Data Engineer

Minimum qualifications:

• Bachelor’s degree in Computer Science, Engineering, Computer Information Systems, Mathematics, Physics, or a related field and 5 years of progressive post-baccalaureate experience in the job offered or in a Data Engineer-related occupation.
• Alternatively, will accept a Master’s degree in Computer Science, Engineering, Computer Information Systems, Mathematics, Physics or a related field, and 3 years of experience in the job offered or in a Data Engineer-related occupation.
• Position requires 3 years of experience in the following: Data pipeline design and dimensional data modeling for synchronous and asynchronous system integration; Coding in Python, Java, Go, or C++ to build and maintain data infrastructure; Data pipeline implementation using external or internal data processing stacks; Data exploration by performing exploratory queries and scripts on data models; and Data consumption and visualization using business intelligence tools including Tableau, Power BI, or DataStudio.

About the job

Google's mission is to organize the world's information and make it universally accessible and useful. Our Devices & Services team combines the best of Google AI, Software, and Hardware to create radically helpful experiences for users. We research, design, and develop new technologies and hardware to make our user's interaction with computing faster, seamless, and more powerful. Whether finding new ways to capture and sense the world around us, advancing form factors, or improving interaction methods, the Devices & Services team is making people's lives better through technology.

The US base salary range for this full-time position is $220,050 - $226,000 +15% Bonus Target + equity + benefits determined by role, level, and location. Individual pay is determined by additional factors, including job-related skills, experience, and relevant education or training. Learn more about benefits at Google.

Position reports to the Google (Mountain View, CA) office & may allow for a hybrid schedule as per Google policy.

Responsibilities

• Design and build secure, scalable, and reliable data processing systems and infrastructure.
• Develop and maintain data models and pipelines to transform data for analysis and machine learning.
• Enable data-driven decisions by analyzing data, profiling systems, and deploying machine learning models.
• Create and consult on data visualizations and reports using various business intelligence tools. Consult with cross-functional partners to define data requirements, standards, and best practices. 
• Provide ongoing support to data users by maintaining dashboards, authoring documentation, and delivering training.