Data Engineer - Monetization

As a Data Engineer in the Monetization team at Twitch, your primary responsibilities will include designing and maintaining data pipelines, collaborating with data scientists to ensure data accuracy, and automating workflows. You'll also be tasked with creating data architectures that simplify complex datasets and making them accessible to various stakeholders across the company.

To qualify for the Data Engineer role in the Monetization team at Twitch, candidates should possess at least 1 year of experience in data engineering or a related field. Strong skills in SQL and Python programming, along with experience in cloud computing tools such as AWS or Google Cloud, are essential. A degree in computer science or a related field is preferred but not mandatory.

As a Data Engineer at Twitch, you'll be utilizing a range of technologies including Spark, EMR, Redshift, Glue, and S3 to develop and manage scalable data solutions. Proficiency in SQL and Python is crucial for creating effective data pipelines and ensuring data reliability.

The culture at Twitch is incredibly collaborative and community-driven. As a Data Engineer in the Monetization team, you will work with passionate professionals who are eager to help each other solve problems and innovate. The environment fosters creativity and encourages teamwork, allowing you to thrive while contributing to the company's mission.

Absolutely! Data Engineers at Twitch enjoy a range of perks including medical, dental, and vision insurance, a 401(k) plan, flexible PTO, and parental leave policies. Additionally, employees receive an Amazon Employee Discount, ensuring a balanced work-life experience.

In your response, focus on specific projects where you designed and built data pipelines. Highlight the technologies you used, challenges faced, and how you ensured data accuracy and reliability throughout the process.

Discuss your methodology for implementing data quality checks, including tools and metrics you use to monitor data accuracy. Make sure to provide examples of how you addressed data integrity issues in past projects.

Detail your experience with cloud platforms such as AWS, Google Cloud, or Azure. Highlight specific tools you have used, like S3 or Redshift, and explain how you've leveraged these tools to optimize data solutions.

Talk about specific automation tools or frameworks you've employed to streamline data workflows. Provide examples of how automation improved efficiency and reduced manual errors in your previous roles.

Share examples of interdisciplinary projects where you worked closely with data scientists and engineers. Discuss how you ensure clear communication of business requirements and translate them into actionable data solutions.

Discuss the various projects you've worked on that utilized SQL and Python, emphasizing your proficiency in writing complex queries and developing transformation scripts. Provide context around the impact of your solutions on the business.

Choose a specific problem that required creative thinking and technical skills. Outline the steps you took to analyze the problem, the solution you implemented, and the results achieved for the company.

Explain your approach to designing scalable data architectures. Discuss the principles you follow and any technologies you've used to ensure that your solutions can handle growing data volumes.

Share key best practices you adhere to in developing data pipelines, including modularization, documentation, and testing of code. Discuss how these practices help improve pipeline performance and maintainability.

Communicate your passion for the gaming and live-streaming industry, your alignment with Twitch's values, and how you see your skills contributing to the company's ongoing mission. Highlight your enthusiasm for working in such an innovative environment.