Skill diminta
Analytical ThinkingCI/CDCommunicationDeliveryJenkinsSwiftSwiftUIXcode
Deskripsi
An ideal candidate is who
- Design and develop advanced iOS applications using
- Swift
- SwiftUI
- , and
- UIKit
Design System
- Build reusable components and extend the shared component library.
- Apply
- MVVM architecture
- , industry-standard
- design patterns
- , and software engineering
- best practices
- Implement robust
- networking
- layers and ensure thread safety using
- async/await
- Combine
- , or
- GCD/Operations
- Optimize for performance and memory efficiency using
- Xcode Instruments
- MetricKit
- , and leak detection tools.
- Utilize
- XCTest
- XCUITest
- Earl grey
- , and snapshot testing frameworks to develop highly maintainable, testable, and automated code.
- Write
- End to End Automation
- unit and snapshot coverage plus the on-device UI journeys for the flows you own.
- Collaborate with designers and backend engineers to ensure pixel-perfect UI and seamless API integration (REST).
- Implement
- push notifications
- background tasks
- , and
- offline data persistence
- with
- Core Data
- or
- SwiftData
- Participate in
- code reviews
- CI/CD integration
- (e.g., Fastlane,Jenkins, Xcode Cloud), and app store delivery workflows.
- Follow Apple’s
- Human Interface Guidelines
- and ensure accessibility, localization, and performance best practices.
- Continuously explore and adopt new frameworks like
- App Intents
- WidgetKit
- Live Activities
- , and
- Dynamic Island
- .● Strong communication skills, with the ability to explain complex technical issues to different audiences
- Possess
- exceptional problem-solving abilities
- and
- analytical thinking
- skills.
- Mentor Interns/SDE1 engineers through code reviews, pairing, and structured feedback — raising the technical bar of the team, not just your own output.
- Explore AI-driven capabilities — on-device ML, LLM-powered features, or AI-assisted dev tools — to enhance product experience and engineering efficiency.
- Proficient in using AI coding assistants (e.g., Claude Code) as part of the daily development workflow — for code generation, refactoring, debugging, test writing, and reviewing merge requests.
- Comfortable adopting AI-assisted engineering practices across the SDLC — from spec-to-code generation and automated test coverage to AI-driven MR review and crash/issue triage — and continuously improving how the team uses these tools.