The Hertz Corporation Data Engineer Interview Questions + Guide in 2025

The Hertz Corporation is a leading global mobility organization, recognized for its extensive network of vehicle rental services and commitment to innovation in the travel and transportation industry.

As a Data Engineer at Hertz, you will be responsible for building and maintaining robust data pipelines and workflows that support machine learning, business intelligence, analytics, and software products. This role requires close collaboration with data scientists, analysts, and software developers to deliver high-quality data-driven solutions that drive operational efficiency and enhance decision-making processes across the organization. Key responsibilities include designing end-to-end data integration architectures, optimizing data workflows, and ensuring the integrity and quality of data through rigorous testing and documentation.

The ideal candidate will possess strong expertise in SQL and Python, with a solid understanding of data engineering principles and cloud-based technologies, particularly in AWS environments. Experience with modern data processing tools like Databricks and event-driven architectures such as Kafka or Kinesis will be highly advantageous. Additionally, the role demands strong problem-solving skills, effective communication abilities, and a knack for teamwork, as you will be expected to guide and mentor junior engineers while collaborating on strategic initiatives.

This guide will help you prepare for your interview by highlighting the key skills and experiences that Hertz values in a Data Engineer, as well as providing insight into the types of technical challenges you may encounter during the interview process.

The interview process for a Data Engineer at Hertz is structured to assess both technical and interpersonal skills, ensuring candidates are well-rounded and fit for the collaborative environment. The process typically consists of several key stages:

1. Initial Screening

The first step is an initial screening, usually conducted via a phone call with a recruiter. This conversation lasts about 30 minutes and focuses on your background, experience, and motivation for applying to Hertz. The recruiter will also gauge your understanding of the role and the company culture, as well as your technical skills relevant to data engineering.

2. Technical Assessment

Following the initial screening, candidates typically undergo a technical assessment. This may involve a coding challenge or a live coding session where you will be asked to solve problems using SQL and Python. Expect to demonstrate your proficiency in advanced SQL techniques, including the use of Common Table Expressions (CTEs), window functions, and complex queries. Additionally, you may be tasked with designing an end-to-end data pipeline, showcasing your ability to architect data solutions effectively.

3. System Design Interview

In this round, candidates are required to present a system design for a data pipeline. You will need to explain your approach to data architecture, including data ingestion, transformation, and storage. This interview assesses your ability to think critically about data flow and your understanding of best practices in data engineering.

4. Behavioral Interview

The behavioral interview focuses on your soft skills and cultural fit within the Hertz team. You will be asked about your past experiences working in teams, handling conflicts, and leading projects. This is an opportunity to demonstrate your communication skills and your ability to collaborate with cross-functional teams, including data scientists and software developers.

5. Final Interview with Hiring Manager

The final stage typically involves a one-on-one interview with the hiring manager. This discussion will delve deeper into your technical expertise, leadership capabilities, and how you can contribute to the team’s goals. Be prepared to discuss your previous projects in detail and how they relate to the responsibilities of the Data Engineer role at Hertz.

As you prepare for these interviews, it’s essential to familiarize yourself with the specific skills and technologies relevant to the position, particularly in SQL and data pipeline design.

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 Technical Landscape

As a Data Engineer at Hertz, you will be expected to have a strong grasp of data pipeline architecture and data integration processes. Familiarize yourself with the specific technologies mentioned in the job description, such as AWS, Databricks, and PySpark. Be prepared to discuss your experience with these tools and how you have utilized them in past projects. Additionally, brush up on advanced SQL techniques, including CTEs and window functions, as these are crucial for the coding portion of the interview.

Prepare for System Design Questions

Expect to encounter system design questions that require you to conceptualize and articulate an end-to-end data pipeline. Practice designing data architectures that can handle various data sources and types, and be ready to explain your design choices. Consider how you would ensure data quality, integrity, and performance in your designs. Use real-world examples from your experience to illustrate your thought process.

Showcase Your Problem-Solving Skills

Hertz values candidates who can demonstrate strong problem-solving abilities. Be prepared to discuss specific challenges you have faced in previous roles and how you approached them. Use the STAR (Situation, Task, Action, Result) method to structure your responses, focusing on the impact of your solutions on the business or project outcomes.

Emphasize Team Collaboration

Collaboration is key in this role, as you will be working closely with data scientists, analysts, and other stakeholders. Highlight your experience in cross-functional teams and your ability to communicate complex technical concepts to non-technical audiences. Prepare examples that showcase your teamwork and leadership skills, especially in Agile or Scrum environments.

Align with Company Culture

Hertz champions diversity and inclusion, so be sure to convey your alignment with these values during the interview. Share experiences that demonstrate your commitment to fostering an inclusive work environment and your ability to work effectively with diverse teams. Understanding Hertz's mission and how your role contributes to their goals will also help you stand out.

Practice Effective Communication

Strong communication skills are essential for this role. Practice articulating your thoughts clearly and concisely, especially when discussing technical topics. Be prepared to explain your past projects and the technologies you used in a way that is accessible to interviewers who may not have a technical background.

Prepare for Behavioral Questions

Expect behavioral questions that assess your adaptability, resilience, and ability to handle feedback. Reflect on past experiences where you had to navigate challenges or changes in direction, and be ready to discuss how you managed those situations. This will demonstrate your ability to thrive in a dynamic environment like Hertz.

Follow Up with Questions

At the end of the interview, be prepared to ask insightful questions about the team, projects, and company culture. This not only shows your interest in the role but also helps you gauge if Hertz is the right fit for you. Consider asking about the current challenges the data engineering team is facing or how they measure success in this role.

By following these tips and preparing thoroughly, you will position yourself as a strong candidate for the Data Engineer role at Hertz. Good luck!

In this section, we’ll review the various interview questions that might be asked during a Data Engineer interview at The Hertz Corporation. The interview process will likely focus on your technical skills in data engineering, including your ability to design and implement data pipelines, your proficiency in SQL and Python, and your experience with cloud technologies and data integration.

System Design

1. Can you describe the architecture of a data pipeline you have designed in the past?

This question assesses your understanding of data pipeline architecture and your ability to communicate complex technical concepts clearly.

How to Answer

Discuss the components of the pipeline, including data sources, transformation processes, and storage solutions. Highlight any challenges you faced and how you overcame them.

Example

“I designed a data pipeline that ingested data from multiple sources, including APIs and databases. I utilized AWS services like Lambda for data processing and S3 for storage. The pipeline transformed the data using Apache Spark, ensuring it was clean and ready for analysis. One challenge was managing data latency, which I addressed by implementing a streaming solution using Kinesis.”

SQL and Data Manipulation

2. How would you write a SQL query to find employees who do not have a manager?

This question tests your SQL skills and your ability to manipulate data effectively.

How to Answer

Explain your thought process and the SQL functions you would use, such as JOINs or subqueries, to achieve the desired result.

Example

“I would use a LEFT JOIN between the employees table and the managers table, filtering for NULL values in the manager ID. The query would look like this: SELECT e.name FROM employees e LEFT JOIN managers m ON e.manager_id = m.id WHERE m.id IS NULL.”

3. Can you explain the difference between a CTE and a temporary table?

This question evaluates your understanding of SQL concepts and their practical applications.

How to Answer

Discuss the use cases for each, including performance considerations and scope.

Example

“A Common Table Expression (CTE) is used for temporary result sets that can be referenced within a SELECT, INSERT, UPDATE, or DELETE statement. It’s more readable and can be recursive. A temporary table, on the other hand, is a physical table stored in the tempdb and can be indexed, but it has a broader scope and can be used across multiple queries.”

Data Integration and ETL

4. Describe your experience with ETL processes. What tools have you used?

This question assesses your hands-on experience with ETL and data integration tools.

How to Answer

Mention specific tools you have used, the types of data you worked with, and the challenges you faced.

Example

“I have extensive experience with Informatica for ETL processes, where I designed workflows to extract data from various sources, transform it for analysis, and load it into our data warehouse. I also used Apache NiFi for real-time data ingestion, which allowed for more flexibility in handling streaming data.”

5. How do you ensure data quality in your pipelines?

This question evaluates your approach to maintaining data integrity and quality.

How to Answer

Discuss the methods you use to validate and clean data, as well as any tools or frameworks you implement.

Example

“I implement data validation checks at various stages of the pipeline, using tools like Great Expectations to define expectations for data quality. Additionally, I perform regular audits and use logging to track data anomalies, allowing for quick identification and resolution of issues.”

Cloud Technologies

6. What experience do you have with cloud platforms, particularly AWS?

This question assesses your familiarity with cloud technologies and their application in data engineering.

How to Answer

Highlight specific AWS services you have used and how they contributed to your data engineering projects.

Example

“I have worked extensively with AWS, utilizing services like S3 for storage, Redshift for data warehousing, and Lambda for serverless data processing. In one project, I set up a data lake on S3, which allowed for scalable storage and easy access for analytics.”

7. Can you explain the concept of event-driven architecture and its benefits?

This question tests your understanding of modern architectural patterns in data engineering.

How to Answer

Discuss the principles of event-driven architecture and how it can improve system responsiveness and scalability.

Example

“Event-driven architecture allows systems to react to events in real-time, improving responsiveness and scalability. For instance, using AWS Kinesis, I can process streaming data as it arrives, enabling near-instantaneous analytics and reducing latency compared to batch processing.”

Teamwork and Communication

8. How do you approach collaboration with data scientists and analysts?

This question evaluates your teamwork and communication skills.

How to Answer

Discuss your strategies for effective collaboration and how you ensure alignment on project goals.

Example

“I prioritize regular communication through stand-up meetings and collaborative tools like JIRA. I also make it a point to understand the data needs of data scientists and analysts, ensuring that the pipelines I build provide them with the necessary data in a usable format.”

9. Describe a time when you had to explain a complex technical concept to a non-technical audience.

This question assesses your ability to communicate effectively across different levels of the organization.

How to Answer

Provide an example that demonstrates your ability to simplify complex ideas and engage your audience.

Example

“I once presented a data integration strategy to the marketing team, who had limited technical knowledge. I used visual aids to illustrate the data flow and focused on the business impact rather than the technical details, which helped them understand how the integration would improve their campaign analytics.”