Top 22 Mastercard Data Engineer Interview Questions + Guide 2024

Essentially operating as a middleman between banks and businesses to facilitate electronic transactions, Mastercard powered over 333 million credit cards over Q1 2024 in the US alone, contracting over 1135 million cards worldwide. As a user-centric financial service provider, Mastercard operates on customer trust and satisfaction, ensured through proper fraud prevention and data-driven personalization.

Data engineers at Mastercard are the backbone of its operations, building systems to process real-time transaction data, identify suspicious activity, create customer segments, aid marketing campaigns, and support the development of machine learning models.

You’ve come to the right place to understand the interview process, decipher critical Mastercard Data Engineer interview questions, and prepare for the interview. So, let’s get started.

The interview process at Mastercard for a data engineer role typically involves multiple rounds designed to assess both your technical skills and behavioral fitness. While the exact process can vary based on the specific role and team, here’s a general overview of the process:

Initial Phone Screening

After your CV has been shortlisted, expect a recruiter to call for the initial phone screening. Your designated recruiter might discuss your experience, technical background, and interest in the role. They’re likely to ask you multiple behavioral questions to assess your alignment with the role and the company. You may also expect some foundational technical questions.

Technical Phone Screening

Data engineers at Mastercard are expected to be proficient in the communication and technical aspects of the job. The technical phone screening round focuses on your skills related to data structures and algorithms, SQL querying, programming languages, and sometimes big data technologies. You may also be asked to answer some product sense questions for data engineers.

Take-Home Challenge Round

Depending on your role and seniority, you may be asked to solve a take-home challenge to address real-life issues that may transpire at Mastercard. Your problem-solving and practical coding abilities will be assessed via this exercise. However, data engineers may also be handed case study visualization problems to solve.

Technical On-Site Interview Loop

If you’ve been able to impress the interviewers in the previous rounds, you’ll be asked to appear for an on-site interview loop, where they’ll delve deeper into your technical expertise with questions about data modeling and architecture, ETL processes, and data warehousing. Data quality validation, maintenance, and management questions may also be asked during this round.

Hiring Manager and Partner Interview

This round assesses your alignment with Mastercard’s values and ability to collaborate with teams. This is also an opportunity to discuss the role, the team, and your career aspirations with the hiring manager. Behavioral and basic product sense questions are asked during this round.

Here are a few questions that are frequently asked during Mastercard data engineer interview:

1. Describe a data project you worked on. What were some of the challenges you faced?
2. Talk about a time when you had trouble communicating with stakeholders. How were you able to overcome it?
3. What makes you a good fit for our company?
4. At Mastercard, how would you improve the customer experience for people primarily using cash?
5. Given the rise of contactless payments, how can Mastercard ensure security without compromising user experience?
6. Design a database system for Swipe Inc. to securely store and manage API keys, user details, and transaction data.
7. Given a table called employees, get the largest salary of any employee by department
8. Write a function named grades_colors to select only the rows where the student’s favorite color is green or red and their grade is above 90.
9. Write a function ugly_powers(s: set) -> bool that takes a set (s) and returns a Boolean value determining whether or not all the elements of set (s) are all ugly powers.
10. Write a query that returns columns representing the total number of bookings in the last 90 days, last 365 days, and overall.
11. Why is it standard practice to explicitly put foreign key constraints on related tables instead of creating a normal BIGINT field? When considering foreign key constraints, when should you consider a cascade delete or a set null?
12. Given two sorted lists, write a function to merge them into one sorted list.
13. You are given two ListNodes of a doubly linked list. Write a function pop_tail which removes the tail of the linked list in O(1)O(1) time. Return the head and tail of the linked list as a list. If the linked list is empty after removal, return None.
14. Given a list of strings of letters from a to z, create a function, sum_alphabet, that returns a list of the alphabet sum of each word in the string.
15. Write a query to identify the manager with the biggest team size.
16. How would you design or modify a database schema to track a customer’s address history, including their moves and the new occupants of those addresses?
17. Write a function to get a sample from a standard normal distribution.
18. What is the difference between Spark RDDs and dataframes? When would you use each?
19. Explain the concept of dimensional modeling. How does it differ from normalized modeling?
20. Given a dataset of customer demographics and purchase history, how would you segment customers based on their spending behavior?
21. Write a function range_vehicles that will give the number of vehicles between the start and end checkpoints.
22. Given an (integer) height h and base b, write a function draw_isosceles_triangle that returns the shape of the isosceles triangle using (a 2D list) of 0s and 1s, where 0 and 1 represent the space outside and inside of the triangle, respectively.

Preparing for a data engineer interview at Mastercard involves several key steps to ensure you cover the necessary technical and non-technical aspects. Here’s a comprehensive guide to help you prepare:

Understand the Job Role

Thoroughly read the job description to understand the specific skills and responsibilities required. Learn about Mastercard’s business model, products, services, and recent news. Understand how data engineering fits into their operations. Prepare your responses accordingly.

Technical Skills Preparation

Data engineering is a balance between engineering and analysis. Your Mastercard data engineer interviewer will expect you to be proficient in languages commonly used in data engineering, such as Python and SQL. You’d also need to understand data warehousing concepts, including ETL processes, data modeling, and tools like Amazon Redshift, to be considered as a potential candidate.

Furthermore, familiarize yourself with big data technologies like Hadoop, Spark, Hive, and Kafka, and have a firm grasp of relational and non-relational databases.

Prepare for Case-Studies

For your take-home round, prepare by solving the case studies on our website. You may also showcase your data projects in your portfolio to gain an edge. Many of the Mastercard case study questions focus on the practical implications of data architecture. You might also have to prepare a presentation to walk the interviewers through your findings.

Behavioral Interview Preparation

It’s imperative to understand Mastercard’s business and values to answer behavior questions. Go through their products, services, and core business model to learn the industry trends and challenges hindering Mastercard’s growth.

As you may be asked detailed questions about your past experiences during the data engineer interview, prepare examples of how you have used data to help drive decisions. As a prominent player in the finance sector, they might also expect their data engineers to understand the importance of data quality and governance. So, be prepared.

Participate in Mock Interviews

Mock interviews are exceptional tools to refine your responses and build confidence to communicate with both technical and non-technical stakeholders. Our P2P mock Interview Portal allows you to participate interactively with other candidates to analyze your preparedness.

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

The average base salary for a data engineer at Mastercard varies between $37,000 and $102,000, with an average of $91,000, based on location, experience, and specific role. The average total compensation, however, can even reach up to $116,000 in bonuses and stock options. Follow our Data Engineer Salary Guide to stay updated on industry standards.

What companies besides Mastercard are hiring data engineers?

Many tech giants and financial institutions hire data engineers. Popular companies include Amazon, Google, Facebook, Microsoft, Netflix, and various banks and fintech firms.

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

Yes, we do have job postings for the Mastercard data engineer role on our Job Board. However, we also recommend following the official career page for more updated job postings.

Landing a data engineer role at Mastercard requires a solid foundation in SQL, data structures, algorithms, and big data technologies. The interview process typically includes technical assessments, coding challenges, and behavioral rounds. Thorough preparation is crucial, including practicing coding, understanding Mastercard’s business, and honing problem-solving skills.

While data engineering might be your primary focus, you can explore other roles like data analyst, data scientist, or product manager in the Mastercard Main Interview Guide, as they often involve close collaboration with data engineers. All the best!