Venture: She Should Run
She Should Run convinces women from all political leanings, ethnicities, sexual identities, and backgrounds to see themselves as future candidates.
Expert: Martha Arellano
Engineering Manager at OfferUp
• Sr. Engineering Manager, Zume Inc.
• Principal Software Development Lead, Tally
• Cloud Platform Architect, VISUAL VOCAL
She Should Run partnered with Rippleworks to design a centralized, easily queryable data warehouse
By identifying and tackling the barriers to elected leadership, She Should Run (SSR) helps women from all political leanings, ethnicities, sexual identities, and backgrounds see themselves as future candidates. SSR’s network included more than 25,000 women across all 50 states in 2021. They are driven by a vision of seeing 250,000 women run for local, state, and federal offices by 2030.
SSR’s top priority was improving the way they use data in order to become more efficient with their resources, more targeted with their outreach, and more impactful with their programming. SSR partnered with Rippleworks to help design a centralized, easily queryable data warehouse that supports their information needs and helps realize their growth goals.
This project gave our organization a clear roadmap for our data architecture that will allow us to drive decision-making and strategic growth for years to come.
Thanks to the guidance, mentorship, and accountability we received throughout the process, our team now has the knowledge and capability to solve creative data problems and answer underlying data questions.
—She Should Run founder and CEO Erin Loos Cutraro
She Should Run partnered with Rippleworks Expert Martha Arellano, an engineering Leader with more than 18 years of experience from her time at OfferUp, Zume, Tally, Visual Local, Google, and Microsoft. Together, they:
• Reviewed SSR’s existing systems and data architecture
• Defined programmatic and organization-wide requirements of new data architecture
• Designed high-level data architecture; developed an implementation plan and timeline for further database development
• Defined resource requirements for implementing and maintaining data architecture future state
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