Power System Engineering

We are tackling one of the world’s most important infrastructure challenges: helping the energy system become more visible, understandable, reliable, affordable, abundant, and clean.Originally born at X, Alphabet’s moonshot factory, Tapestry brings together experts in energy, AI, software, engineering, and product to build tools that help the electricity ecosystem plan smarter, move faster, and operate more efficiently.Chile, New Zealand, Australia, and Brazil as they work toward a cleaner, more resilient energy future.In this role, you will serve as a strategic technical bridge across cross-functional engineering, product, and partner teams to shape and execute Tapestry’s end-to-end grid model strategy. You will lead efforts to transform complex, multi-source industry grid and network data into unified, high-quality standards, such as the Common Information Model (CIM). By driving model quality assurance, optimizing data ingestion, and continuously refining our validation frameworks, you will play a pivotal role in scaling the reach, reliability, and real-world impact of our AI-powered grid platform.Serve as the primary technical liaison connecting internal and external cross functional teams to align on data requirements.Serve as the domain knowledge expert to help Product Manager to define the Model Ingestion User Journey and Functional Requirements.Actively collaborate with Software Engineering to provide operational feedback, flag bugs, and drive improvements for future tooling automation.Model Quality Assurance: Validate that all ingested models meet explicit use-case requirements with specified technical data requirements. Application Deployment: Stage and deliver validated grid models into target testing and production applicationsLead User Acceptance Testing: Conduct rigorous application-level testing of the models to verify performance, coordinate with end-users for validation, and provide final engineering sign-off.Masters degree in Electrical Engineering, Power Systems or related technical field.5+ years of experience in power systems modeling and analysis.Deep technical understanding of power system topologies and explicit experience working with Common Information Model (CIM) standard and data conversion between CIM and other commonly used data formats in the power industry.Strong understanding of power system principles, including power flow, short circuit analysis, transient stability, and economic dispatch.Proficiency in power system analysis software and experience working with physical grid data (GIS) and operational datasets Experience/knowledge in scientific programming languages (e.g, Matlab, Python, JAX, C++, Julia, etc.) with a desire to expand into new areas of coding languages and processes.Comfort navigating modern software environments, interacting with data layers, using ingestion tools, and collaborating closely with developers.D (or equivalent experience) in Electrical Engineering, Power Systems or related technical field.Familiarity with open-source power systems tools, libraries, or data repositories is a plus but not strictly required.Track record of delivering scalable solutions to complex software problemsExperience in startup or high-growth environmentsTapestry ValuesTake charge: Core Software & Automation Development: Use and provide feedback on the tools, and not responsible for writing production code to build the underlying platform or automate the pipeline infrastructure.Upstream Data Sourcing & Commercial Relations: Managing the initial commercial relationship with utilities or navigating the legalities of data sharing remains with the Partnerships and Data Science team.Competitive salary and equityMedical, dental, and vision coverageGenerous PTO and flexible hybrid work model401(k) with employer contributionProfessional developmentThe ability to work on important real-world problems within an Alphabet-backed environmentThe US base salary range for this position is $159,000 - $277,000 + benefits. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training.