Data Engineer, Devices and Services

Minimum qualifications:

• Bachelor’s degree in a quantitative or technical field (e.g., Computer Science, Statistics, Mathematics, Engineering), or equivalent practical experience.
  
• 3 years of experience in data engineering or business intelligence roles contributing to a shared codebase.
  
• 3 years of experience in system design or in a programming language (e.g., Java, C++, Python, etc.).
  
• Experience with relational databases, including writing and optimizing SQL queries and designing schema.
  

Preferred qualifications:

• Experience using SQL in a large-scale investigative, NoSQL, columnar context.
  
• Experience using continuous integration and deployment systems (e.g., Cloud Build, GitLab, Jenkins).
  
• Experience writing unit tests (e.g., PyTest, Selenium, JUnit).
  
• Knowledge in Machine Learning (ML).
  

About the job

In this role, you will build and maintain data processing, machine learning, and automation software to solve representation, scale, and efficiency problems.

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.

Responsibilities

• Build, optimize, and maintain data infrastructure including large-scale data processing pipelines (e.g., ETL; BigQuery, Snowflake, Redshift or Dataflow, Beam, Spark Jobs), Machine Learning (ML) model training/tuning (e.g. AutoML) and inference pipelines and orchestration workflows (e.g., Cloud Composer, Airflow, Prefect).
  
• Advocate for engineering best practices within the team and outside of it by writing and reviewing technical documentation, writing highly-readable and style-conformant code, and leading training.
  
• Comply with complicated and ever-changing policy and governance requirements.
  
• Collect data from analysts, data scientists, and business stakeholders and understand how they fit into larger business priorities.