Data Engineer

Google Cloud accelerates every organization’s ability to digitally transform its business and industry. We deliver enterprise-grade solutions that leverage Google’s cutting-edge technology, and tools that help developers build more sustainably. Customers in more than 200 countries and territories turn to Google Cloud as their trusted partner to enable growth and solve their most critical business problems.

The US base salary range for this full-time position is $123,900 - $151,000 + 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 Austin, TX office & may allow for a hybrid schedule as per Google policy.

Responsibilities

• Design and implement secure, scalable, and reliable data solutions, including data models, pipelines, and infrastructure frameworks.
• Perform exploratory data analysis and leverage machine learning models to enable data-driven insights and business solutions.
• Create and deliver technical enablement materials, including best practice guides, sample code, presentations, and educational workshops.
• Collaborate with product management, engineering, and data science teams to influence product development and drive technical excellence.
• Advise customers on migrating and optimizing database systems and data processing workloads on Google Cloud Platform. Analyze cloud database environments and consult on the optimal design for performance and deployment on Google Cloud Platform.

Minimum qualifications:

• Bachelor’s degree in Computer Science, Computer Engineering, Information Systems, or a related field, and 1 year of experience in the job offered or in a Data Engineer-related occupation.
• Position requires 1 year of experience in the following: Database performance tuning, including SQL query optimization; Database management using tools for backup, recovery, and partitioning; Large-scale data migration from on-premise or data center environments to cloud; Development of ETL or ELT workflows, data pipeline design, and dimensional data modeling; Software development in Java, C++, Python, Go, or JavaScript