Senior QA Engineer (TradeZella product)
We're looking for a QA Engineer who cares about product quality end-to-end, someone who gets trading through real experience, professional or personal. Trading doesn't need to be your career; a hobby or a past chapter counts, but long-term investing alone doesn't.
You'll join TradeZella , our trading journaling and educational platform, and focus on how the product works in real conditions: testing functionality, investigating edge cases, and resolving complex issues together with engineering, product, and support teams. This role requires a genuine interest in the trading domain, along with strong UX testing skills.
What are we looking for?
Testing & QA expertise:- Strong experience with manual testing and end-to-end validation
- Solid UX testing skills beyond formal requirements (user acceptance testing, business acceptance testing, usability validation)
- Hands-on day trading experience (any market: stocks, forex, futures, crypto), past or present, professional or personal, with the ability to walk us through at least one strategy you've traded
- Experience in investigating customer issues and performing root cause analysis
- Experience in building and improving QA processes
Technical skills:- Strong debugging and problem-solving skills, including log analysis (CloudWatch, Logs Explorer)
- Good understanding of networks, HTTP, APIs, and web/client architectures
- Experience with version control systems (GitHub, GitLab, etc.)
- Experience using AI tools for workflow optimization
Communication & mindset:- Ability to translate technical concepts for different audiences (engineering ↔ non-technical)
- Strong sense of ownership and accountability
- Structured, detail-oriented approach
- Fluent English (written and spoken)
Would be a plus:- Experience with Playwright (TypeScript)
Your responsibilities
- Analyze requirements, test functionality, and identify defects, confusions, and edge cases
- Own investigation of complex customer issues through L3 support, driving root cause resolution
- Partner with engineering and product to resolve issues and improve system reliability
- Improve QA practices and elevate debugging and investigation standards, including AI adoption practices
- Support releases and ensure stable rollouts with active post-release follow-up
- Support and extend existing AI agents and skills to make the QA process more efficient
