Business Intelligence Engineer, Devices And Services

About the job

In this role, you will gather, manage data of Google’s consumer hardware supply chain. You will create structure and value out of complex and ambiguous technical issues. You will understand the nuanced requirements of partners both within and outside Devices and Services.

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.

Additional Information:

The US base salary range for this full-time position is $102,000-$151,000 + 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 for new hire 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.

Responsibilities

• Write and review documents, including business requirements, functional design, and technical design for data pipelines and dashboards.
• Communicate with both technical and non-technical audiences, individual contributors and leadership.
• Design, develop, and support data warehouses supporting the supply chain organization.
• Design and develop dashboards and assist with ad hoc data analyses.
• Write SQL scripts and Extract, Transform, and Load (ETL) pipelines using a variety of traditional as well as large-scale distributed data systems.