Data Engineer, Services

As a Data Engineer at Penn Interactive, you will be responsible for collaborating with Data Science, Reporting, Analytics, and engineering teams to design and maintain efficient data pipelines and infrastructure. You'll oversee the continual enhancement of our data architecture and adapt new technologies to improve workflows. Additionally, your duties will include maintaining streaming pipelines, taking ownership of projects, and ensuring high performance and reliability across all systems.

To become a Data Engineer at Penn Interactive, you should have a solid foundation in computer science, complemented by at least 5 years of experience in data or software engineering. Strong knowledge of languages like Python or Java is essential, along with solid skills in SQL and relational databases. Familiarity with tools such as Docker, Kubernetes, and streaming technologies like Kafka and Spark will set you up for success in this role.

While not mandatory, having an interest in professional sports, betting, or eSports can be beneficial for a Data Engineer position at Penn Interactive. This knowledge can help you understand the data landscape better and make informed decisions that align with our business initiatives centered around gaming and user engagement.

As a Data Engineer at Penn Interactive, you will work with a variety of advanced technologies including Python, Java, Docker, and Kubernetes. You'll also have the opportunity to engage with streaming technologies like Kafka, Spark, and Flink, as well as SQL-based data warehouses like BigQuery and Redshift, all while collaborating with cutting-edge data science and analytics teams.

At Penn Interactive, you can expect a fun and relaxed work environment that promotes creativity and collaboration. Recently recognized as a top workplace in the U.S., we value diversity, equity, and inclusion, and we believe that employees thrive when they are free to be themselves. Our culture encourages innovation, mentorship, and continuous learning.

When answering this question, emphasize specific projects you've worked on involving data pipelines. Highlight the technologies you utilized, the challenges faced, and how you ensured their efficiency and reliability. Be prepared to discuss the architecture of the pipelines you've built and any improvements or optimizations you’ve implemented.

Discuss the tools you are most experienced with, such as Python, SQL, and Kafka. Explain your rationale for preferring these tools based on their performance, ease of use, and compatibility with your workflow. Be honest about your experiences using them and any challenges you’ve overcome.

To ensure data quality and integrity, discuss your approaches to data validation and testing. Mention any testing frameworks you're familiar with, such as Pytest or Junit, and provide examples of how you've identified and resolved data discrepancies in past projects.

Share a specific example of a difficult challenge, particularly one related to scaling, data integrity, or pipeline performance. Explain your thought process, the steps taken to resolve the issue, and the outcome. This showcases your problem-solving skills and persistence.

Discuss your methods for staying updated, such as following industry blogs, attending webinars, or participating in online forums. Highlight specific resources that have helped you learn about new trends and tools so you can adapt them in your work.

When discussing collaborative projects with data scientists, emphasize your role in providing data insights and infrastructure support. Outline the project scope, your contributions, and how your collaboration enhanced the project outcomes.

Make sure to mention specific database technologies like MySQL, PostgreSQL, or Redshift, and provide examples from your experience. Share your perspectives on choosing the right database for different tasks, focusing on performance and scalability.

Discuss your experiences with streaming technologies like Kafka or Spark, emphasizing the challenges and best practices in managing real-time data ingestion. Your answer could include specific tools or techniques you've used to optimize this process.

In your answer, highlight the importance of clear and thorough documentation. Discuss how it facilitates understanding and collaboration within a team. Mention specific tools or formats you use to document your processes and workflows.

This is an opportunity to align your values and aspirations with the company’s mission. Share how your skills and interests in data engineering connect with Penn Interactive's focus on innovative and fun gaming experiences. Show your enthusiasm for the role and how you can contribute to the team.