Developed a foundational data processing feature for the Hyp-ed/hyped-2025 repository, focusing on accelerometer sensor data. Built the AccelerometerPreprocessor module in Rust to compute acceleration magnitude, filter outliers using quartile analysis, and assess sensor data reliability, thereby improving the quality of telemetry for downstream analytics and machine learning workflows. Enhanced code maintainability by updating dependencies and removing unused test code, streamlining the build process and reducing technical debt. Demonstrated expertise in embedded systems, data filtering, and sensor data processing through end-to-end delivery of this feature, enabling more reliable product decisions based on robust and well-processed sensor input data.