Data Engineer

As a Data Engineer at Tubi, your primary responsibilities will include owning the data needs of specific product verticals, building high-quality datasets, troubleshooting data quality issues, and acting as a liaison with the core data infrastructure team to meet business needs. You'll be involved in constructing data pipelines and enabling data scientists and analysts to get the insights they require.

The Data Engineer role at Tubi requires 2+ years of data engineering experience, proficiency in SQL, Python or Scala, and a solid understanding of big data tools like Spark. Familiarity with Databricks, DBT, and AWS is advantageous, along with a passion for creating and maintaining production-quality code. Experience working with TB-scale datasets is essential.

At Tubi, ensuring data quality is paramount. As a Data Engineer, you will identify and resolve data quality issues, set up monitors and alerts to prevent future occurrences, and prepare documentation that helps educate your team about the proper use of datasets. This proactive approach ensures our data remains reliable and actionable.

The culture for Data Engineers at Tubi is collaborative and supportive. You will be part of a new team focused on product-oriented data engineering, where mentorship and knowledge sharing are emphasized. Your input in setting best practices is not only welcomed but encouraged, allowing you to play an integral role in shaping the team dynamics.

As a Data Engineer at Tubi, you'll have the opportunity to work closely with various product verticals, including but not limited to Performance Marketing, Growth Marketing, User Identity, Ad Tech, and Content Analytics. Each of these teams poses unique data challenges that will allow you to apply your skills creatively.

In your response, emphasize your thought process and the technologies you used. Detail the steps you took, from identifying the requirements, designing the pipeline architecture, to implementation and testing. Make sure to highlight any challenges you faced and how you overcame them.

Discuss your methodology for identifying and fixing data quality issues, such as implementing alerts for anomalies, creating documentation, and regularly auditing datasets. Explain how you prioritize and communicate these issues within your team.

Be prepared to highlight your familiarity with tools like Spark, Hadoop, or Databricks. Provide specific examples of projects where you used these technologies to manage large datasets and the impact it had on your team's efficiency.

Elaborate on your communication style and how you ensure both teams are aligned. Mention practices like regular syncs, shared documentation, and how you gather feedback to continually enhance data accessibility.

Discuss your commitment to code quality through strategies like thorough testing, using version control, engaging in code reviews, and adhering to coding standards. Explain how these practices prevent issues and support smooth deployments.

Share a specific example that demonstrates your analytical skills and problem-solving ability. Discuss the context of the problem, your approach to solving it, and the eventual outcome, highlighting what you learned in the process.

Explain the various methods you are familiar with for data ingestion, including batch processing and real-time streaming. Provide a brief overview of how you choose the appropriate method based on project requirements.

Mention the resources you utilize to stay updated, such as blogs, courses, and forums. Discuss how you apply new knowledge to improve your work processes and share insights with your team.

Discuss your experience with specific cloud services like AWS or Google Cloud platforms, detailing how you've used these platforms to enhance data management, storage, or processing. Share any particular projects that highlight your ability to leverage cloud technology.

Emphasize the importance of documentation for ensuring team alignment and knowledge sharing. Discuss tools you use or methods you follow for maintaining clear and comprehensive documentation practices.