Senior 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.
  
• Reduce data fragmentation by deprecating legacy trackers and data assets, and by owning technical solutions to establish discoverable source-of-truth assets.
• Size, plan, and lead zero-downtime data migrations across data pipelines that support all Google.org pillar teams, and guide integration across a heterogeneous set of data tools.
  
• Partner with business and engineering partners to design scalable AI-BI reporting architectures and AI workflow guardrails.
  
• Mentor teammates and build cross-team consensus on systems strategy and priorities.

Minimum qualifications:

• Bachelor's degree or equivalent practical experience.
• 5 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.).
  
• 5 years of experience coding in one or more programming languages.
  
• 5 years of experience working with data infrastructure and data models by performing exploratory queries and scripts.
  

Preferred qualifications:

• 5 years of experience with statistical methodology and data consumption tools such as business intelligence platforms and Jupyter notebooks.
• 3 years of experience developing project plans and delivering projects on time within budget and scope.
  
• 3 years of experience partnering with stakeholders (e.g., users, partners, customer), and managing stakeholders/customers.
  
• Experience with Machine Learning for production workflows.
  
• Subject-matter expertise in two or more technical areas (e.g., Big Data, Systems Design, Machine Learning) with advanced proficiency in Python/SQL, distributed data computing (Spark/DataFlow/Flume), data modeling, and cloud data coordination.
  
  

• Bachelor's degree or equivalent practical experience.
• 5 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.).
  
• 5 years of experience coding in one or more programming languages.
  
• 5 years of experience working with data infrastructure and data models by performing exploratory queries and scripts.