SDET Engineer

Role & responsibilities

• End-to-End Functional & Integration Testing: Design, execute, and maintain test scenarios for complex web and native mobile applications (iOS & Android).
• Deep System & API Debugging: Inspect and validate REST APIs using Postman and cURL. Analyze HTTP response codes, request/response payloads, and header configurations to pinpoint broken workflows.
• Mobile App & Device Profiling: Use tools like Android Studio (Logcat) and Xcode to extract live application logs, capture crash reports, and identify front-end vs. back-end issue ownership.
• Database & Data Validation: Execute SQL queries and validate backend database states to verify data integrity across user workflows, transaction states, and background processing.
• Failure & Root-Cause Analysis (RCA): Perform structured failure analysis when bugs occurdistinguishing between client-side bugs, backend service failures, data mismatches, and network drops.
• AI-Assisted QA Workflows: Leverage AI tools (e.g., ChatGPT, Claude, GitHub Copilot, or specialized test generation tools) to accelerate test case writing, generate edge-case scenarios, draft mock payload data, and troubleshoot error stack traces.
• Cross-Functional Communication: Articulate technical findings, defect impact, and reproduction steps clearly to developers, product managers, and engineering leads.

Preferred candidate profile

• Experience: 2+ years of professional hands-on experience in mobile app (iOS/Android) and web application testing.
• Technical & API Debugging: Proficiency with Postman, extracting/modifying cURL commands, reading JSON/XML payloads, and interpreting HTTP status codes ($2xx$, $4xx$, $5xx$).
• Mobile Tooling: Practical experience using Android Studio and Xcode for log inspection, build installation, and crash debugging.
• Architecture Understanding: Solid understanding of multi-tier client-server architectures, microservices interactions, and database structures.
• Database Knowledge: Ability to write basic-to-intermediate SQL queries for data verification.
• AI Tooling Savviness: Demonstrated ability to incorporate AI prompts and workflows into day-to-day testing (e.g., rapid test scenario generation, log parsing, or writing synthetic test data).
• Communication & Ownership: Excellent technical communication skills with a track record of driving defects to resolution alongside engineering teams.