Results-driven Senior iOS Engineer with 11 years of experience developing and launching native iOS applications. Proficient in SwiftUI and UIKit, while implementing architectural patterns in MVC and MVVM, and skilled in testing with XCTest and XCUITest. Expert in improving app performance and stability through root cause analysis and enhanced test coverage across the SDLC.

• Refactored a UIKit banking application to SwiftUI as part of a four engineer team, modernizing the user interface resulting in a 15% improvement in initial load times.
• Optimized large data operations by leveraging async/await to offload processing to background queues, resulting in a 35% reduction in memory usage during peak usage and preventing UI freezes.
• Utilized Instruments to analyze and optimize encrypted data handling, resolving memory leaks and reducing memory consumption by 35% during large data operations.
• Developed fluid and engaging user interfaces by leveraging Core Animation to create custom animations and transitions, enhancing user experience and application interactivity.
• Built core functionalities using SwiftOTP for a password manager application, including token storage and generation of time-based one-time passwords (TOTP), improving user security and account protection.
• Leveraged CryptoSwift to implement on-device AES-256 encryption for secure storage and management of sensitive user credentials, including usernames, passwords, and TOTP secrets.

• Led the design and implementation of scalable automated test frameworks for mobile applications using XCUITest and Compose Test, empowering feature teams to increase code coverage by up to 70%.
• Developed and executed automated unit test suites using XCTest and Swift, achieving 90% test coverage for user flows and reducing test execution time by 30%.
• Maintained testing infrastructure, developing and integrating automation frameworks into AWS CodePipeline and AWS Device Farm, ensuring 99% uptime and increasing deployment frequency by 25%.
• Drove the development of an automated testing framework using Pytest and NumPy for AI threat detection systems, improving detection accuracy by 30%.
• Developed and implemented end-to-end tests for distributed data ingestion pipelines, validating data integrity and performance across various microservices and decreasing data ingestion latency by 15%.
• Conducted performance/load testing with Locust for REST and GraphQL endpoints, identifying bottlenecks and achieving a 25% improvement in API response times.
• Utilized Sentry to analyze errors and logs in staging and production environments, with the goal of root cause identification and resolution and identifying test gaps.