CVS Health Data Engineer Interview Questions + Guide in 2024

Over the years, CVS Health has redefined the future of healthcare by offering convenient and accessible services through its in-store clinics and telehealth options. CVS Health deals with millions of consumers daily and has a vast network of pharmacies, clinics, and online services.

Data engineers play an essential role in helping CVS Health effectively handle this massive customer base. They design, build, and maintain data pipelines that collect data from various CVS health services.

If you’re preparing for an upcoming CVS Health data engineer interview and want to know what to expect, you’re in the right place. This guide will walk you through the hiring process and interview questions and provide useful tips.

The interview process is usually straightforward, can take up to four weeks, and includes four to five rounds. Here’s more detail on each round:

Human Relations

The process begins with an HR screening. In this round, you will discuss your background, experience, and interest in the role. You might be asked about your resume, previous projects, motivations for applying to CVS Health, and availability. HR will then schedule the next three rounds, usually 45 minutes long, and conduct them back-to-back on the same day.

Coding

This round evaluates your technical skills, especially in programming languages commonly used in data engineering, such as Python, SQL, Scala, Java, or others, depending on CVS Health’s tech stack. You might be given coding challenges or asked to solve problems related to data manipulation, data structures, algorithms, or database queries. CVS Health mainly uses CoderPad for such assessments.

Behavioral Interview

This interview round evaluates your soft skills, communication abilities, and how you might fit within the team and company culture. You can expect questions about your previous work experience, approach to challenges and teamwork, and problem-solving methods.

Case Study

Lastly, you will be presented with a case study or a real-world data problem. You’ll be asked to analyze the situation and design a solution, and you may need to present your findings to an interview panel. This stage allows the company to evaluate your ability to think critically about data-related challenges, design efficient solutions, and communicate your ideas effectively.

During the four stages, you’ll encounter technical and behavioral questions covering topics including Python, SQL, probability and statistics, machine learning, ETL pipelines, and A/B testing. Below is a list of 20 questions previously asked in CVS Health data engineer interviews.

1. Discuss a time when you had to make a quick decision.

Unexpected things happen in the workplace, especially in the fast-paced data engineering environment. Interviewers seek candidates who can think on their feet and handle critical situations effectively at CVS Health.

How to Answer

Use the STAR method and clearly outline the situation, task, action, and result. Showcase your ability to make a quick decision under pressure, collaborate with a team, and achieve a positive outcome in any situation.

Example

“In my prior data engineering role, our real-time pharmacy wait time dashboard malfunctioned during a critical test, displaying inaccurate wait times right before launch. Delaying the launch meant frustrated customers and a missed deadline. I quickly assessed the situation, proposing a temporary solution with historical averages to provide some customer information. We communicated this to stakeholders and worked through the night to fix the core issue.”

2. Tell me about a time when you had to manage multiple data projects simultaneously.

Companies like CVS Health often require data engineers to work on multiple projects simultaneously. Hiring managers want to understand how you prioritize tasks and handle workloads.

How to Answer

Again, you can use the STAR method to answer such questions. Start with the context in which you managed multiple projects at once. Explain the projects and the actions you took to manage the workload effectively. Lastly, share the outcome.

Example

“In my past role, I faced multiple data projects. One was to build a customer loyalty data pipeline, while another was to optimize a pharmacy inventory data warehouse for faster queries. To keep things on track, I prioritized tasks, used project management tools, and held regular team meetings. Effective time blocking ensured each project received dedicated focus. When data quality issues arose with the loyalty program data, I adapted by implementing cleaning techniques and collaborating to improve future data collection. With this approach, I was able to finish the projects on time.”

3. Describe the steps you take when you hit a wall.

Data engineering projects can sometimes get complex. The interviewers want to hire those who can stay focused and motivated when faced with difficulties. This question tests your approach to solving problems, resilience, and ability to tackle challenges.

How to Answer

Start by explaining how you acknowledge and define the problem clearly. Describe how you seek additional information to better understand the situation. Mention if and how you consult colleagues or mentors for insights or alternative perspectives.

Example

“When I hit a wall, I first step back to see and define the problem clearly. Then, I troubleshoot independently, reviewing documentation and searching online resources. If I’m stuck, I don’t hesitate to collaborate with colleagues or seek guidance from senior engineers.”

4. How do you handle conflicts with colleagues or teammates?

Conflicts are common in a team setting. Employers focus on hiring data engineers with emotional intelligence because employees with great soft skills are more likely to collaborate effectively in a team setting at CVS Health.

How to Answer

Demonstrate your ability to manage conflicts. Start by acknowledging that conflicts are inherent to teamwork. Then, provide a structured approach or steps you take to resolve disputes.

Example

“Whenever I have a conflict with a colleague, my first step is to listen actively to their concerns without interrupting. I believe it’s crucial to understand their perspective fully before responding. After understanding their viewpoint, I share my perspective calmly and respectfully, aiming to find common ground or areas of compromise. If we can’t resolve the issue, I’m open to involving a mediator, like our supervisor, to help us navigate the conflict constructively.”

5. Discuss a project that did not go as planned. What happened, and what did you learn from the experience?

Data engineering is a dynamic field, and setbacks are inevitable. How you handle these setbacks, learn from them, and move forward matters most. Interviewers at CVS Health want to know what you do when you face failure, whether you get more motivated to finish the project or give up.

How to Answer

Select a challenging project you worked on that ultimately led to valuable lessons. Focus on what the experience taught you rather than assigning blame. Briefly explain the project and what went wrong. Then, describe the strategies you employed to overcome these challenges.

Example

“In a previous role, my team was tasked with integrating a new data analytics platform to enhance our marketing capabilities. Despite thorough planning, we encountered significant delays due to unforeseen data compatibility issues. It was a challenging time, as the project fell behind schedule, frustrating the team and stakeholders. Through this, I learned the importance of flexibility and proactive communication. To resolve the issue, I led a series of troubleshooting sessions and reached out to the platform’s support team for guidance, which eventually helped us identify and resolve our issues.”

6. Given a list of integers, write a function gcd to find the greatest common denominator between them.

The interviewer is testing your knowledge of basic algorithms and their implementation. Writing a function to find the GCD requires a good grasp of coding and the ability to write clean, efficient, and bug-free code.

How to Answer

Explain the importance of algorithmic efficiency and correctness in data engineering tasks. Then, walk the interviewer through your thought process and the steps you would take to implement the function. Discuss any optimizations that can make the function more efficient.

Example

“For this task, I’d use the Euclidean algorithm, a well-known and efficient method, to find the GCD of two numbers. This algorithm repeatedly subtracts the smaller number from the larger one until the two numbers become equal, which is the GCD. For multiple numbers, I’d find the GCD of the first two numbers, use that result to find the GCD with the next number, and so on until I’ve processed the entire list.”

def gcd(a, b):
    while b:
        a, b = b, a % b
    return a

def find_gcd_list(lst):
    result = lst[0]
    for i in lst[1:]:
        result = gcd(result, i)
        if result == 1:
            return 1  # No need to proceed further if the GCD is 1
    return result

# Example use case
numbers = [24, 60, 36]
print(f"The greatest common denominator is: {find_gcd_list(numbers)}")

7. What is the difference between ETL and data pipelines?

The hiring managers want to evaluate your understanding of fundamental data processing concepts to determine whether you can effectively apply them to CVS Health’s data ecosystem. This question evaluates your ability to differentiate between processes and checks whether you have a grasp of the right tools for various data-related tasks.

How to Answer

Briefly define ETL and data pipelines, highlighting their core functions. Emphasize the critical difference: ETL focuses on structured data and populating data warehouses, while data pipelines can handle various data types and have broader use cases.

Example

“ETL (extract, transform, load) pipelines are a specific type of data pipeline focusing on extracting data from source systems, transforming it into a structured format, and loading it into a destination, like a database or data warehouse. They are particularly used in scenarios where data needs to be cleaned, enriched, and standardized before analysis. Data pipelines, on the other hand, refer to the broader process of moving data from one system to another, which may or may not involve data transformation. Data pipelines can include real-time data processing, not just batch processing. They can feed data into systems for analytics, machine learning, or operational use without necessarily storing it in a data warehouse or database.”

8. Write an SQL query to find the total amount of each product sold for each month displayed in separate columns from a monthly sales table.

This question is fundamental and checks your SQL and data aggregation skills. Sales data is essential for retail companies like CVS Health. Analyzing product sales by month helps CVS Health monitor trends and make informed business choices.

How to Answer

Explain the logic behind your SQL query, such as grouping by product and month and using aggregate functions to sum the sales. Write the SQL query and briefly describe its output.

Example

“To solve this problem, I would write an SQL query that groups the sales data by product and month. I’d use the SUM() function to aggregate the total sales for each product per month. Assuming we have a table monthly_sales with columns product_id, sale_amount, and sale_date, the query might look something like this:

SELECT 
  product_id,
  SUM(CASE WHEN MONTH(sale_date) = 1 THEN sale_amount ELSE 0 END) AS January,
  SUM(CASE WHEN MONTH(sale_date) = 2 THEN sale_amount ELSE 0 END) AS February,
  SUM(CASE WHEN MONTH(sale_date) = 3 THEN sale_amount ELSE 0 END) AS March,
  -- Add cases for the remaining months
  SUM(CASE WHEN MONTH(sale_date) = 12 THEN sale_amount ELSE 0 END) AS December
FROM monthly_sales
GROUP BY product_id;

This query will return a row for each product with separate columns for the total sales amount for each month.”

9. Discuss the trade-offs between using a relational database and a NoSQL database for storing customer purchase data.

This question could be asked at a CVS Health data engineer interview because the company deals with large amounts of customer purchase data, requiring efficient storage, retrieval, and analysis. It tests your understanding of relational and NoSQL databases and whether you know when to use each.

How to Answer

Highlight the key differences between relational and NoSQL databases in terms of schema flexibility, scalability, query complexity, and consistency models. Then, discuss the trade-offs related to these aspects.

Example

“Relational databases, with their structured schema, provide strong ACID (atomicity, consistency, isolation, durability) properties, making them ideal for transactions requiring high consistency, such as financial records. They’re also beneficial for complex queries thanks to their mature SQL querying capabilities. On the other hand, NoSQL databases offer schema flexibility and scalability. They can handle large volumes of unstructured or semi-structured data, making them suitable for applications with rapidly evolving data models or those requiring horizontal scaling to manage large datasets or high throughput.”

10. Write a query to create a new table, named flight routes, that displays unique pairs of two locations.

Since CVS Health deals with large amounts of data daily, engineers must know how to create tables, define columns, and ensure data integrity. This question assesses your SQL skills and understanding of creating tables and manipulating data.

How to Answer

Explain the SQL query logic step by step. Ensure you include the necessary SQL commands to create the table with the required structure and constraints.

Example

CREATE TABLE flight_routes (
    route_id INT AUTO_INCREMENT PRIMARY KEY,
    location1 VARCHAR(50) NOT NULL,
    location2 VARCHAR(50) NOT NULL,
    CONSTRAINT unique_locations UNIQUE (location1, location2)
);

“Here, I created a table named ‘flight_routes’ with columns for ‘route_id,’ ‘location1,’ and ‘location2.’ ‘route_id’ is set as the primary key with AUTO_INCREMENT, ensuring each route has a unique identifier. ‘location1’ and ‘location2’ are VARCHAR columns to store the names of the two locations for each route. The UNIQUE constraint ensures that each pair of locations is unique, preventing duplicates in the table.”

11. What is the difference between batch processing and real-time stream processing?

At CVS Health, you will often develop data pipelines incorporating real-time and batch processing. This question checks your understanding of data processing paradigms and how you can apply them effectively to design data pipelines.

How to Answer

Explain the key differences between batch processing and real-time stream processing. Highlight scenarios where each would be more appropriate and how they can impact decision-making.

Example

“Batch processing involves collecting data over a period and then processing it all at once. This method is efficient for large volumes of data that do not require immediate action, such as daily sales reports or monthly inventory checks. It’s cost-effective for non-time-sensitive operations and allows for comprehensive analysis and resource optimization. Real-time stream processing, on the other hand, processes data as soon as it arrives, allowing for immediate actions and decisions. This is critical for applications that rely on the latest data for operational efficiency, such as monitoring patient health through wearable devices or managing pharmacy inventory based on current demand.”

12. Write a function pick_host to find the friend with the optimal location (minimum total distance for all friends) for hosting a party, given a list of friends’ names and their 3D coordinates.

This question evaluates your ability to solve optimization problems using programming skills. In a data-driven environment like CVS Health, it is important to quickly identify optimal solutions based on various data points.

How to Answer

Explain your approach to the problem clearly. Start by mentioning that you will calculate the total distance from each friend’s location to all others and identify the one with the minimum total distance.

Example

“To solve this, I’d write a function pick_host that iterates through each friend’s coordinates, calculating the sum of distances from their location to every other friend’s location. I’d use the Euclidean distance formula for 3D space because we’re dealing with 3D coordinates. The friend with the lowest total distance would be the optimal host.”

13. What is shuffling in Apache Spark, and why is it important in distributed data processing?

Data engineers at CVS Health handle large-scale data processing tasks, such as analyzing patient records, tracking medication sales, or optimizing inventory management. This question probes your understanding of shuffling in Spark, which is important for efficiently handling these distributed data processing tasks.

How to Answer

Explain shuffling as the redistribution of data across partitions. Emphasize its importance in reducing data movement across the cluster, enabling parallel processing, and facilitating operations like joins and aggregations.

Example

“Shuffling in Apache Spark refers to data movement across partitions in a distributed cluster. It’s crucial for several reasons. First, it ensures that data needed for a computation is co-located on the same executor node, reducing the need for data movement across the network and improving performance. Second, shuffling enables operations like joins and aggregations by redistributing the data to the appropriate partitions. Finally, shuffling enables Spark to parallelize operations effectively, distributing the workload across nodes in the cluster for faster processing.”

14. What analysis would you conduct on the user event data from a community forum app to recommend UI improvements?

Understanding user behavior and preferences is vital for creating a user-friendly experience in apps related to healthcare, including CVS Health. As a data engineer, you need to be able to analyze user event data to inform UI improvements that can lead to better health outcomes and customer satisfaction.

How to Answer

Discuss the types of analyses you would conduct on user event data, emphasizing how each analysis can inform UI improvements. Highlight the importance of a data-driven approach to understanding user behavior and making informed decisions that enhance user experience.

Example

“To recommend UI improvements for a community forum app, I would start by looking at user engagement metrics, such as session length and frequency of visits, to gauge overall user interest and identify potential drop-off points. I’d also conduct a navigation flow analysis to see how users move through the app and where they might encounter obstacles. Next, I’d look into feature usage data to understand which aspects of the app are most valuable to users and which might be underutilized or confusing. Additionally, analyzing error logs and user interruptions can provide direct insights into technical or navigational issues within the UI. Also, sentiment analysis on user feedback and discussions in the forum can offer qualitative insights into user experiences and perceptions of the app’s UI.”

15. How do you handle data deduplication in a large dataset using SPA data?

Data integrity and accuracy are critical in healthcare-related data processing, including at CVS Health. The interviewer wants to check your ability to identify and remove duplicate records to ensure data quality.

How to Answer

Explain the various methods to handle data deduplication in Spark, emphasizing scalability, efficiency, and impact on performance. You can also mention advanced techniques using Spark MLlib for complex scenarios.

Example

“Data deduplication is crucial for ensuring data quality in large datasets. In Spark, we have several methods. The dropDuplicates() function is a simple yet powerful way to remove all duplicate records based on all columns. However, for more granular control, we can define custom logic using Spark SQL functions to identify and remove duplicates based on specific columns we care about. Additionally, Spark MLlib libraries offer advanced techniques for complex deduplication scenarios involving fuzzy matching.”

16. How do you sort a 100GB file when you are constrained to only 10GB of RAM?

The interviewer wants to assess your understanding of techniques for handling large-scale data sorting with limited resources because CVS deals with massive datasets. They seek candidates with efficient sorting techniques for tasks like analyzing patient records, managing inventory, and processing transactions.

How to Answer

Describe the process of external sorting, particularly merge sort. Mention how breaking down the file into manageable chunks, sorting each chunk in memory, and then merging these sorted chunks can efficiently sort the entire file.

Example

“For sorting a 100GB file with only 10GB of RAM, we can’t rely on traditional in-memory sorting algorithms. In this situation, I’d employ an external sorting technique like merge sort. First, I’d split the large file into smaller chunks that can fit in memory, say 1GB chunks. Then, I’d sort each chunk individually using an efficient internal sorting algorithm. Finally, I’d iteratively merge these sorted chunks together, ensuring the overall sorted order is maintained. This process would use the available disk space for temporary files and efficiently sort the entire dataset.”

17. Can you discuss the challenges and considerations when migrating on-premise databases to the cloud?

Migrating sensitive healthcare data requires careful planning to ensure data privacy and regulatory compliance. The interviewer wants to evaluate your understanding of the challenges and considerations involved in migrating to the cloud.

How to Answer

Discuss the challenges and considerations in a structured manner, highlighting the importance of each factor in the context of CVS Health. You can mention challenges such as data security and cost management.

Example

“Migrating on-premise databases to the cloud involves addressing critical challenges like ensuring data security in line with healthcare regulations, minimizing downtime during large data transfers, and ensuring that applications remain compatible and performant in a new environment. Cost management is also key, as moving to the cloud changes the cost structure around data storage and processing.”

18. Explain the difference between the XGBoost and random forest algorithms and give an example of when you would use one over the other.

This question tests your knowledge of machine learning algorithms like XGBoost and random forest. Understanding the differences is important to select the right tool for specific tasks, ensuring CVS Health gets accurate and useful insights from data.

How to Answer

Briefly outline the fundamental differences between XGBoost and Random Forest, focusing on their algorithmic approach, strengths, and typical use cases. Provide an example scenario to illustrate when one algorithm might be preferred.

Example

“XGBoost, based on gradient boosting, is excellent for complex, high-dimensional datasets where feature interactions are crucial. This makes it valuable in healthcare in scenarios such as predicting patient readmission risks based on numerous patient attributes. On the other hand, random forest, using bagging, is more straightforward and robust out of the box. For instance, at CVS Health, if we’re developing a predictive model to identify high-risk patients for chronic disease management, we might lean towards XGBoost. However, if our goal is to understand which factors contribute most to patient medication adherence, we might choose random forest.”

19. Discuss the advantages and disadvantages of different NoSQL databases, such as MongoDB and Cassandra.

CVS Health seeks data engineers who understand the advantages and disadvantages of MongoDB and Cassandra and can make informed decisions when designing databases for healthcare applications. The right NoSQL database is necessary for CVS Health’s vast amounts of data.

How to Answer

Briefly discuss the advantages and disadvantages of MongoDB and Cassandra in CVS Health’s context. Provide examples or scenarios where each database would be a suitable choice, emphasizing the impact on healthcare data management and analysis.

Example

“MongoDB’s flexible schema and rich query language make it an excellent choice for evolving data structures, such as patient records with changing attributes over time. In contrast, Cassandra excels in scalability and high availability, making it ideal for real-time monitoring of medical devices across multiple locations. However, MongoDB’s challenge with data integrity across documents could pose risks in maintaining accurate patient records. For instance, CVS Health might use MongoDB for its patient portal, allowing dynamic updates to patient profiles and personalized health recommendations. On the other hand, Cassandra could power the backend system for monitoring medication inventories across CVS Health’s pharmacies, ensuring real-time updates and availability.”

20. How does random forest generate its forest, and what are the advantages of using it over algorithms like logistic regression?

The choice of machine learning algorithm depends on the specific problem and data characteristics. This question allows CVS Health interviewers to gauge your understanding of machine learning models, specifically ensemble learning techniques.

How to Answer

Briefly explain how random forest works, highlighting its generation of multiple decision trees and using their averaged predictions to improve model accuracy and prevent overfitting. Then, discuss the advantages of using random forest over logistic regression.

Example

“Random forest generates its forest by creating multiple decision trees during the training phase, with each tree made from a random subset of the data and features. Random forest offers advantages over logistic regression, particularly in CVS Health contexts, due to its flexibility in handling classification and regression tasks; management of large, high-dimensional datasets without feature selection; and proficiency in dealing with missing values. Unlike logistic regression, which assumes a linear relationship between independent variables and the outcome, random forest can model complex nonlinear relationships.”

Getting through a data engineer interview is not a cakewalk because there’s a lot of competition. It takes more than just solving technical problems. You’ll need a good strategy and the right skills to do well. In this section, we’ll share some tips to help you make it.

Research the Company

Research and understand CVS Health’s business, mission, values, and recent projects related to data engineering. This will help you tailor your answers in the interview accordingly.

Start preparing for your interview by following our data engineering learning path, which covers everything from the basics to advanced topics.

Review Basics

Before you dive into the complex questions that could be asked in the interview, review the basics. This includes Python, SQL, ETL, and Scala. Don’t forget the data engineering frameworks, platforms, and technologies, such as Hadoop, Spark, Kafka, Airflow, and AWS. Practice more extended problems step by step using our takehomes feature.

Level Up Coding

Practice coding challenges related to data manipulation, algorithms, and SQL questions. At Interview Questions, you can find a wide range of data engineering questions to challenge yourself.

Prepare Situational Questions

Be ready to discuss your previous experiences and how you handled challenges in data engineering projects. Structure your responses using the STAR method (situation, task, action, result). Make the most of our coaching feature to receive insider advice on refining your responses.

Mock Interviews

Remember to practice mock interviews here at Interview Query to become more comfortable with the interview format and receive valuable feedback.

What is the average salary for a data engineer role at CVS Health?

The average base salary for a data engineer at CVS Health is $123,000.

To learn more about data engineer salaries, check our comprehensive guide.

What other companies are hiring data engineers besides CVS Health?

In addition to CVS Health, consider applying to other healthcare companies, such as UnitedHealth Group, Anthem, Cigna, and Aetna. Take a moment to browse through their job listings and apply for positions that align with your preferences and skills.

Does Interview Query have job postings for the CVS health data engineer role?

Yes, you will find job openings for CVS Health on our job board, including the data engineer role. Keep an eye on the listings, as they are subject to change.

We hope this guide has equipped you with knowledge and tips to excel in your interview. Focus on improving your technical and interpersonal skills and stay informed about the latest developments in data engineering.

For more about CVS Health’s interview process, check out our main CVS Health interview guide. We also cover other roles, such as software engineer, data analyst, and data scientist. If you’re considering other positions, take a look at them, too.

For further insights and preparation, check out our guides on the top 100+ Data Engineer interview questions and case studies.

We hope you land your dream role at CVS Health soon! Best of luck!