Analytics 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 $139,000 - $172,000 + 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 office & may allow for a hybrid schedule as per Google policy.

Responsibilities

• Own data engineering workstreams, including data acquisition, data pipelining, data quality, data prep, and data warehousing.
• Collaborate with team members in delivering custom web applications
• Design and implement advanced analytics, AI/ML integrations, and rich data visualizations.
• Consistently engage stakeholders and partner teams to gain a deep understanding of business needs, and to develop innovative solutions to challenging business problems.
• Incubate, prototype, and iterate in this space to quickly bring new ideas to production

Minimum qualifications:

• Bachelor’s degree in Computer Science, Manufacturing Engineering, Industrial Engineering, Mechanical Engineering, or a related field and 5 years of progressive post-baccalaureate experience in the job offered or in a Manufacturing Engineer-related occupation.
• Alternatively, will accept a Master’s degree in Computer Science, Manufacturing Engineering, Industrial Engineering, Mechanical Engineering or a related field, and 3 years of experience in the job offered or in a Manufacturing Engineer-related occupation.
• Position requires 3 years of experience in the following: Web application development across the full stack, including client and server side code, and interactive visualizations, using industry standard technologies such as Angular, React, or .Net; Development of AI/ML analytics, and integration into a reporting stack; Dashboard design and creation using common tools, such As PowerBi and Tableau; Design and development of ETL pipelines for enterprise data applications, using industry standard tools such as Informatica, Microsoft SSIS, or Databricks; and Experience delivering predictive analytics and forecasting using technologies such as scikit, TensorFlow, and PyTorch.