The Hershey Company Data Engineer Interview Questions + Guide in 2025

The Hershey Company is a global leader in the confectionery industry, dedicated to creating sweet moments of Hershey happiness for everyone, every day.

As a Data Engineer, you'll play a pivotal role within the Enterprise Data organization, which drives value by providing high-quality, governed data to support analytics and decision-making across the organization. Your key responsibilities will include developing and delivering robust data pipelines, optimizing existing data solutions, and collaborating with cross-functional teams, including data scientists and project managers, to ensure engineering standards meet Hershey's best practices. A successful Data Engineer at Hershey combines strong technical skills with an understanding of business needs, ensuring that data solutions align with strategic objectives. The role requires proficiency in SQL and experience with cloud-based data solutions, as well as a demonstrated ability to lead projects and engage effectively with stakeholders.

This guide will help you prepare for the interview by highlighting essential skills, competencies, and the company culture, ensuring you can showcase your strengths and align your experiences with Hershey's values.

The interview process for a Data Engineer at The Hershey Company is structured to assess both technical skills and cultural fit within the organization. It typically consists of several stages designed to evaluate your experience, problem-solving abilities, and alignment with the company's values.

1. Initial Screening

The process begins with an initial screening, which may take place over the phone or via a video call. During this stage, a recruiter will review your resume and qualifications, discussing your background and interest in the role. This conversation often includes questions about your experience with data engineering, your familiarity with relevant tools and technologies, and your understanding of the data landscape at Hershey.

2. Technical Interview

Following the initial screening, candidates typically participate in a technical interview. This may involve a combination of coding challenges and discussions about data engineering concepts. Expect to demonstrate your proficiency in SQL, Python, and other relevant programming languages, as well as your ability to design and implement data pipelines. You may also be asked to solve problems related to data optimization and performance tuning, reflecting the responsibilities outlined in the job description.

3. Behavioral Interview

The next step usually involves a behavioral interview, where you will meet with the hiring manager and possibly other team members. This interview focuses on your past experiences and how they relate to the role. You may be asked to provide examples of how you've handled challenges in previous positions, your approach to teamwork, and how you align with Hershey's core values. The SOAR (Situation, Obstacle, Action, Result) or STAR (Situation, Task, Action, Result) techniques may be used to frame your responses.

4. Panel Interview

In some cases, candidates may be invited to a panel interview, which includes multiple interviewers from different departments. This format allows the team to assess your fit across various functions and gauge your ability to collaborate with cross-functional teams. Questions may cover a range of topics, including your technical expertise, project management skills, and your ability to communicate complex ideas to both technical and non-technical stakeholders.

5. Final Interview

The final stage of the interview process may involve a more in-depth discussion with senior leadership or key stakeholders. This interview is an opportunity for you to ask questions about the company's vision, culture, and future projects. It also serves as a chance for the interviewers to evaluate your long-term potential within the organization.

As you prepare for your interviews, consider the specific skills and experiences that align with the responsibilities of a Data Engineer at Hershey, particularly in areas such as data pipeline development, cloud solutions, and collaboration with data science teams.

Next, let's explore the types of questions you might encounter during the interview process.

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

Understand the Interview Process

The interview process at Hershey can be extensive, often involving multiple rounds including HR screenings, technical interviews, and meetings with various stakeholders. Be prepared for a mix of behavioral and technical questions, and understand that the company values a good cultural fit. Familiarize yourself with the SOAR (Situation, Obstacle, Action, Result) method, as many interviewers may use this framework to assess your problem-solving abilities.

Showcase Your Technical Skills

As a Data Engineer, proficiency in SQL and Python is crucial. Brush up on your SQL skills, focusing on complex queries, data manipulation, and performance optimization. Additionally, be prepared to discuss your experience with data pipeline development and any relevant tools you’ve used, such as Informatica or Talend. Highlight any projects where you’ve successfully implemented data solutions, emphasizing your role in the process.

Emphasize Collaboration and Communication

Hershey places a strong emphasis on teamwork and collaboration. Be ready to discuss your experience working in cross-functional teams, particularly with data scientists, business analysts, and project managers. Prepare examples that demonstrate your ability to communicate complex technical concepts to non-technical stakeholders, as this will be key in your role.

Prepare for Behavioral Questions

Expect behavioral questions that assess your alignment with Hershey's core values. Reflect on past experiences where you demonstrated leadership, problem-solving, and adaptability. Use the STAR method to structure your responses, ensuring you clearly outline the situation, your actions, and the results achieved.

Be Ready for a Cultural Fit Assessment

Hershey's interview process often includes a "vibe check" to assess cultural fit. Be authentic and personable during your interactions. Show enthusiasm for the company and its values, and be prepared to discuss why you want to work for Hershey specifically. This is your chance to convey your passion for the brand and how you align with its mission.

Follow Up and Stay Engaged

After your interviews, consider sending a thank-you email to express your appreciation for the opportunity and reiterate your interest in the role. This not only demonstrates professionalism but also keeps you on the interviewers' radar. Given the feedback about delayed responses, staying engaged can help you stand out.

Stay Positive and Resilient

The interview process can be lengthy and may involve some frustrations, such as delayed responses or lack of communication. Maintain a positive attitude throughout the process, and be prepared to follow up if you haven’t heard back in a reasonable timeframe. Resilience is key, and showing that you can handle uncertainty with grace will reflect well on you.

By following these tips, you can position yourself as a strong candidate for the Data Engineer role at Hershey. Good luck!

In this section, we’ll review the various interview questions that might be asked during a Data Engineer interview at The Hershey Company. The interview process will likely focus on your technical skills, problem-solving abilities, and how well you can collaborate with cross-functional teams. Be prepared to discuss your experience with data pipelines, cloud solutions, and your approach to data governance and security.

Technical Skills

1. Can you describe your experience with building data pipelines?

This question aims to assess your hands-on experience in data engineering and your familiarity with the tools and technologies used in the process.

How to Answer

Discuss specific projects where you designed and implemented data pipelines, highlighting the technologies you used and the challenges you faced.

Example

“In my previous role, I built a data pipeline using Apache Spark and AWS Glue to process large datasets from various sources. I faced challenges with data quality, which I addressed by implementing validation checks at each stage of the pipeline, ensuring that only clean data was ingested into our data warehouse.”

2. What tools have you used for data integration and ETL processes?

This question evaluates your familiarity with industry-standard tools and your ability to leverage them effectively.

How to Answer

Mention specific tools you have experience with, and provide examples of how you used them in your projects.

Example

“I have extensive experience with Informatica and Talend for ETL processes. In my last project, I used Informatica to automate data extraction from multiple sources, transforming it into a format suitable for analysis, which significantly reduced the time needed for reporting.”

3. How do you ensure data quality and integrity in your projects?

This question assesses your understanding of data governance and quality assurance practices.

How to Answer

Explain the methods you use to maintain data quality, including validation, testing, and monitoring techniques.

Example

“I implement data validation rules at the point of entry and conduct regular audits to ensure data integrity. Additionally, I use automated testing frameworks to catch any discrepancies early in the data pipeline, which helps maintain high data quality standards.”

4. Describe your experience with cloud-based data solutions.

This question focuses on your knowledge of cloud technologies and their application in data engineering.

How to Answer

Discuss specific cloud platforms you have worked with and how you utilized them in your data engineering tasks.

Example

“I have worked extensively with AWS and Azure for data storage and processing. For instance, I used AWS S3 for data storage and AWS Lambda for serverless data processing, which allowed us to scale our data operations efficiently.”

5. Can you explain the concept of data governance and its importance?

This question evaluates your understanding of data governance principles and their relevance in data engineering.

How to Answer

Define data governance and discuss its significance in maintaining data quality, security, and compliance.

Example

“Data governance refers to the management of data availability, usability, integrity, and security. It is crucial because it ensures that data is accurate and trustworthy, which is essential for making informed business decisions and complying with regulations.”

Behavioral Questions

1. Tell me about a time you faced a significant challenge in a project. How did you overcome it?

This question assesses your problem-solving skills and resilience in the face of adversity.

How to Answer

Use the STAR method (Situation, Task, Action, Result) to structure your response, focusing on the challenge and your approach to resolving it.

Example

“In a previous project, we encountered a major data inconsistency issue that threatened our timeline. I organized a team meeting to identify the root cause, which turned out to be a misconfiguration in our ETL process. We quickly implemented a fix and adjusted our testing protocols, which allowed us to meet our deadline without compromising data quality.”

2. How do you prioritize tasks when managing multiple projects?

This question evaluates your time management and organizational skills.

How to Answer

Discuss your approach to prioritization, including any tools or methods you use to manage your workload effectively.

Example

“I prioritize tasks based on their impact on project goals and deadlines. I use project management tools like Jira to track progress and ensure that I focus on high-priority tasks first. Regular check-ins with my team also help me adjust priorities as needed.”

3. Describe a situation where you had to collaborate with a cross-functional team.

This question assesses your teamwork and communication skills.

How to Answer

Provide an example of a project where you worked with different teams, highlighting your role and how you facilitated collaboration.

Example

“I worked on a project that required collaboration between the data engineering, data science, and marketing teams. I organized regular meetings to ensure everyone was aligned on objectives and shared updates. This open communication helped us successfully launch a new analytics tool that improved our marketing strategies.”

4. What motivates you to work in data engineering?

This question aims to understand your passion for the field and what drives you professionally.

How to Answer

Share your enthusiasm for data engineering and how it aligns with your career goals.

Example

“I am motivated by the challenge of transforming raw data into actionable insights. I find it rewarding to solve complex problems and contribute to data-driven decision-making that can significantly impact a business’s success.”

5. How do you stay updated with the latest trends and technologies in data engineering?

This question evaluates your commitment to continuous learning and professional development.

How to Answer

Discuss the resources you use to keep your skills current, such as online courses, webinars, or industry publications.

Example

“I regularly follow industry blogs, participate in webinars, and take online courses on platforms like Coursera and Udacity. I also attend local meetups and conferences to network with other professionals and learn about emerging trends in data engineering.”