Data Engineer

Data Engineer, YouTube

Minimum qualifications:

• Bachelor's degree or equivalent practical experience.
• 5 years of experience designing data pipelines, and dimensional data modeling for synch and asynch system integration and implementation using internal (e.g., Flume, etc.) and external stacks (DataFlow, Spark, etc.).
  
• 5 years of experience coding in one or more programming languages.
  
• 5 years of experience working with data infrastructure and data models by performing exploratory queries and scripts.
  

Preferred qualifications:

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

About the job

The YouTube team helps budding creators build careers, artists and media companies reach audiences, and create products like YouTube Kids, YouTube Music, and YouTube TV. The YouTube Business Strategy and Operations team is responsible for driving all go-to-market functions for the YouTube business organization.

As a Data Engineer within YouTube Analytics and Data Science, you will be part of a community of analytics professionals who work on impactful projects. You will build the data sets that help run the business, piping the relevant data into and out of our tools, and making it useful for analysts across the organization to drive reporting and insights. You will be responsible for democratizing YouTube’s business data, helping business leaders make sense of business operations through timely, accurate, and business intelligence. You will build and maintain the YouTube ETL systems to produce useful datasets, establish best practices for data sets and reporting, and develop a breadth of expertise in various data domains.

At YouTube, we believe that everyone deserves to have a voice, and that the world is a better place when we 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 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.

As a Data Engineer within YouTube Analytics and Data Science, you will be part of a community of analytics professionals who work on impactful projects. You will build the data sets that help run the business, piping the relevant data into and out of our tools, and making it useful for analysts across the organization to drive reporting and insights. You will be responsible for democratizing YouTube’s business data, helping business leaders make sense of business operations through timely, accurate, and business intelligence. You will build and maintain the YouTube ETL systems to produce useful datasets, establish best practices for data sets and reporting, and develop a breadth of expertise in various data domains.

Responsibilities

• Build and maintain data platforms to enable data reliability, data integrity, and data governance, enabling accurate, consistent, and trustworthy data sets.
• 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.
• Work closely with analysts to productionize and scale value-creating capabilities, including data integrations and transformations, model features, and statistical and machine learning models. Engage with the analyst community, understand critical user journeys and data sourcing inefficiencies, advocate best practices and lead analyst trainings.
• 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.

Experience Level

Mid Level