Data Analytics Engineer, YouTube

Job Description

Minimum qualifications:
Bachelor’s degree or equivalent practical experience.
3 years of experience coding in one or more programming languages.
3 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.).
3 years of experience working with data infrastructure and data models by performing exploratory queries and scripts.
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 navigate ambiguity and work in a fast-moving environment with multiple stakeholders.
Ability to break down multi-dimensional problems.
Excellent business and technical communication, organizational, and problem-solving skills.
About the jobYouTube has grown into a global community where people all over the world access information, share videos, and build culture. The YouTube team helps creators build careers, artists and Media Companies reach audiences, create products like YouTube Kids, YouTube Music, and YouTube TV, and engages communities around shared passions and global conversations.The YouTube Go-To-Market 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.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.The US base salary range for this full-time position is $118,000-$174,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. The range displayed on each job posting reflects the minimum and maximum target salaries for the position across all US locations. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Your recruiter can share more about the specific salary range for your preferred location during the hiring process.Please note that the compensation details listed in US role postings reflect the base salary only, and do not include bonus, equity, or benefits. Learn more about benefits at Google.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 productionlize and scale value-creating capabilities including data integrations and transformations, model features, and statistical and machine learning models.
Drive standards in data reliability, data integrity and data governance, enabling accurate, consistent, and trustworthy data sets, business intelligence products, and analyses.
Engage with the analyst community, communicate with analysts to understand user journeys and data sourcing inefficiencies, advocate best practices, and lead analyst training.
Engage with the analyst community, communicate with analysts to understand user journeys and data sourcing inefficiencies, advocate best practices, and lead analyst training.Engage with the analyst community, communicate with analysts to understand user journeys and data sourcing inefficiencies, advocate best practices, and lead analyst training.Engage with the analyst community, communicate with analysts to understand user journeys and data sourcing inefficiencies, advocate best practices, and lead analyst training.