Data Engineer, Google.org for Social Impact

• Define the architecture and technical solution for a harmonized data infrastructure, leading complex ETL constraints across both internal and external Google.org data systems.
• Develop a harmonized data repository and algorithmic data infrastructure solutions that simplify reporting and visualization across Google.org data tools.
  
• Execute zero-downtime data migrations with shadow testing and automated rollback checkpoints.
  
• Build well-structured data assets and tables powering dashboards and self-service AI-powered BI analytics tools.
  
• Implement AI-assisted data validation workflows.

Minimum qualifications:

• Bachelor's degree or equivalent practical experience.
• 3 years of experience coding in one or more programming languages.
  
• 3 years of experience working with data infrastructure and data models by performing exploratory queries and scripts.
  
• 3 years of experience designing data pipelines, and dimensional data modeling for synch and asynch system integration and implementation using internal (e.g., Flume, etc.) and external stacks (DataFlow, Spark, etc.).
  

Preferred qualifications:

• 3 years of experience with statistical methodology and data consumption tools such as business intelligence platforms and Jupyter notebooks.
  
• 3 years of experience partnering with stakeholders (e.g., users, partners, customer), and managing stakeholders/customers.
  
• 3 years of experience developing project plans and delivering projects on time within budget and scope.
  
• Experience with Machine Learning for production workflows.
  
• A passion for social impact, philanthropy, and empowering nonprofits and civic entities through data.
  
  

• Bachelor's degree or equivalent practical experience.
• 3 years of experience coding in one or more programming languages.
  
• 3 years of experience working with data infrastructure and data models by performing exploratory queries and scripts.
  
• 3 years of experience designing data pipelines, and dimensional data modeling for synch and asynch system integration and implementation using internal (e.g., Flume, etc.) and external stacks (DataFlow, Spark, etc.).