10 Teleflex SQL Interview Questions (Updated 2025)

Teleflex employees use SQL to analyze medical product data, ensuring that quality assurance standards are met by tracking product performance and safety metrics. They also rely on SQL to gather information from manufacturing databases, helping them find ways to make their production processes more efficient, this is the reason why Teleflex evaluates jobseekers with SQL query questions during interviews for Data Science and Data Engineering positions.

Thus, to help you practice, here’s 10 Teleflex SQL interview questions – how many can you solve?

10 Teleflex SQL Interview Questions

SQL Question 1: Identify the Top Teleflex Customers

Teleflex is a global provider of medical technologies in various clinical fields. In Teleflex's database, there is a sales table that records information about every product purchased by a customer. The sales table has the following schema:

The task is to write a SQL query to identify customers who are "whale users" for Teleflex. For this task, define a "whale user" as a customer who contributes to the top 10% of total revenue for the company. Provide the customer_id and total revenue they have generated.

sales Example Input:

Answer:

WITH total_revenue AS (
    SELECT SUM(revenue) as total_rev
    FROM sales
),
cust_revenue AS (
    SELECT customer_id, SUM(revenue) as cust_rev
    FROM sales
    GROUP BY customer_id
)
SELECT cr.customer_id, cr.cust_rev
FROM cust_revenue cr, total_revenue tr
WHERE cr.cust_rev > (tr.total_rev * 0.10);

This query first calculates the total revenue generated (total_revenue). It then groups sales by customer_id , summing the revenue generated by each customer (cust_revenue). The final SELECT statement retrieves customers whose total revenue generated (cust_rev) exceeds 10% of the company's total revenue.

To solve a super-customer analysis question on DataLemur's free interactive SQL code editor, try this Microsoft SQL Interview problem:

Check out the latest updates from Teleflex, a company dedicated to enhancing medical technology and improving patient outcomes! Understanding their innovations can give you a deeper insight into the evolving landscape of medical devices and their significance in healthcare.

SQL Question 2: Top 3 Salaries

Imagine there was a table of Teleflex employee salary data. Write a SQL query to find the top 3 highest paid employees in each department.

Teleflex employees Example Input:

department Example Input:

Example Output:

Solve this question and run your code right in DataLemur's online SQL environment:

Answer:

We use the DENSE_RANK() window function to generate unique ranks for each employee's salary within their department, with higher salaries receiving lower ranks. Then, we wrap this up in a CTE and filter the employees with a ranking of 3 or lower.

WITH ranked_salary AS (
  SELECT
    name,
    salary,
    department_id,
    DENSE_RANK() OVER (
      PARTITION BY department_id ORDER BY salary DESC) AS ranking
  FROM employee
)


SELECT
  d.department_name,
  rs.name,
  rs.salary
FROM ranked_salary AS rs
INNER JOIN department AS d
   ON rs.department_id = d.department_id
WHERE rs.ranking <= 3
ORDER BY d.department_id, rs.salary DESC, rs.name ASC;

If the solution above is hard to understand, you can find a step-by-step solution with hints here: Top 3 Department Salaries.

SQL Question 3: Can you describe the different types of joins in SQL?

In SQL, a join is used to combine rows from different tables based on a shared key or set of keys, resulting in a single merged table of data.

There are four distinct types of JOINs: INNER JOIN, LEFT JOIN, RIGHT JOIN, and FULL OUTER JOIN.

(INNER) JOIN: Retrieves records that have matching values in both tables involved in the join.

SELECT *
FROM Table_A
JOIN Table_B;
SELECT *
FROM Table_A
INNER JOIN Table_B;

LEFT (OUTER) JOIN: Retrieves all the records/rows from the left and the matched records/rows from the right table.

SELECT *
FROM Table_A A
LEFT JOIN Table_B B
ON A.col = B.col;

RIGHT (OUTER) JOIN: Retrieves all the records/rows from the right and the matched records/rows from the left table.

SELECT *
FROM Table_A A
RIGHT JOIN Table_B B
ON A.col = B.col;

FULL (OUTER) JOIN: Retrieves all the records where there is a match in either the left or right table.

SELECT *
FROM Table_A A
FULL JOIN Table_B B
ON A.col = B.col;

SQL Question 4: Analyze Monthly Average Sales Using Window Function

Teleflex is a global provider of medical technology products. Assume, you have access to a dataset of their monthly sales as a Data Analyst. The dataset, sales, contains data for each purchase made, its date, the product_id of the purchased product, and the unit price.

Your task is to write a SQL query that calculates the average unit price of each product for each month, considering only the latest transaction of a product for each customer in that month. The purpose of this task is to track the average price of each product on a monthly basis.

Here is the sales dataset:

sales Example Input:

You should output a table that includes the month, product_id, and the average unit price for that product in the month.

Example output:

Answer:

Here is a PostgreSQL query that solves this problem:

SELECT
    EXTRACT(MONTH FROM sale_date) AS month,
    product_id,
    AVG(unit_price) OVER (PARTITION BY product_id, DATE_TRUNC('month', sale_date)) AS avg_unit_price
FROM (
    SELECT 
        sale_date,
        customer_id,
        product_id,
        unit_price,
        ROW_NUMBER() OVER (PARTITION BY customer_id, product_id, DATE_TRUNC('month', sale_date) ORDER BY sale_date desc) AS row_num
    FROM 
        sales
) t 
WHERE
    t.row_num = 1
ORDER BY
    month, product_id;

In this query, an inner subquery is first used to assign a row number to each sale transaction for each customer for each product in each month, with the latest transaction having a row number of 1. This restricts our calculation to only consider the latest unit price for each customer for each product every month. Function DATE_TRUNC is used to extract the month from the timestamp. The outer query then calculates the average unit price for each product for each month using a window function. The result is ordered by month and product for clarity.

To practice a related window function SQL problem on DataLemur's free interactive coding environment, try this Amazon SQL question asked in a BI Engineer interview:

SQL Question 5: What's the difference between a one-to-one vs. a one-to-many relationship between two entities? Give examples.

When designing a database schema, 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 car and a license plate - each car has one license plate, and each license plate belongs to one car.

On the other hand, a one-to-many relationship is when one entity can be associated with multiple instances of the other entity. For example, a person can have multiple email addresses, but each email address only relates back to one person.

SQL Question 6: Filter Customer Records for Teleflex

Teleflex is a global provider of medical technologies designed to improve the health and quality of people’s lives. They apply purpose-driven innovation with a patient-centric approach. As part of their operations, they would like to view a filtered list of their customers based on some criteria.

Based on that requirement, create a SQL query to get a list of customers who are 'active', from the 'USA', and whose 'last_order_date' is greater than '01/01/2022'.

Here is sample customer data:

customers Example Input:

Example Output:

Answer:

SELECT customer_id, name
FROM customers
WHERE status = 'active' AND country = 'USA' AND last_order_date > '01/01/2022'

This query will check each record in the customers table and only select those where the status is 'active', the country is 'USA', and the last_order_date is after January 1, 2022. The resulting table will display the customer_id and name of these customers.

SQL Question 7: What are the benefits of normalizing a database?

There are several advantages to normalizing a database, including less redundancy, more flexibility, and better performance.

• Less Redundancy: Normalization reduces redundancy by breaking down a larger, more general table into smaller, more specific tables. This reduces the amount of data that needs to be accessed for queries.

• More Flexibility: Normalization makes it easier to modify the structure of the database, as there is less redundancy, so it allows you to make changes to one table without affecting others. This makes it easier to adapt the database to changing business needs (a very real reality at Teleflex!)

• Better 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 can result in faster query times and better overall performance.

SQL Question 8: Average Daily Call Durations

For a global manufacturer of medical and surgical products like Teleflex, Service is an important department. They might ask you to find the average duration of customer service calls per department on a daily basis.

Assume you have the following two tables: department and service_calls.

department Example Input:

service_calls Example Input:

Your task is to write a SQL query to find the average duration of calls in seconds per department on a daily basis.

Answer:

SELECT d.dept_name, 
       date(s.call_date) as date_of_call, 
       AVG(s.call_duration_in_seconds) as average_call_duration
FROM department d
JOIN service_calls s ON d.dept_id = s.dept_id
GROUP BY d.dept_name, date_of_call
ORDER BY date_of_call, d.dept_name;

This SQL query works by joining the department and the service_calls tables on the dept_id. The GROUP BY clause groups the data by the department name (from department table) and the call_date (from service_calls table). The AVG function is then used to calculate the average call duration in seconds. The ORDER BY clause sorts the data first by date_of_call then by department name.

To practice a very similar question try this interactive Verizon International Call Percentage Question which is similar for calculating percentages or this Amazon Average Review Ratings Question which is similar for calculating averages.

SQL Question 9: Click-through to Conversions Rates for Teleflex

Teleflex is a global provider of medical technologies designed to improve the health and quality of people’s lives. The Teleflex marketing team has been running various digital advertising campaigns, and they need help analyzing the performance of these campaigns. The goal is to understand how well the digital ads are performing in terms of click-through and conversions (product purchase).

Teleflex tracks two main actions:

1. When a potential customer (identified by a unique user_id) clicks on a digital ad. This is recorded in the ad_clicks table.
2. When a potential customer purchases a product. This is recorded in the product_orders table.

Write an SQL query that calculates the total click-through-rate (CTR) and conversion rate for each campaign_id. The rates should be defined as follows:

• Click-through-rate (CTR) = (Number of Clicks / Total Impressions) * 100
• Conversion Rate = (Number of Orders / Number of Clicks) * 100

Assume that:

• If a user_id is present in the ad_clicks table, this means the user has seen the ad (impression).
• If a user_id is present in the product_orders table, this means the user has purchased a product.

ad_clicks Example Input:

product_orders Example Input:

Answer:

SELECT c.campaign_id,
       (COUNT(DISTINCT c.user_id)) AS total_impressions,
       (COUNT(DISTINCT o.user_id)) AS total_orders,
       ((COUNT(DISTINCT c.user_id))::decimal / (COUNT(*))::decimal * 100) AS ctr,
       ((COUNT(DISTINCT o.user_id))::decimal / (COUNT(DISTINCT c.user_id))::decimal * 100) AS conversion_rate   
FROM ad_clicks c
LEFT JOIN product_orders o ON c.campaign_id = o.campaign_id AND c.user_id = o.user_id
GROUP BY c.campaign_id;

This query first joins the ad_clicks and product_orders tables on the campaign_id and user_id columns. It then finds the total number of impressions and orders for each campaign, which are used to calculate the click-through rate (CTR) and conversion rate, respectively.

To practice a similar problem on DataLemur's free interactive SQL code editor, attempt this SQL interview question asked by Facebook:

SQL Question 10: Can you define what a database index is, and give some examples of different types of indexes?

A database index is a way to optimize the performance of a database by reducing the amount of data that needs to be searched to retrieve a record.

There are several types of indexes that can be used in a database:

1. Primary index: a unique identifier is used to access the row directly.
2. Unique index: used to enforce the uniqueness of the indexed columns in a table.
3. Composite index: created on multiple columns of a table, is used to speed up the search process for multiple columns
4. Clustered index: determines the physical order of the data in a table

For a concrete example, say you had a table of Teleflex customer payments with the following columns: payment_id, customer_id, payment_amount, and payment_date.

Here's what a clustered index on the payment_date column would look like:

CREATE CLUSTERED INDEX payment_date_index ON teleflex_customer_payments (payment_date)

A clustered index on the payment_date column would determine the physical order of the records in the table based on the payment_date. This means that the records with the earliest payment_date values would be stored together physically in the table, followed by records with later payment_date values.

Having a clustered index on the payment_date column can speed up queries that filter or sort the data based on the payment_date, as the records are already physically sorted in the table. For example, if you want to retrieve all the payments made in the month of January, the database can use the clustered index to quickly locate and retrieve the desired records without having to search through the entire table.

How To Prepare for the Teleflex SQL Interview

The key to acing a Teleflex SQL interview is to practice, practice, and then practice some more! Besides solving the earlier Teleflex SQL interview questions, you should also solve the 200+ DataLemur SQL Interview Questions which come from companies like Google, Uber, and Microsoft.

Each SQL question has multiple hints, detailed solutions and best of all, there is an online SQL coding environment so you can instantly run your query and have it checked.

To prep for the Teleflex SQL interview you can also be a great idea to practice SQL problems from other healthcare and pharmaceutical companies like:

• West Pharma SQL questions
• Hill-Rom Holdings SQL questions
• Change Healthcare SQL questions
• ResMed SQL questions

However, if your SQL coding skills are weak, forget about jumping right into solving questions – strengthen your SQL foundations with this SQL interview tutorial.

This tutorial covers SQL concepts such as aggreage functions like MIN()/MAX() and handling dates – both of which show up often during Teleflex SQL interviews.

Teleflex Data Science Interview Tips

What Do Teleflex Data Science Interviews Cover?

In addition to SQL query questions, the other topics to prepare for the Teleflex Data Science Interview are:

• Statistics Interview Questions
• Python or R Programming Questions
• Product Metrics Interview Questions
• ML Interview Questions
• Behavioral Interview Questions

How To Prepare for Teleflex Data Science Interviews?

To prepare for the Teleflex Data Science interview make sure you have a strong understanding of the company's values and company principles – this will be important for acing the behavioral interview. For the technical Data Science interviews, get ready by reading Ace the Data Science Interview. The book's got:

• 201 Interview Questions from Facebook, Google & startups
• A Crash Course covering Product Analytics, SQL & ML
• Great Reviews (1000+ reviews, 4.5-star rating)