Experience working with Big Data, information retrieval, data mining, or machine learning.

Experience in building multi-tier high availability applications with modern web technologies (e.g., NoSQL, MongoDB, SparkML, TensorFlow).

Experience architecting, developing software, or internet scale production-grade Big Data solutions in virtualized environments.

About the job

The Google Cloud Learning Services (CLS) mission is to make it easy for anyone, anywhere to learn the skills to succeed with Google Cloud technology. We are a team of Researchers, Engineers, Product Managers, Program Managers, and Architects developing next-gen personalized direct learning technologies. We deliver technical learning platform experiences for Google Cloud Platform (GCP) customers and measure their impact in the degree they provide them with the skills they need to succeed in building, deploying, and operating cloud solutions. We work cross-functionally with Cloud Learning Services Product, Marketing, Sales, and Support organizations to drive actionability.

As part of the CLS Data Products and Insights (DPI) team, you will be leading data science analytics projects across Google Cloud Learning Services and partnering with CLS researchers, developers, and cross-functional senior leaders across Google Cloud Support, Product, Engineering, Brand, and Sales organizations to build data infrastructure solutions that enable data scientists and other business stakeholders to experiment and generate actionable insights to the business. You will develop an understanding of business objectives, risks, challenges, and opportunities to ensure that data infrastructure and analytics efforts are supportive of our learning business decision making and are actionable by executive leadership.

We are responsible for democratizing CLS's business data, helping business leaders make sense of business operations through timely, accurate, and robust business intelligence. We use SQL and ETL 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 Engineers scale centralized reporting, to ensure the business is operating more effectively and efficiently.

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 $146,000-$216,000 + 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

• Act as a trusted technical advisor to business stakeholders and solve complex Big Data challenges.
  
• Work closely with data scientists to productionize and scale value-creating capabilities, including data integrations and transformations, model features, and statistical and machine learning models.
  
• Analyze on-premises and cloud database environments and consult on the optimal design for performance and deployment on Google Cloud Platform.
  
• 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.
  
• Drive the highest standards in data reliability, data integrity, and data governance, enabling accurate, consistent, and trustworthy data sets, business intelligence products, and analyses.