The role
From Google's own posting.
The Google.org Tech for Social Impact (TSI) team works to accelerate sustained social impact through technology. We do this by supporting Google.org and the wider ecosystem of nonprofits and civic entities through data infrastructure, data visualization tools, and products that help them accomplish their missions.
The TSI team leads the work to unify Google.org’s data and operational workflows into a single, trusted source of truth across global giving. By building a harmonious data repository, integrating partner records and CRM workflows, and powering AI-enabled business intelligence and workflow automations, we eliminate administrative friction and enable both data-driven human decision-making and safe, reliable AI autonomy across our philanthropic portfolio.
Individual pay is determined by factors including job-related skills, experience, and relevant education or training.
US: $156000 - $226000 (USD) + 15% bonus target + equity + benefits
Learn more about benefits at Google .
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.