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.
- Master's degree in a quantitative field (e.g., Computer Science, Engineering, Statistics, Math).
- Experience with data warehouses, distributed data platforms, and data lakes.
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About the job
As a Data Analytics Engineer within YouTube Analytics and Decision Support, you will be part of a community of analytics professionals who work on projects ranging from developing critical data pipelines that help run the business, build tools to analyze the content partnerships and creator ecosystem which guide business leadership on optimizing the effectiveness and efficiency of our partner facing business teams.
Data is the fundamental building block for every tool and every insight. We 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.
We are responsible for YouTube's business data, helping business leaders make sense of business operations through timely, accurate, and robust business intelligence. We use SQL and YouTube's systems to produce useful datasets, establish best practices for data sets and reporting, and develop a breadth of expertise in various data domains. Ultimately, Data Analytics Engineers scale centralized reporting, to ensure the business is operating more effectively and efficiently.
In this role, you will embody a product owner mindset and influence building data products to users.
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.