Join the YouTube Business Strategy & Operations team, instrumental in driving go-to-market functions for the YouTube Business Organization. This role is key to shaping growth strategies and resource allocation within a dynamic global environment. You will collaborate with a community of analytics professionals, contributing to critical data pipelines, building analysis tools for content partnerships and creator ecosystems, and providing data-driven insights to leadership for optimizing partner-facing business teams. At YouTube, we champion the power of voice and connection through stories, operating at the forefront of technology and creativity to reflect the world and foster community.

Your role will involve close collaboration with analysts to productionize and scale value-adding capabilities, encompassing data integrations, transformations, model features, and statistical/machine learning models. You'll be responsible for building and maintaining robust data platforms that ensure data reliability, integrity, and governance, leading to accurate and trustworthy datasets. Furthermore, you will conduct requirements gathering sessions with subject matter experts and stakeholders to define critical business data needs, and subsequently design, build, and optimize data architecture and ETL pipelines. Writing and reviewing both end-user and technical documentation, including requirements, design documents for data systems, and data standards, will also be a core part of your duties.

We are seeking candidates with a Bachelor's degree or equivalent practical experience, coupled with a minimum of 3 years of hands-on coding experience in at least one programming language. A proven track record of 3 years in designing data pipelines and dimensional data modeling for both synchronous and asynchronous system integrations, utilizing internal and external stacks (such as Flume, DataFlow, Spark), is essential. Additionally, you should possess 3 years of experience working with data infrastructure and data models, actively performing exploratory queries and scripting. Preferred qualifications include a Master’s degree in a quantitative field, experience with data warehouses, large-scale distributed data platforms, data lakes, and AI/Gen AI data applications, alongside exceptional problem-solving, communication, and analytical skills.