Sr. Data Engineer (P3) & Data Engineer (P2) at Tesla (FAST library, RAPID dashboard, 4680 recipe systems); Process Development Engineer at Rivian.
Current RoleBusiness Operations & Strategy — Family-Owned Enterprise
Supporting operations, business planning, and field evaluation initiatives at my family's agricultural enterprise.
EducationUC Berkeley (M.Eng.) · UC San Diego (B.S.)
M.Eng. in Materials Science & Engineering with a Data Science concentration (3.92 GPA); B.S. in Chemical Engineering & Computer Science Minor.
I. About Me
Data engineering, materials science, and business operations.
My background spans data engineering, battery manufacturing, and materials science. I currently support business operations and strategic initiatives at my family's agricultural enterprise.
My engineering experience centers on battery manufacturing telemetry, cell formation protocols, and computational materials research. At Tesla (May 2023 – December 2024), I worked as a Data Engineer (P2) and Senior Data Engineer (P3), where I built the FAST (Formation Analytics Screening Toolbox) library, maintained the real-time RAPID telemetry dashboard for the Berlin and Austin Gigafactories, streamed Milmont facility sensor data to AWS, and built API recipe synchronization workflows for 4680 battery cells.
Before joining Tesla full-time, I worked as a Data Science Intern at ATOM Science in San Francisco (September 2022 – May 2023) while completing my M.Eng. at UC Berkeley, applying transfer learning and active learning in Python to screen molecular databases for drug discovery. Earlier, as a Process Development Engineer in Formation at Rivian (September 2021 – September 2022), I developed six cell formation recipes for the R1S, R2, and R3 programs and wrote precharge Solid Electrolyte Interphase (SEI) anomaly detection protocols.
Today, I focus on business operations and strategy at my family's agricultural enterprise, helping manage day-to-day operations while exploring practical ways to apply data analysis, field trials, and clear stakeholder reporting to agricultural workflows.
Battery formation analytics, real-time Gigafactory dashboards, and 4680 cell API recipe systems at Tesla.
Production data engineering and analytical tools built at Tesla to evaluate new development recipes during battery formation, stream real-time factory telemetry to AWS, and detect End-of-Line (EoL) cell defects.
Key Results
Enabled deterministic pass/fail trial evaluations for battery formation development recipes through the FAST library.
Increased R&D trial throughput for 4680 battery cells with automated API recipe synchronization.
Defined vendor scoring parameters for business analytics teams using End-of-Line defect detection metrics.
Content-addressable persistence, O(n) branching data structures, and historical commit restoration.
A Java implementation of core Git version-control functionality using content-addressable file persistence, object serialization, and O(n) complexity branching.
Key Results
Implemented core Git commands, O(n) branching, and persistent historical state restoration in Java.
View-only source code available upon email request.