Senior Data Engineer

Minimum Qualifications:

• Bachelor's degree in a quantitative field (e.g., Statistics, Computer Science, Math, Physics, Engineering), or equivalent practical experience.
• 8 years of experience in data engineering or business intelligence roles.
• Experience with relational databases, including SQL queries, database definition, and schema design.
• Experience with one or more programming language (e.g., Python, Java, C++, etc.).

Preferred Qualifications:

• Master’s degree in a quantitative discipline (e.g., Computer Science, Engineering, Statistics, Math).
• Experience with data warehouses, distributed data platforms, and data lakes.
• Ability to break down multi-dimensional problems.
• Ability to navigate ambiguity in a fast-paced environment with multiple stakeholders.
• Excellent business and technical communication, organizational, and investigative skills.

About the Job

As a Data Engineer within YouTube Analytics and Data Science, you will be part of a community of analytics professionals who work on projects ranging from developing critical data pipelines, to building tools to analyze the content partnerships and creator ecosystem.

The team is responsible for YouTube’s business data, helping business leaders make sense of operations through accurate business intelligence. The team uses SQL and YouTube’s ETL systems to produce useful datasets, and establish best practices for data sets and reporting.

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

• 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 (ETL) pipelines to make them accessible for Business Data Analysts, Data Scientists, and business users to enable data-driven decision-making.
• Work with analysts to scale value-creating capabilities, including data integrations and transformations, model features, and statistical and machine learning models.
• Drive data reliability, data integrity, and data governance, enabling accurate, consistent, and trustworthy data sets, business intelligence products, and analyses
• Communicate with analysts to understand user journeys and data sourcing inefficiencies.