Principal Software Engineer Job at IQbusiness
by IQbusiness
in
Job role overview
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Date posted
September 8, 2026
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Closing date
October 8, 2026
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Hiring location
South Africa
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Qualification
None
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Experience
None
Description
Job Description
- iqx the Technology Division of iqbusiness is looking for an exceptional Principal Software Engineer to join our Product Engineering team.
- This is a hands-on, high-impact role for a senior engineer who enjoys solving complex business challenges, influencing technical direction, and owning the full software delivery lifecycle.
- You will work closely with product managers, designers, and engineers to build secure, scalable, and client-focused solutions used by leading wealth and asset management firms globally.
- Operating within a regulated fintech environment, you'll have the opportunity to shape architecture decisions, improve engineering standards, and drive innovation through modern AI-assisted engineering practices.
Required Skills and Experience
- Extensive software engineering experience operating at Senior, Staff, Lead, Principal, or equivalent level.
- Proven track record of delivering production-ready software from ambiguous requirements through to successful adoption.
Strong commercial experience with:
- TypeScript
- JavaScript
- React
- Node.js
- SQL and relational databases
- Hands-on cloud experience with AWS or similar cloud platforms.
- Strong architecture and systems design capabilities.
- Experience with deployment, observability, and production troubleshooting.
- Excellent stakeholder management and communication skills.
- Strong ownership mindset with a passion for quality and delivery excellence.
- Knowledge of secure software development practices, accessibility standards, and compliance-driven environments.
AI-First Engineering
We're looking for engineers who have embedded AI into their development workflow. Successful candidates will be able to demonstrate:
- Practical use of AI tools throughout the software delivery lifecycle.
- Strong prompt engineering and AI-assisted development practices.
- Critical evaluation and validation of AI-generated output.
- Awareness of AI limitations, risks, and failure modes.
- Real-world examples where AI has delivered measurable improvements in productivity or quality.
How to Apply
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