Head of YouTube Business Data Engineering
Job Summary
Job Description
The Head of YouTube Business Data Engineering leads data engineering and infrastructure projects, building a team from the ground up. This role involves defining business data needs through stakeholder engagement, driving data reliability and governance standards, and optimizing data architecture and ETL pipelines. The successful candidate will collaborate with analysts, data scientists, and business users to enable data-driven decision-making, and build a roadmap for the team's goals and strategy. Responsibilities include requirements gathering, project scoping, analyst training, and advocating best practices. The role requires significant experience in data infrastructure, data modeling, pipeline design, and team leadership.
Must have:
- 10+ years data infrastructure experience
- 10+ years data pipeline design experience
- 5+ years coding and team leadership experience
- Data modeling expertise
- ETL pipeline optimization
- Stakeholder management
Good to have:
- Master's degree in quantitative field
- Experience with data warehouses, distributed platforms, and data lakes
- Ability to work in changing environments
- Excellent communication and problem-solving skills
3 skills required
Job Details
Minimum qualifications:
- Bachelor's degree or equivalent practical experience.
- 10 years of experience in working with data infrastructure and data models by performing exploratory queries and scripts.
- 10 years of experience in designing data pipelines, and dimensional data modeling for synch and asynch system integration and implementation using internal and external stacks.
- 5 years of experience with coding in one or more programming languages, and in a people management, supervision, or team leadership role.
Preferred qualifications:
- Master’s degree in a quantitative field (e.g., Computer Science, Engineering, Statistics, Math).
- Experience with data warehouses, distributed data platforms, and data lakes.
- Ability to work in a changing environment with multiple stakeholders.
- Excellent thinking skills, with the ability to break down multi-dimensional problems.
- Excellent business and technical communication, organizational, and problem-solving skills.
About the job
The YouTube Business Strategy and Operations 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. The team shapes go-to-market priorities to accelerate growth and resource the business accordingly, enhancing skills and capabilities to support execution of business priorities, driving efficiency across business operations, and building excellent go-to-market infrastructure (e.g., data, reports, dashboards).At YouTube, we believe that everyone deserves to have a voice, and that the world is a better place when we listen, share, and build community through our stories. We work together to give everyone the power to share their story, explore what they love, and connect with one another in the process. Working at the intersection of cutting-edge technology and boundless creativity, we move at the speed of culture with a shared goal to show people the world. We explore new ideas, solve real problems, and have fun — and we do it all together.Responsibilities
- Lead data engineering and infrastructure projects for YouTube. Build a goals, strategy, and roadmap.
- Build data engineering and data infrastructure team from the ground up.
- Conduct requirements gathering and project scoping sessions with executive stakeholders, subject matter experts and business users to discover and define business data needs. Drive the standards in data reliability, data integrity, and data governance.
- Engage with the analyst community, communicate with analysts to understand user journeys and data sourcing inefficiencies, advocate best practices, and lead analyst trainings.
- Design, build, and optimize the data architecture and 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.
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