Penn Interactive Ventures Data Engineer Interview Questions + Guide in 2025

Penn Interactive Ventures (PIV) is the digital arm of PENN Entertainment, a leader in integrated entertainment and gaming experiences across North America, committed to pushing the boundaries of online gaming and sports media.

As a Data Engineer at Penn Interactive, you will play a pivotal role in developing and maintaining robust data systems that support various business initiatives. Your key responsibilities will include creating and optimizing data pipelines, developing APIs and services, and ensuring the integrity and efficiency of data flows across internal and external platforms. You will collaborate closely with data scientists, analysts, and other engineering teams to enhance data architecture and support real-time data processing, which is essential for delivering cutting-edge entertainment and gaming experiences.

To excel in this role, a solid foundation in computer science is paramount, particularly in data structures, distributed systems, and algorithms. Proficiency in programming languages such as Python or Java, coupled with a strong understanding of SQL and relational databases, is essential. Experience with cloud platforms (AWS, GCP, Azure), containerization tools (Docker, Kubernetes), and streaming technologies (Kafka, Spark) will significantly bolster your application. Additionally, a passion for data, a collaborative spirit, and a keen interest in sports and gaming will make you a perfect fit for the dynamic team at Penn Interactive.

This guide will equip you with the insights and knowledge necessary to prepare for your interview, helping you to articulate your relevant experiences and showcase your technical prowess effectively.

The interview process for a Data Engineer at Penn Interactive Ventures is designed to assess both technical skills and cultural fit within the team. Here’s a breakdown of the typical steps involved:

1. Initial Screening

The process begins with an initial screening, typically conducted by a recruiter. This 30-minute phone call focuses on understanding your background, experience, and motivations for applying to Penn Interactive. The recruiter will also provide insights into the company culture and the specifics of the Data Engineering team, ensuring that you have a clear understanding of what to expect.

2. Technical Assessment

Following the initial screening, candidates will undergo a technical assessment. This may take place over a video call and will involve a series of coding challenges and problem-solving exercises. Expect to demonstrate your proficiency in SQL, Python, and algorithms, as well as your understanding of data structures and distributed systems. You may also be asked to discuss your experience with data pipelines, cloud platforms, and any relevant technologies such as Kafka or Airflow.

3. Team Interviews

Successful candidates will then participate in a series of interviews with team members, including Data Engineers, Data Scientists, and possibly ML Engineers. These interviews will delve deeper into your technical expertise, focusing on your experience with building distributed systems, maintaining data infrastructure, and collaborating with cross-functional teams. Behavioral questions will also be included to assess your teamwork and communication skills, as well as your passion for data and the gaming industry.

4. Final Interview

The final stage of the interview process typically involves a conversation with a senior leader or manager within the Data Engineering team. This interview will focus on your long-term career goals, your fit within the company culture, and how you can contribute to the team’s objectives. It’s an opportunity for you to ask questions about the team dynamics, ongoing projects, and the company’s vision for the future.

As you prepare for your interviews, consider the specific skills and experiences that will be relevant to the role, particularly in areas such as data engineering, database management, and cloud technologies. 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 Company’s Vision

Familiarize yourself with Penn Interactive Ventures' mission to challenge the norms of the gaming industry. They are focused on creating immersive and innovative gaming experiences. Reflect on how your skills and experiences align with this vision, and be prepared to discuss how you can contribute to their goals, especially in the context of data engineering.

Highlight Your Technical Expertise

Given the emphasis on SQL, algorithms, and Python in the role, ensure you can discuss your experience with these technologies in detail. Be ready to provide examples of how you've used SQL for data manipulation, algorithms for problem-solving, and Python for building data pipelines or services. Demonstrating your technical proficiency will be crucial.

Showcase Your Experience with Distributed Systems

The role requires a solid foundation in building distributed systems. Prepare to discuss specific projects where you designed or maintained such systems, focusing on the challenges you faced and how you overcame them. Highlight your experience with tools like Kafka or similar messaging systems, as well as your familiarity with cloud platforms like AWS, GCP, or Azure.

Emphasize Collaboration and Communication Skills

Penn Interactive values strong organization and collaboration skills. Be prepared to share examples of how you've worked effectively in teams, particularly in cross-functional settings with data scientists, analysts, or other engineering teams. Highlight your ability to communicate complex technical concepts clearly to non-technical stakeholders.

Prepare for Problem-Solving Scenarios

Expect to encounter problem-solving questions that assess your analytical thinking and technical skills. Practice articulating your thought process when tackling complex data engineering challenges. Use the STAR (Situation, Task, Action, Result) method to structure your responses, ensuring you convey not just the solution but also the rationale behind your decisions.

Be Ready to Discuss Your Passion for Data

Demonstrate your enthusiasm for data engineering and how it drives your career. Share any personal projects, contributions to open-source, or continuous learning efforts that showcase your commitment to the field. If you have an interest in professional sports, betting, or eSports, weave that into your narrative to connect with the company’s focus areas.

Adaptability to New Technologies

The role requires adapting to new technologies and frameworks. Be prepared to discuss how you've approached learning new tools in the past and how you stay current with industry trends. This will show your willingness to grow and evolve with the company’s needs.

Ask Insightful Questions

Prepare thoughtful questions that reflect your understanding of the company and the role. Inquire about the team dynamics, the technologies they are currently exploring, or how they measure success in their data engineering initiatives. This not only shows your interest but also helps you gauge if the company is the right fit for you.

By following these tips, you can present yourself as a well-rounded candidate who is not only technically proficient but also aligned with the company’s culture and goals. Good luck!

Penn Interactive Ventures Data Engineer Interview Questions

In this section, we’ll review the various interview questions that might be asked during a Data Engineer interview at Penn Interactive Ventures. The interview will focus on your technical skills, problem-solving abilities, and experience in data engineering, particularly in building and maintaining data pipelines, working with APIs, and collaborating with cross-functional teams. Be prepared to demonstrate your knowledge of distributed systems, SQL, and cloud platforms.

Technical Skills

1. Can you explain the differences between SQL and NoSQL databases? When would you choose one over the other?

Understanding the strengths and weaknesses of different database types is crucial for a Data Engineer.

How to Answer

Discuss the characteristics of SQL databases (structured data, ACID compliance) versus NoSQL databases (flexibility, scalability). Provide scenarios where each would be appropriate based on data requirements.

Example

“SQL databases are ideal for structured data and complex queries, making them suitable for applications requiring ACID transactions, like financial systems. In contrast, NoSQL databases excel in handling unstructured data and can scale horizontally, making them a better choice for applications like social media platforms where data types and volumes can vary significantly.”

2. Describe your experience with building and maintaining data pipelines. What tools have you used?

This question assesses your hands-on experience with data engineering tasks.

How to Answer

Highlight specific tools (like Airflow, Kafka, etc.) and your role in the pipeline development process, including any challenges faced and how you overcame them.

Example

“I have built and maintained data pipelines using Apache Airflow for orchestration and Kafka for real-time data streaming. In one project, I faced challenges with data latency, which I resolved by optimizing the pipeline architecture and implementing better error handling mechanisms.”

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

Data quality is critical in data engineering, and interviewers want to know your approach.

How to Answer

Discuss methods such as data validation, testing, and monitoring that you implement to maintain data integrity.

Example

“I implement data validation checks at various stages of the pipeline to ensure data quality. This includes schema validation, duplicate checks, and using monitoring tools to alert me of any anomalies in real-time data processing.”

4. What is your experience with cloud platforms, and how have you utilized them in your projects?

Cloud platforms are essential for modern data engineering, and your familiarity with them is crucial.

How to Answer

Mention specific cloud services (AWS, GCP, Azure) and how you have leveraged them for data storage, processing, or analytics.

Example

“I have extensive experience with AWS, particularly using S3 for data storage and Redshift for data warehousing. In a recent project, I migrated our on-premise data warehouse to Redshift, which improved our query performance and reduced costs significantly.”

5. Can you explain how you would design a data model for a new feature in our application?

This question tests your ability to think critically about data architecture.

How to Answer

Outline your approach to understanding requirements, designing the schema, and considering scalability and performance.

Example

“I would start by gathering requirements from stakeholders to understand the data needs for the new feature. Then, I would design a normalized schema to reduce redundancy while ensuring it can scale as usage grows. I would also consider indexing strategies to optimize query performance.”

Programming and Algorithms

1. Describe a challenging algorithm you implemented in a previous project. What was the problem, and how did you solve it?

This question assesses your problem-solving skills and understanding of algorithms.

How to Answer

Explain the problem, the algorithm chosen, and the reasoning behind your choice, including any optimizations made.

Example

“In a project that required real-time data processing, I implemented a sliding window algorithm to efficiently calculate moving averages. This approach reduced the time complexity from O(n^2) to O(n), allowing us to handle larger datasets without performance degradation.”

2. How do you approach debugging a data pipeline that has failed?

Debugging is a critical skill for a Data Engineer, and interviewers want to know your process.

How to Answer

Discuss your systematic approach to identifying and resolving issues, including tools and techniques used.

Example

“I start by checking the logs to identify where the failure occurred. Then, I trace the data flow to pinpoint the source of the issue, whether it’s a data format problem or a connectivity issue with an API. I also use monitoring tools to set alerts for future failures.”

3. What programming languages are you most comfortable with, and how have you used them in data engineering?

Your programming skills are essential for this role, and interviewers want to know your proficiency.

How to Answer

Mention the languages you are proficient in and provide examples of how you have used them in data engineering tasks.

Example

“I am most comfortable with Python and Java. I use Python for data manipulation and building ETL processes, leveraging libraries like Pandas and NumPy. In Java, I have developed microservices for data processing that integrate with our data pipelines.”

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

Understanding modern architectural patterns is important for a Data Engineer.

How to Answer

Define event-driven architecture and discuss its advantages, particularly in data processing scenarios.

Example

“Event-driven architecture is a design pattern where the flow of the program is determined by events. This approach allows for real-time data processing and scalability, as services can react to events asynchronously, improving system responsiveness and resource utilization.”

5. How do you handle version control in your data engineering projects?

Version control is crucial for collaboration and maintaining code quality.

How to Answer

Discuss your experience with version control systems and how you manage changes in your projects.

Example

“I use Git for version control, ensuring that all code changes are tracked and documented. I follow a branching strategy where features are developed in separate branches and merged into the main branch after thorough code reviews and testing.”