PepsiCo Data Engineer Interview Questions + Guide in 2025

PepsiCo is a global food and beverage industry leader with renowned brands like Pepsi, Lay’s, and Gatorade. With a presence in over 200 countries, PepsiCo innovates and leads in consumer preferences.

The Data Engineer position at PepsiCo is a dynamic role that involves developing and maintaining high-quality data collection processes. You’ll be part of the Enterprise Data Operations team, leveraging big data and digital technologies to enable advanced analytics, business insights, and new product development. Expect to work on building data pipelines, maintaining data integrity, and supporting various business units in a hybrid cloud and on-premise environment. This role requires strong Python, SQL, PySpark, and Azure skills, focusing on driving data-driven decision-making across the organization.

Prepare to dive deep into PepsiCo’s transformative digital initiatives with this comprehensive guide on Interview Query! This covers the interview process and commonly asked PepsiCo data engineer interview questions to help you prepare and excel.

The interview process usually depends on the role and seniority; however, you can expect the following in a PepsiCo data engineer interview:

Recruiter/Hiring Manager Call Screening

If your application is shortlisted, a recruiter from Pepsico’s Talent Acquisition Team will reach out to verify your work experience, technical skills, and interest in the position. This screening may include behavioral questions to understand your fit within the company and technical questions to gauge your expertise.

Sometimes, the hiring manager may join the call to provide more insights into the role and answer your questions. This initial call generally lasts about 30 minutes.

Technical Virtual Interview

You will be invited to a technical virtual interview after passing the recruiter screening. For Pepsico Data Engineer positions, this interview generally lasts around 1 hour and includes video conferencing and screen sharing to solve coding problems.

Common technical questions revolve around:

• ETL Pipelines
• SQL Queries
• Database Management
• Python Coding
• PySpark functions

You may also be required to work on live coding problems and discuss various data architectures and database solutions you have previously worked on.

Onsite Interview Rounds

If you succeed in the virtual technical screening, you’ll be invited to an onsite interview (or a series of virtual interviews, depending on the company’s current operations). These interviews may comprise multiple rounds where you will be tested on your technical skills, problem-solving abilities, and domain knowledge.

These rounds may include:

• Deep Dive Technical Interviews: Questions on SQL optimization, ETL pipeline design, data warehousing, cloud platforms, and programming languages like Python and PySpark.
• System Design Interviews: Architecture discussions, data infrastructure frameworks, and your experience with large-scale data applications.
• Behavioral Interviews: Assessing your fit with Pepsico’s mission and culture, your ability to work in a team, and stakeholder management.

Typically, interviews at PepsiCo vary by role and team, but commonly, Data Engineer interviews follow a fairly standardized process across these question topics.

1. Select the top 3 departments with at least ten employees and rank them by the percentage of employees making over 100K.

Given the tables for employees and departments, select the top 3 departments with at least ten employees and rank them according to the percentage of their employees who make over 100K in salary.

2. What are type I and type II errors in hypothesis testing?

In hypothesis testing, type I errors (false positives) occur when you reject a true null hypothesis. In contrast, type II errors (false negatives) occur when you fail to reject a false null hypothesis.

Bonus: Describe the probability of making each type of error mathematically.

3. How would you debug and improve the efficiency of a slow SQL query?

If a SQL query takes too long, determine if the execution time exceeds acceptable limits. Then, debug the issue by analyzing query execution plans, checking for indexing issues, and optimizing the query structure.

4. What are type I and type II errors in hypothesis testing, and how do they differ?

In hypothesis testing, type I errors (false positives) occur when a true null hypothesis is incorrectly rejected, while type II errors (false negatives) happen when a false null hypothesis is not rejected.

Bonus: Describe the probability of making each type of error mathematically.

5. How does random forest generate the forest and why use it over logistic regression?

Explain the process of how random forest generates multiple decision trees and discuss the advantages of using random forest over logistic regression.

6. Does increasing the number of trees in a random forest always improve accuracy?

If you sequentially increase the number of trees in a random forest model, will the model’s accuracy continue to improve indefinitely?

7. What are the differences between XGBoost and random forest, and when would you use each?

Compare the XGBoost and random forest algorithms, highlighting their differences. Provide an example scenario where one would be preferred over the other.

A few tips for navigating your Pepsico Data Engineer interviews include:

• Know Pepsico’s Data Operations: Familiarize yourself with Pepsico’s enterprise data operations and how they leverage data for business insights and product development.
• Brush Up On Technical Skills: Be prepared to solve problems related to ETL, SQL, database management, and analytics. Hands-on practice will significantly improve your confidence.
• Understand Cloud Platforms: Since cloud experience, particularly with Azure, is highly valued, ensure you are comfortable discussing cloud data engineering solutions and best practices.

Need more detailed guidance or practice questions? Check out Interview Query for an extensive range of interview preparation resources. Sign up using the link below and start your journey toward completing your Pepsico Data Engineer interview.

What is the average salary for a Data Engineer at Pepsico?

What kind of technical skills are essential for a Data Engineer position at PepsiCo?

To excel as a Data Engineer at PepsiCo, you’ll need expertise in SQL, Python, PySpark, Scala, and cloud platforms like Azure. Experience with data modeling and ETL/ELT pipelines and familiarity with technologies like Databricks and Kubernetes are crucial. An understanding of metadata management and data lineage and proficiency with version control systems like GitHub will also be beneficial.

What will my responsibilities be as a Data Engineer at PepsiCo?

You’ll be responsible for developing and managing data pipelines, maintaining data quality, building automation and monitoring frameworks, and collaborating with data science and product teams. By leveraging PepsiCo’s enterprise data foundations, you will play a pivotal role in enabling business insights, advanced analytics, and product development.

Why should I consider a Data Engineer role at PepsiCo?

PepsiCo operates at the forefront of digital transformation, leveraging big data to drive business innovation. As a Data Engineer, you’ll work with cutting-edge technologies in a dynamic, high-growth environment. You’ll contribute to meaningful projects that impact areas such as eCommerce, mobile experiences, and IoT while also enjoying a culture of innovation and collaboration.

If you want more insights about the company, check out our main PepsiCo Interview Guide, where we have covered many interview questions that could be asked. We’ve also created interview guides for other roles, such as software engineer and data analyst, where you can learn more about PepsiCo’s interview process for different positions.

You can also check out all our company interview guides for better preparation, and if you have any questions, don’t hesitate to reach out to us.

Good luck with your interview!