineering positions.

As such, to help you prepare for the Lightspeed POS SQL interview, we've collected 9 Lightspeed POS SQL interview questions – can you solve them?

9 Lightspeed POS SQL Interview Questions

SQL Question 1: Compute Monthly Average Rating for each Lightspeed POS Product

You have been handed a dataset that contains product reviews for the various products offered by Lightspeed POS. The dataset consists of columns: , , , and .

Write an SQL query to compute the average stars each product received on a monthly basis.

Example Input:

Example Output:

Answer:

This query first extracts the month from the in a common table expression (CTE). Then, it groups the reviews by and in the main query and applies the function to compute the average rating per product for each month. Finally, it orders the result by and .

For more window function practice, try this Uber SQL problem within DataLemur's interactive coding environment:

SQL Question 2: Determine the total daily sales per category

Lightspeed POS, a company that provides point of sale software, conducts millions of sales transactions daily. They support many businesses from different sectors. Each product is classified into a particular category. Management needs a daily report of total sales for each category to better understand the sales dynamics.

Design appropriate tables for this business scenario, create some sample data, and produce a SQL query that would generate a report of the total daily sales per category. Consider factors such as database performance and storing sales and product information efficiently.

Example Input:

Example Input:

Answer:

Use the following SQL block to generate the report:

This query joins the and tables using the field. The clause groups the sales data by and . The calculates the total sales for each category and ensures that the sales are reported daily. Finally, the clause sorts the resulting data by the sale date and then by category.

SQL Question 3: Can you explain the concept of a constraint in SQL?

Constraints are used to specify the rules concerning data in the table. It can be applied for single or multiple fields in an SQL table during the creation of the table or after creating using the ALTER TABLE command. The constraints are:

Say you were storing sales analytyics data from Lightspeed POS's CRM inside a database. Here's some example constraints you could use:

• PRIMARY KEY constraint: You might use a PRIMARY KEY constraint to ensure that each record in the database has a unique identifier. For example, you could use the "opportunity_id" field as the primary key in the "opportunities" table.

• FOREIGN KEY constraint: You might use a FOREIGN KEY constraint to link the data in one table to the data in another table. For example, you could use a foreign key field in the "opportunities" table to reference the "account_id" field in the "accounts" table.

• NOT NULL constraint: You might use a NOT NULL constraint to ensure that a field cannot contain a NULL value. For example, you could use a NOT NULL constraint on the "opportunity_name" field in the "opportunities" table to ensure that each opportunity has a name.

• UNIQUE constraint: You might use a UNIQUE constraint to ensure that the data in a field is unique across the entire table. For example, you could use a UNIQUE constraint on the "email" field in the "contacts" table to ensure that each contact has a unique email address.

• CHECK constraint: You might use a CHECK constraint to ensure that the data in a field meets certain conditions. For example, you could use a CHECK constraint to ensure that the "deal_probability" field in the "opportunities" table is a value between 0 and 100.

• DEFAULT constraint: You might use a DEFAULT constraint to specify a default value for a field. For example, you could use a DEFAULT constraint on the "stage" field in the "opportunities" table to set the default value to "prospecting"

SQL Question 4: Calculate Average Transaction Value

As a data analyst at Lightspeed POS, you are tasked to analyze transaction data from different stores using the Lightspeed POS system. Your task is to find out the average transaction value for each store in the first quarter of 2022.

Example Input:

Example Output:

Answer:

This query calculates the average transaction value per store for the first quarter of 2022. The WHERE clause is used to filter the transactions within the first quarter of 2022. The GROUP BY clause groups the data by store_id. The AVG() function calculates the average transaction value for each group.

To practice a very similar question try this interactive Uber User's Third Transaction Question which is similar for dealing with transaction data or this Stripe Repeated Payments Question which is similar for analyzing transaction records.

SQL Question 5: List a few of the ways you find duplicate records in a table in SQL.

"One creative way is to use the window function ROW_NUMBER() and partition by whichver columns you are loooking for duplicates in. If any row has a row_number() more than 1, then it's a duplicate!

You could also use COUNT(DISTINCT col1) vs. COUNT(*) and see if they return different numbers, indicating the prescence of duplicates!

SQL Question 6: Average Daily Sale Per Product

As a data analyst at Lightspeed POS, we need to understand how our products are selling on a daily basis. This includes understanding which products are on top of our sale list.

Write a SQL query that finds the average number of sales per day for each product ID for the month of June 2021.

For the purpose of this problem, let's use the following sample data from the "sales" table.

Example Input:

Expected output:

Example Output:

Answer:

This query selects the product ID and the average quantity sold as "avg_daily_sale". This is done for the entries of the "sales" table where the sale date is from the month of June 2021. Grouping by product ID allows us to get this average figure for each individual product.

SQL Question 7: What are the different kinds of joins in SQL?

Joins in SQL allow you to combine data from different tables based on a shared key or set of keys.

Four JOIN types are available in SQL. For an example of each one, say you had sales data exported from Lightspeed POS's Salesforce CRM stored in a PostgreSQL database, and had access to two tables: and .

• : retrieves rows from both tables where there is a match in the shared key or keys. For example, an INNER JOIN between the Sales table and the Customers table would retrieve only the rows where the customer_id in the Sales table matches the customer_id in the table.
• : retrieves all rows from the left table (in this case, the sales table) and any matching rows from the right table (the table). If there is no match in the right table, NULL values will be returned for the right table's columns.
• : retrieves all rows from the right table (in this case, the customers table) and any matching rows from the left table (the sales table). If there is no match in the left table, NULL values will be returned for the left table's columns.
• : retrieves all rows from both tables, regardless of whether there is a match in the shared key or keys. If there is no match, NULL values will be returned for the columns of the non-matching table.

SQL Question 8: Calculate Weighted Ratings

Given the sales and reviews of each product in the store, write a SQL query to calculate the weighted rating of each product. The weighted rating could be calculated as follows: . The sales data include the ID of the product, the date of the sale, and the quantity sold. The reviews data include the ID of the product, the rating (from 1 to 5 stars), and the date of the review. Calculate the final result using ROUND() function to round to 2 decimal places.

Example Input:

Example Input:

Answer:

This SQL code splits the problem into several portions. It first calculates the total amount of sales in the CTE, then calculates the total amount of sales per product in the CTE, and the average stars per product in the CTE. Finally, it joins these CTEs together and applies the weighted rating formula to calculate the final output.

To practice a very similar question try this interactive Amazon Average Review Ratings Question which is similar for calculating rating averages or this Wayfair Y-on-Y Growth Rate Question which is similar for dealing with sales data.

SQL Question 9: Count of Transactions Per Hour in a Day

Imagine you are a data analyst at Lightspeed POS. The business wants to know the peak hours of transactions for a specific day to better adjust staffing and resources. Please write a SQL query to get the count of transactions every hour for a particular date, August 1, 2022. Assume the time is stored in 24-hour format.

Example Input:

Example Output:

Answer:

The SQL query basically extracts the hour from the transaction timestamp, counts the number of transactions that happened during each hour while focusing on the specific day: '2022-08-01'. The results are then grouped by the hour and ordered in ascending order. The output table clearly indicates the number of transactions that happened every hour on August 1, 2022.

Preparing For The Lightspeed POS SQL Interview

The key to acing a Lightspeed POS SQL interview is to practice, practice, and then practice some more! Beyond just solving the earlier Lightspeed POS SQL interview questions, you should also solve the 200+ SQL questions from real Data Science & Analytics interviews which come from companies like Microsoft, Google, Amazon, and tech startups.

Each exercise has multiple hints, fully explained answers along with a discussion board to see how others solved it and most importantly, there is an interactive SQL code editor so you can instantly run your SQL query and have it checked.

To prep for the Lightspeed POS SQL interview you can also be useful to solve interview questions from other tech companies like:

• Shopify SQL questions

• Square SQL questions

• Salesforce SQL questions

• oracle SQL questions

In case your SQL query skills are weak, don't worry about diving straight into solving questions – refresh your SQL knowledge with this free SQL tutorial.

This tutorial covers things like aggreage functions like MIN()/MAX() and filtering groups with HAVING – both of which show up routinely during Lightspeed POS SQL interviews.

Lightspeed POS Data Science Interview Tips

What Do Lightspeed POS Data Science Interviews Cover?

In addition to SQL interview questions, the other types of questions tested in the Lightspeed POS Data Science Interview are:

• Stats Interview Questions
• Python Pandas or R Coding Questions
• Analytics and Product-Metrics Questions
• ML Modelling Questions
• Behavioral & Resume-Based Questions

How To Prepare for Lightspeed POS Data Science Interviews?

The best way to prepare for Lightspeed POS Data Science interviews is by reading Ace the Data Science Interview. The book's got:

• 201 Interview Questions from Google, Microsoft & tech startups
• A Refresher covering SQL, AB Testing & ML
• Amazing Reviews (900+ reviews, 4.5-star rating)