Martin Wainaina

Improved app performance by 30% and achieved a 4.8-star App Store rating across Co-op Bank and YEA! by eliminating expensive main-thread operations and parallelizing data processing using Swift Concurrency based on Xcode Instruments analysis. Reduced authentication user drop-off by 25% by architecting a unified biometrics flow (FaceID, TouchID, M-PIN, AWS Liveliness) with SwiftUI, Keychain, and SSL pinning for 71,000+ active users. Owned end-to-end delivery of 25 production features from prototyping to App Store release with zero critical regressions over a 21-month engagement. Reduced API integration error rate by 35% by defining REST/JSON contract standards and introducing typed-throws error handling across network layers (QA defects reduced from 14 per sprint to under 4). Cut regression bug rate by 20% by introducing XCTest suites for authentication, payment, and navigation modules, raising test coverage from near-zero to 60%.

Developed and maintained secure banking applications serving over 2 million users. Implemented mobile apps (Android and iOS) using SwiftUI and Kotlin, improving performance by 30%. Diagnosed and resolved bugs to ensure seamless functionality across platforms. Collaborated with front-end and back-end teams to deliver scalable solutions. Contributed to apps built for Co-op Bank, including YEA! Co-op Bank and Mco-opCash (New).

Led delivery of 3 core banking modules (MoneyGram, Co-op Remit, Inter-Account Transfers), achieving a 99.91% transaction success rate across 707,207 transactions using MVVM and Clean Architecture with SOLID principles. Reduced onboarding time by 60% for 2 junior iOS engineers via code review standards, reusable SwiftUI components, and architectural guidelines (time-to-first PR reduced from 3 weeks to 5 days). Improved authentication reliability to 98.2% uptime for 71,000+ daily users by redesigning Keychain-based session management with Swift Concurrency (async/await and Actor model) to eliminate race conditions. Delivered crash and usage insights by analyzing 13,500 events with Python (Pandas, NumPy), surfacing 3 high-impact UX improvements adopted in two sprints.