Walmart Data Engineer Interview Questions + Guide in 2024

Walmart Data Engineer Interview Questions + Guide in 2024Walmart Data Engineer Interview Questions + Guide in 2024


Walmart is one of the world’s largest chains of discount department stores, and continues to build its online sales platform to complement that huge physical footprint. This means that data engineers are more in demand than ever to help them build pipelines, maintain databases, and collaborate with analysts to provide clean data and deploy models.

This interview guide provides a detailed overview of the Walmart Data Engineer interview process.

It is designed for applicants such as yourself who are interested in pursuing a Data Engineering role at Walmart. It includes a variety of commonly asked interview questions, as well as practical tips to enhance your chances of securing the position.

What Is the Interview Process Like for a Data Engineer Role at Walmart?

As a Data Engineer at Walmart, you will be tasked with not only maintaining extensive data systems but also suggesting process improvements and working with cross-functional teams. The interview process will thus evaluate your technical abilities, problem-solving, and communication.

The interview process typically spans around 2-3 weeks.

Step 1: Preliminary Screening With A Recruiter

In this first step, a recruiter assesses your background, experience, and fit for the role. Be prepared to discuss your resume, highlighting projects and accomplishments relevant to data engineering. Make sure to use this opportunity to ask the recruiter questions to understand the role, and prepare some key points to sell your role-specific skills.

Step 2: Technical Interviews

After the initial screening, you’ll have a technical phone interview with a member of your prospective team. This interview will test your knowledge in areas such as relational databases, SQL, and big data tools.

Step 3: Onsite Interviews

The onsite phase may involve multiple rounds. These include:

  • Technical Interviews: Focusing on specific skills like ETL processes, database optimization, and big data technologies. For more senior roles, there might be a round focusing on advanced topics in data engineering, such as data architecture, big data solutions, or specific technologies used at Walmart.
  • Case Study or Practical Assessment: You might be given a real-world problem to solve or asked to design a data system, highlighting your approach to data management and problem-solving skills.
  • Behavioral Interview: This round assesses your soft skills, teamwork, and cultural fit within Walmart. Also be prepared to discuss how your values align with Walmart’s corporate culture.

What Questions Are Asked in a Walmart Data Engineer Interview?

In Walmart’s Data Engineer interviews, candidates are tested on their SQL expertise, algorithmic problem-solving, and systems design skills, crucial for tasks like data analysis, warehouse design, and real-time data processing. Questions focus on practical scenarios like SQL query optimization, efficient data structure implementation, and handling sensor data for customer analytics.

We’ll discuss these interview questions in more depth below:

1. Given two tables, a users table (with demographic information) and a neighborhoods table, write a query that returns all neighborhoods that have zero users.

As a Data Engineer at Walmart, you may need to execute similar SQL functions for optimizing resource allocation and targeting marketing efforts.

How to Answer

Explain the logic of joining the two tables (users and neighborhoods) in such a way that you can count the number of users in each neighborhood and then filter out those neighborhoods with zero users.


“I would perform a LEFT JOIN between the users table and the neighborhoods table on the neighborhood identifier. Then, I would use a GROUP BY clause on the neighborhood identifier and a COUNT function on the users’ IDs. The key is to use a HAVING clause to filter out the neighborhoods where the count of users is zero. This approach ensures we consider all neighborhoods, even those without any associated users, and only return those with no users.”

2. 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, a Data Engineer at Walmart 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.


“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.“

3. Given a list of sorted integer lists, write a function to create a combined list while maintaining sorted order without importing any libraries or using sort functions in Python.

To streamline logistics at Walmart, data engineers need to understand concepts like merging sorted integer lists without resorting to Python’s built-in functions.

How to Answer

Go through a step-by-step approach to merge the lists while maintaining sorted order. Focus on how you iterate through each of the lists to compare elements, choose the smallest element at each step, and add it to the final merged list.


“In my solution, I’ll maintain an array of pointers to track the current element in each list. At each step, I’d look for the smallest element in each list and add it to the merged list. This way, we effectively merge all lists while maintaining their sorted order. This approach is efficient because it can minimize comparisons and eliminate the need for any external sorting library. Each iteration involves only a comparison of the heads of the remaining lists.”

4. 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, optimize inventory management, and provide insights for strategic planning at Walmart.

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.


“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.”

5. How would you migrate a large dataset from a traditional RDBMS to a cloud-based data warehouse like Google BigQuery or AWS Redshift?

This type of experience as a data engineer would be extremely valuable as companies like Walmart would have ongoing projects where they are shifting from legacy systems to cloud-based ones.

How to Answer

Discuss the assessment phase, where you evaluate the data, its schema, and dependencies. Then, talk about choosing the right tools for migration and planning for data transformation if necessary. Emphasize the importance of minimizing downtime. Finally, discuss testing the migrated data in the new environment and the transition process. Highlight specific tools or services provided by cloud platforms that you would utilize.


“I’d start with a thorough assessment of the current database. I’d select appropriate tools for the migration, like AWS Database Migration Service or Google’s BigQuery Data Transfer Service, depending on the target platform. It’s crucial to plan for any data transformation that might be needed to fit the new schema or data model. I’d ensure a backup is in place to prevent data loss and then proceed with the migration, potentially in stages to minimize downtime. I’d conduct extensive testing after migration to ensure data integrity. Finally, I’d plan a cutover strategy to switch to the new system, ensuring minimal impact on ongoing operations.”

6. Imagine you have sensor data coming from various stores, tracking customer movements. How would you process and store this data to analyze foot traffic patterns?

Real-time, high-volume sensor data is increasingly important for retailers like Walmart to understand customer behavior and optimize store layouts. As a data engineer, you may expect to tackle similar problems.

How to Answer

Focus on the end-to-end process of handling sensor data. Briefly discuss the storage, emphasizing the need for scalable and efficient storage solutions. Talk about how you would leverage this data to derive insights.


“I would first set up a real-time data ingestion pipeline using a tool like Apache Kafka, which can handle high-throughput data streams efficiently. Considering the volume and nature of the data, a time-series database or a partitioned data lake in the cloud would be appropriate, as they are optimized for time-based data and can scale. The next step would be to clean and preprocess the data, removing any anomalies or irrelevant information. For analysis, I would use a distributed computing framework like Apache Spark, which is well-suited for large-scale data processing. The resulting insights can be used to optimize store layouts, improve customer experience, and drive sales strategies.”

7. Explain how you would use Apache Kafka for handling real-time data streams in a retail context.

In the context of Walmart, Apache Kafka is a popular tool for data engineers to handle real-time data streams for tasks such as monitoring transactions, updating inventory in real-time, and analyzing customer behaviors.

How to Answer

Explain Kafka’s role and its various benefits. Highlight how it is a useful tool in the context of real-time data streams in a retail scenario.


“Kafka serves as a centralized platform for data ingestion, enabling the collection of large volumes of data with high throughput and low latency. For instance, transaction data can be streamed in real-time through Kafka, allowing for immediate analysis of sales trends or inventory levels.

Kafka’s topic-based publish-subscribe model also allows for scalable and flexible data distribution to different downstream systems or applications for further processing. This can be integrated with a processing framework like Apache Spark for real-time analytics, enabling the user to gain instant insights into customer behavior, operational efficiency, or inventory management.”

8. Which Data Structures can be used for implementing LRU cache?

At Walmart, questioning a prospective data engineer about designing an LRU cache system is important to assess their ability to optimize real-time data management. Given the dynamic nature of order updates and customer interactions, a scalable LRU cache is vital for enhancing the responsiveness of Walmart’s platform.

How to Answer

Your answer should highlight the key features of an LRU cache — particularly its need for quick access to elements and the ability to track the usage order of items. 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.”

9. You are given the root of a binary tree. You need to determine if it is a valid binary search tree (BST).

This task is important in scenarios where binary trees represent relationships within datasets, such as product hierarchies or organizational structures, in the context of Walmart.

How to Answer

Discuss the properties of a valid BST and explain your approach to traversing the tree. You should talk about the use of recursion and the importance of considering edge cases.


“I would use a recursive approach to solve this problem. In a BST, a key property is that the left subtree of a node contains nodes with keys lesser than the node’s key, and the right subtree only nodes with keys greater. I would implement a function that traverses the tree in-order and checks if the value of each node is greater than the previously visited node. It’s also important to handle edge cases, like ensuring the approach works for trees with all nodes on one side and considering how to handle duplicate values.”

10. Given two strings A and B, write a function to return whether A can be shifted some number of places to get B.

String manipulation is a concept that you will need a thorough grasp of, to work in Walmart as a data engineer. This question tests your expertise for tasks where string representations are used to identify data, ensuring that any shifts in data representation are accurately detected.

How to Answer

Explain your logic clearly, and remember to mention handling edge cases like empty strings or strings of different lengths.


“I would first check if A and B are of equal length, as that is the only scenario where this shift is feasible. Then, I would concatenate A with itself, forming a new string A+A. The logic is that if B is a shifted version of A, then B must be a substring of A+A. For example, if A is ‘abcd’ and B is ‘cdab’, concatenating A with itself gives ‘abcdabcd’, and you can see that B is a substring of this.”

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

Troubleshooting inconsistencies during ETL jobs ought to be part of your day-to-day as a data engineer, e.g. for ensuring accurate compensation data at Walmart.

How to Answer

Discuss the approach to identify the latest salary entry for each employee. Explain the importance of filtering out only the latest entries for each employee.


“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.”

12. Let’s say you’re setting up the analytics tracking for a web app. How would you create a schema to represent client click data on the web?

Client clicks data is important to understand for enhancing user experience, marketing strategies, and website optimization at Walmart. The interviewer is looking to assess your expertise as a data engineer to understand what data is important to capture, how to organize it for efficient analysis, and your approach to data modeling.

How to Answer

Follow this approach to answer such questions: 1. Ask clarifying questions. 2. Assess requirements. 3. Present your solution. 4. Create a validation plan to assess the solution and iterate it for continuous improvement.


“In creating such a schema, I would capture essential attributes such as the user ID (to uniquely identify users), session ID (to track individual user sessions), timestamp (to record the exact time of the click), page URL (to identify which page was clicked), and click details (like the element clicked and the type of click). Capturing metadata such as the user’s device type, browser, and geographic location can provide valuable insights as well.

The schema should be designed for efficient querying, so I would normalize the data. For high query performance and scalability, especially with large data, a NoSQL database like MongoDB might be more suitable than a traditional SQL database. This allows for more flexibility in handling semi-structured data and can easily scale with the growth of the web app’s user base.

In terms of data storage, I would consider using a time-series database or a columnar storage format if the primary analysis involves time-based aggregations or rapid querying of specific columns.”

13. Walmart needs to optimize the layout of items on shelves in its stores. You are given an array representing the popularity score of different products (a higher score means it’s more popular). Write an algorithm to arrange these products on a single shelf.

As a Data Engineer at Walmart, optimizing the layout of items on store shelves is essential for improving customer experiences and maximizing sales.

How to Answer

Explain that the key to solving this problem is to first sort the products by their popularity score. It’s important to mention handling edge cases like an even number of products and how to deal with products with the same popularity score.


“I’d start by sorting the array of product popularity scores in descending order. Then, I would place the most popular product (the first in the sorted array) in the center of the shelf. Next, I’d take the subsequent products and alternate their placement left and right of the center product. This method would ensure that the popularity decreases as we move away from the center. For example, if the sorted popularity scores are [5, 4, 3, 2, 1], the arrangement on the shelf would be [2, 4, 5, 3, 1]. In case of an even number of products, I would place one of the two most popular products in the center, ensuring a balanced decrease in popularity on both sides. The time complexity of this algorithm is O(n log n) due to the sorting step, where n is the number of products.”

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

Walmart, with its vast amount of data, relies on distributed systems for data storage and processing. Understanding how data partitioning impacts query performance is crucial to a data engineer for optimizing data retrieval and processing times.

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. It’s also important to touch on potential challenges like data skewing and how partitioning strategies impact different types of queries.


”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.”

15. How would you deploy a containerized data processing application using Kubernetes, and what factors would you consider for scaling?

The question tests your practical knowledge in deploying applications in a containerized environment, as well as your foresight as a data engineer in planning for scalability, which is key for handling fluctuating data processing demands in a company as big as Walmart.

How to Answer

Discuss the steps for deploying a containerized application. Highlight scalability considerations, such as setting appropriate resource limits and ensuring high availability through multi-node setup or multi-zone/multi-region deployment. Mention the importance of monitoring and logging for performance tuning and capacity planning.


”I would first package the application into a Docker container. This involves writing a Dockerfile that defines the application environment and dependencies. Then I’d create Kubernetes manifests, including a Deployment for managing the application pods and a Service for internal or external communication. If external access is needed, an Ingress resource might also be defined.

For scaling considerations, I would configure the Deployment with resource requests and limits to ensure the application has enough resources to run efficiently, but doesn’t over-consume cluster resources. I’d also use Kubernetes’ Horizontal Pod Autoscaler (HPA) to automatically scale the number of pods based on CPU or memory usage metrics.

Setting up robust monitoring and logging is vital for observing the application’s performance and for making informed decisions about scaling and resource allocation. Tools like Prometheus for monitoring and ELK Stack (Elasticsearch, Logstash, Kibana) for logging can be integrated into the Kubernetes environment.”

16. How would you handle data loss during a migration?

As a data engineer, you need to be able to handle unprecedented situations and mitigate risks associated with data migration. Data loss can have a significant impact on companies like Walmart, so the interviewer wants to test your ability to implement robust data management practices.

How to Answer

You should talk about focusing on preventive measures, immediate response strategies, and long-term solutions. Emphasize the importance of comprehensive planning and backup strategies before migration. Then, discuss the steps you would take to identify and assess the extent of data loss if it occurs. Finally, mention how you would restore the lost data from backups and implement measures to prevent future occurrences.


”I would initially focus on preventive measures, such as ensuring thorough backups are made before starting the migration. This involves creating a complete and tested backup, ideally in a format that is easy to restore.

If data loss is detected during migration, the first step is to pause the migration process to prevent further loss. I would then assess to understand the scope of the loss, using data reconciliation processes and integrity checks against the backup. Once the affected data is identified, I will restore it from the backups.

After addressing the immediate issue, I would analyze the cause of the data loss to prevent future occurrences. This might involve adjusting the migration strategy, improving data validation methods during the transfer, or enhancing the robustness of the data backup and recovery systems. Continuous monitoring and validation are key in subsequent migrations to ensure data integrity throughout the process.”

17. Walmart wants to optimize the layout of a new supercenter for better customer navigation. The store layout is represented as a grid, where each cell is a department or aisle. Some cells are blocked and cannot be traversed. Write an algorithm to find the shortest path from the entrance to a particular department.

This tests your understanding - as a data engineer - of graph traversal algorithms. This type of problem is analogous to Walmart’s challenges in logistics and warehouse management.

How to Answer

You should explain your choice of algorithm and the reasons behind your choice. Detail the steps of the chosen algorithm, describing how it accounts for different weights of traversing each cell.


“The time complexity of Dijkstra’s algorithm is O(V^2) for a store with V vertices (or cells) when using a simple min-priority queue, but it can be optimized to O(V + E log V) with a Fibonacci heap, where E is the number of edges. This approach, unlike BFS, takes into account the different ‘costs’ associated with moving through various parts of the store ((considering factors like aisle congestion or obstructions), resulting in a more practical path for navigation within a complex environment like a supercenter.

The entrance is represented as the initial node. I’d use a priority queue to continually visit the node with the smallest known distance from the start that hasn’t been visited yet. For each neighboring node, I would calculate the distance from the start node. If this calculated distance is less than the currently known distance, I’d update it with the smaller value. This process should be continued until all nodes have been visited.”

18. Can you describe a situation where you had to collaborate with a difficult team member?

The interviewer needs to understand how you handle conflicts in a team setting, as data engineering often requires close collaboration with various teams in Walmart.

How to Answer

Use the STAR method of storytelling - discuss the Specific situation you were challenged with, the Task you decided on, the Action you took, and the Result of your efforts. Make sure to quantify impact when possible.


“In a past project, I encountered a challenge working with a team member who tended to make unilateral decisions and had difficulty effectively communicating their thought process.

Realizing this was affecting our productivity and team dynamics, I requested a private meeting with this colleague. I aimed to understand their perspective while expressing the team’s concerns in a constructive way. During our conversation, I learned that their approach stemmed from a deep sense of responsibility and a fear of project failure. I acknowledged their commitment and then elaborated on how collaborative decision-making could enhance project outcomes.

We agreed on a more collaborative approach, with regular briefings where updates were clearly outlined. This experience taught me the value of addressing interpersonal challenges head-on, but with empathy. The situation improved significantly post our discussion – decisions were more balanced, and there was a noticeable increase in team cohesion and morale.”

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

For a company like Walmart, which operates in a competitive and dynamic retail environment, data engineers who are 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.


“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.”

20. Tell us about a time when you encountered an unexpected problem in a data engineering project. How did you identify the issue, and what steps did you take to resolve it?

Data engineering often involves complex and unpredictable issues, and Walmart would be interested in how you handle such situations.

How to Answer

Emphasize your analytical process, attention to detail, and collaborative efforts to solve the problem. It’s important to show that you can remain calm, collaborative, and efficient under pressure.


“In a previous project, I was responsible for managing a data migration from an on-premises database to a cloud-based solution. Midway through the process, we encountered significant and unexpected delays in data transfer, which risked derailing our migration timeline.

The first step I took was to analyze the migration logs to identify anomalies. I discovered that the bottleneck was occurring during peak business hours, which led me to hypothesize that network congestion was the issue. To test this, I scheduled a smaller-scale data transfer during off-peak hours, which validated my hypothesis as the transfer was completed much faster. I worked with the IT infrastructure team to devise a strategy. We decided to implement network throttling to prioritize migration traffic and rescheduled the bulk of the data transfer to off-peak hours.”

How to Prepare for a Data Engineer Role at Walmart

Use the tips below to help you ace your data engineering interview at Walmart.

Understand the Role and Responsibilities

Research the specific role you’re applying to. Understand the key responsibilities and skills required.

Explore the specific role at Walmart through our Data Engineering *Learning Path to gain a comprehensive understanding of how your skills align with the requirements of this position.*

Brush Up on Technical Skills

Gain proficiency in SQL, Python, Java, or Scala. Understand database concepts, ETL processes, and data warehousing principles. Understand data modeling concepts and warehousing architectures (such as the star schema, snowflake schema, etc.) for efficient data storage and retrieval.

Walmart deals with large datasets, so understanding distributed computing is crucial. Learn about big data technologies like Hadoop, Spark, and Kafka. Get comfortable using cloud services as Walmart leverages cloud computing extensively.

A good way to boost your confidence is to work on projects that mimic real-world data engineering challenges. Check our article on our handpicked data engineering projects.

Familiarize Yourself with Walmart’s Business Model

Develop a solid understanding of the retail industry, including supply chain management, inventory management, customer analytics, and e-commerce trends. Research Walmart’s business model, focusing on how they use data to drive decisions, optimize operations, and enhance customer experience. Understanding the company’s culture and goals will allow you to align your responses with what Walmart is looking for in an employee.

Tailor Your Resume

Highlight work that is related to the position you are applying to. Look at your CV from the point of view of a prospective interviewer, and edit it accordingly. This will demonstrate early on that you are a good fit.

Practice Behavioral Questions

Prepare for behavioral questions using the STAR method. Reflect on your past experiences and practice articulating them in a concise, impactful manner.

You can familiarize yourself with behavioral questions by visiting our Interview Questions sectionIt offers a wide range of practice questions to help you structure your responses effectively using the STAR method.

Mock Interviews

Engage in mock interviews to simulate the real interview experience. Seek feedback to improve your responses, technical knowledge, and problem-solving approach.

Participate in Mock Interviews to practice and showcase your teamwork and communication skills.

Networking and Insights

If possible, connect with current Walmart Data Engineers or employees. Gain insights into the company culture and work environment. Follow Walmart on platforms like LinkedIn for updates on their latest projects and initiatives.

Prepare Questions for the Interviewer

Prepare thoughtful questions to ask your interviewers about Walmart’s work culture, challenges, and expectations. This shows your interest and eagerness to engage with the company’s ethos and future goals.


What is the average salary for a Data Engineer Role at Walmart?


Average Base Salary


Average Total Compensation

Min: $80K
Max: $158K
Base Salary
Median: $100K
Mean (Average): $109K
Data points: 308
Min: $24K
Max: $347K
Total Compensation
Median: $204K
Mean (Average): $191K
Data points: 10

View the full Data Engineer at Walmart Global Tech salary guide

The average base salary for a Data Engineer at Walmart is US$108,790, while the estimated average total compensation is US$190,971, making the compensation attractive for qualified applicants.

For a broader understanding of Data Engineers’ salaries, be sure to check out our comprehensive Data Engineer Salary Guide that spans various industries, companies, and positions.

Where can I read more discussion posts on Walmart’s Interview Process here in Interview Query?

Unfortunately, Interview Query does not currently provide any discussion posts relating to Walmart’s Data Engineering role. However, you can check our discussion board for information related to data engineering roles in general. Please feel free to check it out at your convenience.

Are there job postings for Walmart Data Engineer roles on Interview Query?

Yes, there are job posts regarding Walmart’s data engineering position here on Interview Query. If you are interested in more details, you can explore our listings for Walmart Data Engineering positions.


In conclusion, preparing for a Walmart Data Engineer interview is a comprehensive process that combines honing technical skills, understanding the specific needs and challenges of the retail and e-commerce industry, and aligning with Walmart’s unique business model.

If you need more information about this, check out our main Walmart Interview Guide. We’ve provided in-depth information there, covering various positions at Walmart, including Data Analyst, Data Scientist, Machine Learning, Software Engineer, and other roles.

Also, explore our other articles for a comprehensive list of data engineering interview questions, case studies, and Python questions.

If you need more in-depth preparation, you can practice from our take-home assignments. For insights on other roles, we have free interview guides for every major tech company.

As a bonus tip, you can check out Walmart’s handy guide on their careers page on acing the interview.

Your preparation should encompass not only core data engineering concepts but also a deep dive into Walmart’s approach to using data in driving business decisions.

We hope that you land your dream role very soon!