Data Product Lead, YouTube
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Minimum qualifications:
- Bachelor’s degree or equivalent practical experience.
- 7 years of experience with data analysis and SQL/MySQL.
- 7 years of experience in technical troubleshooting, and managing internal/external partners or customers.
- 7 years of experience in system design or code comprehension in one programming language (e.g., Java, C++, Python, etc.).
Preferred qualifications:
- Master’s degree in Business, Statistics, Mathematics, Economics, Engineering, Applied Science, a related field or equivalent practical experience.
- Experience with delivering data products at enterprises.
- Ability to navigate ambiguity in a changing environment with multiple stakeholders.
- Ability to think critically and investigate, breaking down issues into manageable components.
- Excellent business and technical communication, organizational, and problem-solving skills.
About the job
The YouTube GTM team is responsible for driving all go-to-market functions for the YouTube Business organization including strategy and business operations, analytics and data science, partnership enablement, and product activation. This team combines operational and problem-solving skills with a pragmatic sense of how to get things done and drive change across a global organization.As a Data Product Manager, you are responsible for shaping go-to-market priorities to accelerate growth and resource the business accordingly, enhancing skills and capabilities to support execution of business priorities. You will drive efficiency across business operations and building excellent go-to-market infrastructure (e.g. data, reports, dashboards and Generative Artificial Intelligence (AI) solutions).
Responsibilities
- Lead and manage Go-to-Market (GTM) products supporting the YouTube Business teams.
- Utilize core technical role-related knowledge, relevant Google-wide technologies and processes, and translate business needs into technical solutions.
- Conduct requirements gathering and project scoping sessions with subject matter experts, business users, and executive stakeholders to discover and define business data needs.
- Design, build, and optimize the data architecture and extract, transform, and load (e.g., Extract, Transform, and Load (ETL)) pipelines to make them accessible for business data analysts, data scientists, and business users to enable data-motivated decision-making.
- Write and review end-user and technical documents, including requirements and design documents for existing and future data systems, as well as data standards and policies.
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