Visa Data Engineer Interview Questions + Guide in 2024

Visa Inc. has one simple vision—to make Visa the best payment method globally—for everyone, everywhere. Visa operates VisaNet, one of the world’s most advanced processing networks, capable of handling over 13,000 transactions per second. In 2024, for example, it will continue to innovate by enhancing Visa Acceptance Services to simplify digital payments for its merchants.

Data engineers play a vital role at Visa, enhancing VisaNet’s capabilities to handle massive data volumes, among other impressive responsibilities.

This interview guide provides a detailed overview of the Visa data engineer interview process.

It is designed for applicants like yourself who are interested in pursuing a data engineering role there. It includes a variety of commonly asked interview questions and practical tips to enhance your chances of securing the position.

Getting ready for an Data Engineer interview at Visa? The Visa Data Engineer interview span across 10 to 12 different question topics. In preparing for the interview:

• Know what skills are necessary for Visa Data Engineer roles.
• Gain insights into the Data Engineer interview process at Visa.
• Practice real Visa Data Engineer interview questions.

Interview Query regularly analyzes interview experience data, and we’ve used that data to produce this guide, with sample interview questions and an overview of the Visa Data Engineer interview.

The data engineer interview generally tests the following skills:

• Python coding
• SQL
• Database design
• Data architecture/big data technologies
• Soft skills

For the coding questions, expect a difficulty level of LeetCode’s easy to medium.

Please note that the questions and structure of the interview process will differ based on the team and function advertised in the job ad. Always read through the job description carefully while preparing your interview strategy.

The entire process can take 3 to 5 weeks.

Step 1: Initial Phone Call

Here, the recruiter will ask you exploratory questions to learn more about you and your interests, past projects, and skill sets that relate to the job role. They’ll also give you a detailed overview of the role. Prepare some responses for common behavioral and CV-based questions to ace this step. You can also use the opportunity to learn more about the next stages of the interview.

Step 2: Online Assessment

You’ll then need to pass a CodeSignal or HackerRank assessment test, which typically consists of Python, SQL, Docker, and Linux questions. The test will be a mix of LeetCode’s easy-to-medium coding questions and multiple-choice questions.

For this round, review lists, hashmaps, linked lists, time and space complexities of basic operations, and basic algorithms. For SQL, practice CASE WHEN statements, joins, and window functions.

Step 3: Technical Interviews

After the coding challenge, you’ll be invited to a series of interviews (3 to 4) over a couple of weeks. These will test your data structures and algorithms, systems design, behavioral, and OOP knowledge through both theoretical and situational, case-based questions. Each interview will be approximately 45 minutes.

Our top tip for technical interviews is to talk through problems as you solve them, as the interviewer will want to understand your thought process. For solving coding challenges, practice on whiteboards or Google Docs instead of IDEs, as you will not get any syntax support during a live coding round.

In this section, we’ll review the various interview questions that might be asked during a Visa data engineer interview, along with some tips on how to tackle them.

You can also check out this video to learn more about the ten different types of data engineering questions interviewers ask, before you start solving our list of questions below.

1. Tell me something about yourself that is not in your resume.

It allows the interviewer to understand what you believe your unique value-add is apart from technical skills.

How to Answer

Focus on a unique project or experience that shows your passion or creativity.

Example

“While my resume highlights my technical experience with data architectures and real-time processing systems, it doesn’t show my enthusiasm for mentorship. At my last job, I informally led a peer training group where we shared best practices for cloud-based data challenges. I find that teaching others really fosters a collaborative team environment. I believe this would help me contribute positively to continuous learning at Visa.”

2. Tell me about a time you went above and beyond in a project.

Data engineers willing to go the extra mile to enhance project outcomes are highly valued.

How to Answer

Choose an example from your professional experience where you took additional steps that were not expected in your role but significantly contributed to the project’s success.

Example

“I was part of a team tasked with optimizing our data warehousing processes. While my primary responsibility was to manage and optimize ETL workflows, I identified a broader opportunity to improve data quality across our entire pipeline. I proposed a project to implement a comprehensive data quality framework. I took the initiative to research and present a plan to our management, highlighting the long-term benefits of efficiency and reliability. After getting the green light, I led the development of this framework, collaborating with both the engineering and data analytics teams. This involved creating data validation rules, automating data quality checks, and integrating alerts for anomalies. The implementation of this framework resulted in a 12% reduction in data processing issues, significantly improving the efficiency of our data analytics operations.”

3. Tell me about a time you failed.

The ability to learn from mistakes is essential for Visa engineers to stay on the cutting edge of innovation.

How to Answer

Select a time when you faced a setback at work and focus on what you learned from the experience. To structure your response in an organized manner, familiarize yourself with the STAR (situation, task, action, result) method.

Example

“In my previous role, I had to optimize a complex ETL process that was taking too long to complete. Based on my analysis, I proposed a series of performance improvements. However, when I implemented them, the process crashed, causing a significant data outage. It was a critical failure, and I worked tirelessly with the team to resolve the issue.

This experience taught me the importance of thorough testing and monitoring during any system optimization. I also learned the value of communicating with the team and stakeholders, informing them of progress and setbacks.”

4. Why do you want to join Visa?

Knowledge of the company will show that you are genuinely interested and prepared for the role.

How to Answer

Your answer should reflect your understanding of Visa’s work, culture, and the opportunities that attract you to the company. Discuss aspects of Visa that align with your personal and professional goals.

Example

“Visa has had an unparalleled impact on global digital payments. I am also deeply inspired by the company’s commitment to financial inclusion. The opportunity to work on VisaNet, one of the most advanced transaction processing networks, is particularly appealing to me. This role aligns with my goal to contribute to financial accessibility everywhere in the world, particularly in emerging markets.”

5. Where do you see yourself in five years?

This will help the interviewer assess your long-term potential within the company.

How to Answer

Provide a realistic career path that aligns with the opportunities at Visa. Focus on how you plan to develop your skills and take on increasing responsibilities, and mention whether you see yourself growing laterally or otherwise in your tenure.

Example

“I envision myself as a data engineering manager at Visa. I plan to achieve this by deepening my expertise in scalable cloud architectures and real-time data processing, both critical to Visa’s operations. I also hope to mentor junior engineers and help shape the next generation of innovators within the company.”

6. Given two sorted lists, write a function to merge them into one sorted list. What’s the time complexity?

This tests your ability to efficiently manipulate datasets. At Visa, you’ll need to consolidate data from different sources into a single, organized dataset for analysis.

How to Answer

Implement a two-pointer technique to iterate through both lists simultaneously, comparing elements and adding the smaller one to a new list until you’ve gone through both lists. This minimizes the time and space needed to achieve a fully merged list.

Example

“I’d initialize two pointers at the start of each list. Comparing the elements at these pointers, I’d then add the smaller of the two to a new list and advance the pointer. This process repeats until all elements from both lists are in the new list. If one list is finished first, I’d append the rest of the other list directly. This method ensures a sorted merge and operates with a time complexity of O(n + m), where n and m are the lengths of the two lists.”

7. We are building a system to detect fraudulent transactions in near real time. How would you build and implement a solution to identify these transactions? What data points would you consider important?

You’ll need to understand complex data systems and apply data engineering principles to solve problems that impact the company’s operations and customer security.

How to Answer

Outline a high-level approach to designing a fraud detection system using data analytics and machine learning techniques. Also, mention tools and technologies you’d employ, such as Apache Kafka for streaming data.

Example

“I’d implement a robust data pipeline that captures and processes transactions as they occur, using Apache Kafka for real-time data streaming. For the detection mechanism, I would employ machine learning models that analyze patterns of normal and fraudulent activities. Key data points would include transaction amount, location, device ID, merchant type, and transaction time. Anomalies detected by the system would be flagged for immediate review. I would also integrate continuous learning into the system so that it adapts to new fraud-detection rules as they develop.”

8. Let’s say we have a table with ID and Name fields. The table holds over 100 million rows, and we want to sample a random row in the table without throttling the database. Write a query to randomly sample a row from this table.

Visa data engineers need to be able to efficiently query and sample from large datasets.

How to Answer 

Briefly mention how full table scans can be detrimental to database performance, especially when dealing with millions of rows. Highlight one or two approaches like OFFSET with random numbers and reservoir sampling (Knuth’s algorithm). Mention their core concepts and suitability for the given scenario. Finally, choose the method you think is most optimal in a Visa context.

Example

“Given its alignment with efficient database querying and its utilization of standard SQL functions, I’d prioritize the OFFSET method. However, if memory constraints are tighter, reservoir sampling could be a valuable alternative.”

9. Given the global nature of Visa’s operations, data latency can be a challenge. Describe a strategy to minimize latency for users accessing the Visa network from different parts of the world.

This question addresses a critical operational challenge for Visa, given its global reach.

How to Answer 

Discuss the use of distributed systems and how the geographical distribution of data centers and cloud services can reduce latency. Mention relevant strategies like content delivery networks (CDNs), edge computing, and global load balancing.

Example

“I would go for a combination of distributed data centers and cloud services strategically located around the world. By implementing a multi-region cloud architecture, data and processing tasks can be handled close to where transactions occur. A content delivery network (CDN) can cache frequently accessed data at various points close to users, further speeding up response times. I would also use global load balancing to distribute user requests based on location, as they would be routed to the nearest server.”

10. We have a table representing a company payroll schema. Due to an ETL error, the employees table did an insert instead of updating the salaries when doing compensation adjustments. The head of HR still needs the salaries. Write a query to get the current salary for each employee.

Troubleshooting and fixing data quality issues will be part of your day-to-day as a data engineer at Visa.

How to Answer

Mention the use of SQL constructs like subqueries, window functions, or GROUP BY clauses. Your explanation should demonstrate your ability to write efficient SQL queries.

Example

“To get the current salary for each employee from the payroll table, I would use ROW_NUMBER() over a partition of the employee ID, ordered by the salary entry date in descending order. This ordering ensures that the most recent entry has a row number of 1. I would then wrap this query in a subquery or a common table expression (CTE) and filter the results to include only rows where the row number is 1. This method ensures that only the latest salary entry for each employee is retrieved, correcting the ETL error that caused multiple inserts.”

11. If tasked with reducing the processing time of Visa’s payment authorization system, what approach would you take to optimize the existing workflows?

This business challenge is central to Visa’s core operations.

How to Answer

Describe a systematic approach to identifying bottlenecks and suggesting specific improvements. Mention what performance metrics you’d select to guide optimizations and more advanced technologies, such as in-memory data processing or asynchronous processing techniques, that could be leveraged.

Example

“I’d first conduct a thorough analysis of the current workflow to identify any inefficiencies. Using tools like New Relic or Datadog, I would monitor and collect performance metrics during peak transaction periods. Based on the insights, I would potentially implement in-memory data processing to speed up data retrieval and computation. Implementing asynchronous processing methods could help decouple components of the system, allowing parts of the workflow to execute independently. This approach enhances the system’s ability to scale during high-demand periods.”

12. Write an SQL query to select the 2nd highest salary in the engineering department.

You will need to demonstrate your understanding of window functions in solving specific data retrieval problems. Such operations are necessary for Visa’s day-to-day coding requirements.

How to Answer

Choose the function that best fits the query’s requirements. In this case, DENSE_RANK or RANK would be appropriate, as they can handle ties in salary values. Explain how you would use the chosen function in a subquery to achieve the desired result.

Example

“I would use the DENSE_RANK function. This function will assign ranks to salaries within the department while handling ties appropriately. I would create a subquery that assigns a rank to each salary using DENSE_RANK, ordered in descending order. Then, in the outer query, I would select the salary where the rank is 2. This approach ensures that if multiple employees share the highest salary, the query will still return the true second highest salary.”

13. Which data structures can be used for implementing LRU cache?

Questioning a prospective data engineer about designing an LRU cache system helps assess their ability to optimize real-time data management.

How to Answer Highlight the key features of an LRU cache, particularly its need for quick access to elements and the ability to track item usage order. Mention suitable data structures that facilitate these features.

Sample Answer: “The most efficient approach is to use a combination of a HashMap and a doubly linked list. The HashMap provides $O(1)$ access time to cache items, which is essential for quick retrievals. The doubly linked list maintains the items in the order of their usage. When the cache reaches its capacity and requires removing the least recently used item, the item at the tail of the list can be removed efficiently. This combination allows for constant time operations for adding, accessing, and removing items. This makes the HashMap and doubly linked list combination ideal in high-load systems where performance and efficiency are paramount.”

14. Write a function that takes an N-dimensional array as input and returns a 1D array.

This tests your algorithmic thinking and proficiency in handling complex data structures. It’s extremely relevant, as engineers often need to process and transform multi-dimensional data into a more manageable form to analyze.

How to Answer

Describe a methodical approach to recursively traverse and flatten the nested lists. Emphasize the importance of considering various levels of nesting and different data types within the lists.

Example

“I’d write a function to check each element of the input array; if the element is a list, the function would recursively flatten it. If the element is not a list (i.e., an integer), it would be added directly to the output array. This method ensures that all nested lists, regardless of their depth, are properly flattened and all integers are collected into a single, one-dimensional array.”

15. Explain how data partitioning works in a distributed computing environment and its impact on query performance.

Visa, with its vast amounts of data, relies on distributed systems for data storage and processing.

How to Answer Explain what data partitioning is and its types. Discuss how partitioning reduces the load on any single server and increases query performance. Mention key concepts like data locality and partition keys. Also, touch on potential challenges like data skewing and how partitioning strategies impact different types of queries.

Example ”Data partitioning is done so that each partition can be stored and processed on different nodes of a distributed system, allowing for parallel processing. There are several types of partitioning, with horizontal partitioning being the most common, where data is split into rows. Vertical partitioning splits data into columns, and functional partitioning involves dividing data based on its function or usage.

The key impact of partitioning on query performance is that it enables more efficient data processing. By distributing the data across multiple nodes, queries can run in parallel, significantly reducing response times, especially for large datasets. This is particularly effective for read-heavy operations. Data locality is another important factor—keeping related data on the same node can reduce the time and resources needed for data retrieval.

However, it’s crucial to choose the right partition key to avoid issues like data skew, where one partition ends up significantly larger than others, leading to bottlenecks. The effectiveness of partitioning also depends on the nature of the queries; some queries may benefit more from certain types of partitioning than others.”

16. Given a table of bank transactions with columns: id, transaction_value, and created_at representing the date and time for each transaction, write a query to get the last transaction for each day.

In a real-world scenario, you might need to extract similar insights from transactional data for daily financial summaries or end-of-day reports.

How to Answer

In your response, you should focus on using a window function to partition the data. Explain the function and how the ORDER BY clause within it helps in determining the latest transaction.

Example

“To write this query, I would use a window function like ROW_NUMBER(), partitioning the data by the date portion of the created_at column and ordering by created_at in descending order within each partition. This setup will assign a row number of 1 to the last transaction of each day. Then, I would wrap this query in a subquery or use a CTE to filter out the rows where the row number is 1. The final output would be ordered by the created_at datetime to display the transactions chronologically. This approach ensures we get the last transaction for each day without missing any days.“

17. What is the Hadoop Distributed File System (HDFS), and how does it differ from a traditional file system?

Understanding HDFS is crucial for working with vast amounts of transactional data across distributed environments.

How to Answer

Your answer should focus on aspects like scalability, fault tolerance, data distribution, and how HDFS manages large datasets.

Example

“Unlike traditional file systems, HDFS spreads data across many nodes, allowing it to handle petabytes of data. HDFS is highly fault-tolerant; it stores multiple copies of data (replicas) on different machines, ensuring that data is not lost if a node fails. It is designed to work with commodity hardware, making it cost-effective for handling massive amounts of data. HDFS is tightly integrated with the MapReduce programming model, allowing for efficient processing.”

18. Say you are tasked with designing a data warehouse for a new online retailer. How would you design the system?

As a data engineer, you will need to be adept at systems design in order to enhance decision-making processes.

How to Answer

Begin by discussing requirements gathering (understanding the type of data, volume, and business needs). Then, move on to the design phase, talking about the choice of a suitable data warehousing model (like star schema or snowflake schema), the importance of scalability, and data security. Also, mention the ETL processes and how you would ensure data quality and integrity.

Example

“I would start by identifying the key business questions the warehouse needs to answer and the types of data required. This includes transactional data, customer data, inventory data, etc. Based on this, I’d choose the star schema for its simplicity and effectiveness in handling typical retail queries. Scalability is critical, so I’d opt for a cloud-based solution like AWS Redshift or Google BigQuery. For ETL processes, I’d ensure that the data extraction is efficient, transformations are accurate, and loading is optimized for performance. I’d emphasize data integrity, security, and compliance with relevant data protection regulations. This approach ensures the data warehouse is robust, scalable, and aligned with business objectives.”

19. What will happen if your Spark job fails at 40% completion?

This question is aimed at assessing your understanding of fault tolerance and error handling.

How to Answer

Discuss the mechanisms of fault tolerance in the context of Spark. Explain how these systems handle partial job failures and the processes for job recovery or restart.

Example

“If a job fails at 40% completion, the system’s fault tolerance mechanisms kick in. Spark uses lineage information of the RDDs to recompute only the lost data partitions. Due to its in-memory processing, Spark can quickly reprocess these partitions rather than restarting the job from scratch. The system will attempt to continue the job from the point of failure, leveraging its DAG (Directed Acyclic Graph) execution model to determine which parts of the data need to be recomputed. This approach ensures efficient recovery with minimal performance impact. I would also address the underlying cause of the failure to prevent future occurrences.”

20. Given two strings, string1, and string2, write a function str_map to determine if there exists a one-to-one correspondence (bijection) between the characters of string1 and string2.

This tests your understanding of one-to-one relationships between elements as you may often perform data synchronization between different systems.

How to Answer

Describe an approach that checks if each character in string1 maps uniquely to a character in string2, and vice versa. Mention the importance of considering the length of the strings and the use of data structures like hash maps for tracking character mappings.

Example

“My function would first check if both strings are of equal length; if not, a bijection isn’t possible. If they are of the same length, I would use two hash maps to track the mappings from string1 to string2 and from string2 to string1. The function iterates through the characters of the strings simultaneously, updating and checking the maps. If, at any point, a character in one string maps to more than one character in the other, the function returns false, indicating no bijection exists. If the iteration completes without such conflicts, the function returns true.”

Here are some tips to help you excel in your interview.

Study the Company and Role

Research recent Visa-related news and updates. Also, learn the company’s values and any challenges it is facing. Understanding the company’s culture and strategic goals prepares you to present yourself better and know if they are a good fit for you.

Once you understand more about the company, try to learn how the team you are applying to supports its goals.

Brush Up on Technical Skills

Be sure you have a strong foundation in Python, SQL, and data structures. Familiarity with cloud computing platforms is also a plus. However, keep in mind that the main objective of the interview is to assess your grasp of fundamental concepts and first principles thinking. So, your goal should be to study data structures, tools, algorithms, etc., in the context of real-world problems.

Check out the resources we’ve tailored for data engineers: a case study guide, a compendium of data engineer interview questions, data engineering projects to add to your resume, and a list of great books to help you on your engineering journey. If you need further guidance, consider our tailored data engineering learning path.

Prepare Behavioral Interview Answers

Soft skills like collaboration, effective communication, and problem-solving are paramount to succeeding in any job, especially in a collaborative environment like Visa’s.

To test your current preparedness for the interview process, try a mock interview to improve your communication skills.

Keep Up with the Industry

The data engineering landscape is constantly evolving, so keep yourself updated on the latest technologies, news, and best practices.

Network with Employees

Connect with Visa employees through LinkedIn or other online platforms. They can provide valuable insights into the company culture and the interview process.

Check out our complete Data Engineer Prep Guide to ensure you don’t miss anything important while preparing for your interview at Visa.

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

The average base salary for a data engineer at Visa is US$125,557, making the remuneration well above the average base salary for general data engineer roles in the US, which is US$107,307.

What other companies can I apply for besides the Visa data engineer role?

You can apply to similar roles at Mastercard, PayPal, or Stripe.

For insights on other tech jobs, you can read more on our Company Interview Guides page.

Are there job postings for Visa data engineer roles on Interview Query?

We have several Visa data engineer jobs listed, which you can apply for directly through our job portal. You can also look at similar roles relevant to your career goals and skill set.

Succeeding in a Visa data engineer interview requires a strong foundation in technical skills and problem-solving, as well as a demonstration of leadership and effective communication.

For other data-related roles at Visa, consider exploring our business analyst, data analyst, data scientist, and various guides in our main Visa interview guide.

For more information about interview questions for data engineers, peruse our main data engineering interview guide and case studies, as well as our Python and SQL sections.

Check out more of Interview Query’s content, and we hope you land your dream role at Visa soon!