About the Role:

With such a large catalog of content, data and machine learning are fundamental to Tubi's success, and Tubi's Data Platform is the cornerstone of all data and ML use. At Tubi, you will join a stellar team of engineers with a passion for solving the most challenging problems using cutting-edge technology. In this Lead Data Engineering role, you will be expected to be a hands-on leader, leading by example as you and your team build out real-time systems to handle data at massive scale. You will enable machine learning engineers to iterate and experiment faster than ever before. You will help data scientists take ideas to production in days or weeks, not months or years. And you will build tools to enable data analysis and modeling for even the least tech-savvy colleagues. In short, you will enable Tubi to be truly data-driven.

Responsibilities: 

• Handle the collection and processing of large scale raw data.
• Building low latency and low maintenance data infrastructure that powers the whole company
• Develop and enhance our analytics pipeline by creating new tools and solutions to facilitate intuitive data consumption for stakeholders
• Constructs and improves infrastructure for data extraction, transformation, loading and cleaning from diverse sources using API’s, SQL and AWS technologies.
• Improving data quality by building tools, processes and pipelines to enforce, check and manage data quality at a large scale.
• Implement CI/CD pipelines for data operations ensuring efficient and smooth deployment of data models and applications.
• Address ad hoc data requests and core pipeline tasks

Your Background:

• 5+ years of experience building scalable batch and streaming data pipelines (Spark or Flink)
• 3+ years of experience designing and implementing pipelines for ETL and cleaning of data from a wide variety of sources using API’s, SQL, Spark and AWS technologies.
• Greenfield data warehouse modeling experience
• Strong knowledge of Streaming, distributed databases, and cloud storage (e.g., S3).
• Strong experience in JVM language (Scala is not required, but preferred)
• Prior experience with Kafka, Kinesis, or equivalent.

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