Data Analysts and Data Engineers at Enghouse Systems write SQL queries almost every single workday. They use SQL for analyzing customer interaction data for optimization of communication solutions and managing databases for maintaining software product quality. For this reason Enghouse Systems asks prospective hires SQL coding interview questions.
To help you study for the Enghouse Systems SQL interview, we've curated 9 Enghouse Systems SQL interview questions in this article.
Enghouse Systems is a global software and services company. We have a database that records all purchases made by customers. We would like to identify the customers who have made the most number of purchases over the last year. For this, please write a SQL query to generate a list of top 10 customers with the most number of purchases.
Consider the table where each row signifies a unique transaction.
transaction_id | customer_id | purchase_date | product_id | amount_paid |
---|---|---|---|---|
1201 | 3012 | 2021-09-15 | 3001 | 125.5 |
3490 | 5641 | 2021-09-20 | 1908 | 70.6 |
9310 | 3012 | 2021-10-01 | 1201 | 200.0 |
7645 | 7623 | 2021-10-05 | 3001 | 125.5 |
6871 | 9011 | 2021-10-11 | 1908 | 70.6 |
We want to know who the top 10 customers by the number of purchases are. Please consider only transactions in the last one year.
customer_id | total_purchases |
---|---|
3012 | 2 |
5641 | 1 |
7623 | 1 |
9011 | 1 |
This SQL query will group transactions by customer_id, count the transactions for each customer within the last year, order the customers by the number of transactions in descending order and finally limit the result to top 10.
To work on another SQL customer analytics question where you can code right in the browser and have your SQL code instantly graded, try this Walmart Labs SQL Interview Question:
Given a table of Enghouse Systems employee salary data, write a SQL query to find the 2nd highest salary at the company.
employee_id | salary |
---|---|
1 | 2500 |
2 | 800 |
3 | 1000 |
4 | 1200 |
second_highest_salary |
---|
1200 |
Try this interview question interactively on DataLemur:
You can find a detailed solution here: 2nd Highest Salary.
The primary key of a table is a column or combination of columns that serves to uniquely identify each row in the table. To define a primary key in a SQL database, you can use the constraint.
For instance, consider a table of :
In this example, the column is the primary key of the Enghouse Systems employees table.
Primary keys are important in databases for several reasons:
Given a database of calls handled by agents at Enghouse Systems with an 'agent_calls' table, write a SQL query to determine the average call length per agent for each day.
call_id | agent_id | call_date | call_length(minutes) |
---|---|---|---|
101 | 1 | 2022-06-20 | 15 |
102 | 1 | 2022-06-20 | 20 |
103 | 2 | 2022-06-20 | 25 |
104 | 1 | 2022-06-21 | 30 |
105 | 2 | 2022-06-22 | 35 |
call_date | agent_id | avg_call_length |
---|---|---|
2022-06-20 | 1 | 17.5 |
2022-06-20 | 2 | 25.0 |
2022-06-21 | 1 | 30.0 |
2022-06-22 | 2 | 35.0 |
Query to solve this problem:
This code works by applying the function over partitions defined by the and . This means it computes the average call length for each distinct pair of and . The result is then ordered by and , hence you can see the average call length per agent for each day.
p.s. Window functions show up super often during SQL interviews, so practice the 27+ window function questions on DataLemur
An inner join only includes rows from both tables that satisfy the join condition, whereas a full outer join includes all rows from both tables, regardless of whether they match the join condition or not.
For a concrete example, imagine you had two database tables, an advertising campaigns table which had information on Google Ads keywords and how much was bid for each keyword, and a Enghouse Systems sales table, which has data on how many products were sold and which Google Ads keyword drove that sale.
: retrieves rows from both tables where there is a match in the shared key or keys. For example, an INNER JOIN between the table and the table could be performed using the keyword column as the shared key. This would retrieve only the rows where the keyword in the table matches the keyword in the table.
: retrieves all rows from both tables, regardless of whether there is a match in the shared key or keys. If there is no match, values will be returned for the columns of the non-matching table.
Enghouse Systems wants to monitor its sales and manage its inventory. The company sells different software products, and they want to keep track of the product sales per region. Each sale belongs to a particular salesperson, and they also like to know which salesperson has the highest sales in a particular region.
They also want to track their product inventory in different warehouses. They need to know which product has low stock, and based on the sales data, they may want to transfer products from one warehouse to another.
Design tables to model this situation, and write a SQL query to find out the salesperson with the highest sales in each region.
sales_id | salesperson_id | region_id | product_id | sales_amount |
---|---|---|---|---|
001 | 101 | 201 | 501 | 1000 |
002 | 102 | 202 | 502 | 1500 |
003 | 103 | 203 | 503 | 3200 |
004 | 101 | 201 | 504 | 2000 |
005 | 102 | 202 | 503 | 4000 |
salesperson_id | name |
---|---|
101 | John |
102 | Max |
103 | Alice |
region_id | region_name |
---|---|
201 | North |
202 | South |
203 | East |
product_id | product_name |
---|---|
501 | Software1 |
502 | Software2 |
503 | Software3 |
504 | Software4 |
region_name | highest_salesperson | sales_amount |
---|---|---|
North | John | 3000 |
South | Max | 5500 |
East | Alice | 3200 |
The above query joins the , , and tables using the respective IDs. It then groups the data by and the salesperson name. The aggregate function is used to find the highest sales amount by each salesperson for each region.
Denormalization is the practice of altering a database schema in a way that breaks the normalization rules (1st, 2nd, 3rd normal forms).
Here's 3 reasons to de-normalize a database at Enghouse Systems:
Improved performance: Denormalization can often improve performance by reducing the number of expensive join operations required to retrieve data. This is particularly useful when the database is being used for online analytical processing (OLAP) as frequent joins across multiple tables can be slow and costly.
Scalability: Denormalization can increase the scalability of a database by requiring less data to be read and processed when executing a query. This can be beneficial when the database is expected to handle a high volume of queries.
Simplification: One way to simplify the design of a database is by using denormalization to reduce the number of tables and relationships that need to be managed. This can make the database easier to understand and maintain.
In "Enghouse Systems", which is a telecommunications and contact center software provider, one key metric to measure performance is the average duration of customer calls per agent. As a data analyst, you are asked to provide an SQL query that pulls up each agent's average call duration for the most recent month.
The table keeps track of all calls made:
call_id | agent_id | customer_id | start_time | end_time |
---|---|---|---|---|
1001 | 001 | 101 | 07/20/2022 09:30:00 | 07/20/2022 09:34:00 |
1002 | 001 | 102 | 07/20/2022 09:40:00 | 07/20/2022 10:00:00 |
1003 | 002 | 103 | 07/20/2022 09:40:00 | 07/20/2022 10:10:00 |
1004 | 002 | 104 | 07/20/2022 10:10:00 | 07/20/2022 10:30:00 |
1005 | 001 | 105 | 07/20/2022 10:15:00 | 07/20/2022 10:45:00 |
agent_id | avg_call_duration(min) |
---|---|
001 | 24.6667 |
002 | 25.0000 |
This SQL query calculates the average duration of calls per agent for the most current month. It first extracts the difference between and to get the call duration in seconds (using PostgreSQL's and functions), and then converts it to minutes by dividing by 60. It then groups these calculations by to get the average call duration per agent. The function is used to ensure we are only considering data from the current month.
To practice a very similar question try this interactive Verizon International Call Percentage Question which is similar for calculating percentage based on telecom data or this Amazon Server Utilization Time Question which is similar for time duration calculation.
As part of Enghouse System's analytics team, you are assigned to analyze the customers acquired from the recent business deal. Enghouse Systems has just acquired another company and thus, added that company's customers to its own customer base. The company already had a subscription model and we need to check how many of these new customers had active subscriptions at the time of acquisition, and how many of those continued their subscription after acquisition.
The table has the following schema:
customer_id | acquisition_date |
---|---|
101 | 2021-06-01 |
102 | 2021-06-01 |
103 | 2021-06-01 |
104 | 2021-06-02 |
The table has the following schema:
customer_id | subscription_start_date | subscription_end_date |
---|---|---|
101 | 2021-05-01 | 2021-12-31 |
102 | 2021-05-01 | 2021-07-01 |
103 | 2021-06-01 | 2021-09-01 |
104 | 2021-06-02 | 2021-06-10 |
Write a SQL query to find the number of customers who had an active subscription on the acquisition date and the number of these customers who continued their subscription after acquisition.
This PostgreSQL query joins the table with the table where the acquisition date is between the subscription start and end dates. The resulting table includes all the customers with active subscriptions during the acquisition. Then within this result, it counts the number of customers who continued their subscription after the acquisition date. The final result returns these two counts.
Since joins come up frequently during SQL interviews, try this interactive Snapchat Join SQL question:
The best way to prepare for a Enghouse Systems SQL interview is to practice, practice, practice. In addition to solving the above Enghouse Systems SQL interview questions, you should also solve the 200+ SQL questions on DataLemur which come from companies like Amazon, Microsoft, Meta, and smaller tech companies.
Each problem on DataLemur has hints to guide you, detailed solutions and most importantly, there is an online SQL code editor so you can right in the browser run your query and have it graded.
To prep for the Enghouse Systems SQL interview it is also wise to solve SQL questions from other tech companies like:
But if your SQL skills are weak, forget about jumping right into solving questions – strengthen your SQL foundations with this SQL tutorial for Data Analytics.
This tutorial covers topics including filtering groups with HAVING and LEAD/LAG – both of these show up frequently in SQL interviews at Enghouse Systems.
In addition to SQL interview questions, the other question categories to practice for the Enghouse Systems Data Science Interview are:
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