Senior Quality Analyst

SimplifyNext; Singapore; On-site; SGD 150,000 - 210,000; Full time

Job summary

SimplifyNext is seeking a Senior Quality Analyst to lead quality strategy across complex programmes, partnering with architects, engineers and client stakeholders to embed quality from day one. You will drive automation-first testing, governance, and AI-enabled approaches to ensure observable, resilient systems.

In this role you will shape testing across UI, API, data and integrations, mentor junior QAs, and measure delivery health with metrics such as lead time, deployment frequency and MTTR.

Qualifications

• Strong experience in quality engineering within agile, product-centric environments.
• Ability to define and implement automation strategies across complex systems.
• Hands-on with CI/CD integration and modern test frameworks.
• Familiarity with cloud-native architectures and AI/ML testing.

Responsibilities

• Define and lead end-to-end quality strategies aligned with business outcomes.
• Champion shift-left quality and embed testing into design, development, and deployment.
• Lead automation across UI, API, data, and integrations.
• Define and track quality metrics to improve delivery predictability.
• Coach teams and contribute to building SimplifyNext’s Quality Engineering practice.

Skills

Quality engineering

Automation strategy

CI/CD

AI testing

Cloud-native

Performance testing

Tools

Playwright

Cypress

Selenium

Job description

At SimplifyNext, quality is not a phase – it is a discipline woven into every stage of how we deliver. As a Senior Quality Analyst, you will play a leadership role in shaping how quality is defined, engineered, and scaled across our engagements. You will work at the intersection of modern application development, AI-driven solutions, and enterprise automation, partnering with architects, engineers, and client stakeholders to ensure quality is built into the foundation – not tested at the end. This role goes beyond traditional QA. You will lead quality transformation for our clients, drive automation-first testing strategies, and explore how AI can redefine how quality is assured in complex, real-world systems.

Responsibilities

Quality Leadership & Strategy

• Define and lead end-to-end quality strategies for complex programmes, ensuring alignment with business outcomes, regulatory requirements, and modern architecture patterns.
• Act as a trusted advisor to clients and senior stakeholders, shaping how quality is approached across teams, platforms, and releases.
• Establish quality governance frameworks across multi-workstream programmes, including standards, metrics, and reporting structures.
• Champion a shift-left and quality engineering mindset, embedding testing into design, development, and deployment practices.
• Partner with architects and developers to design for testability, ensuring systems are observable, resilient, and scalable.
• Advocate and guide teams on practices such as TDD, BDD, contract testing, and CI/CD-driven quality gates.

Automation & AI-Driven Testing

• Lead the design and implementation of automation-first testing strategies across UI, API, data, and integration layers.
• Drive adoption of modern test automation frameworks such as Playwright, Cypress, and Selenium, and ensure they are scalable, maintainable, and integrated into delivery pipelines.
• Explore and implement AI-assisted testing approaches – including test generation, intelligent test selection, anomaly detection, and self-healing automation.
• Define approaches for testing AI/ML systems and agentic workflows, including handling non-deterministic behaviour, model drift, and data quality risks.

End-to-End Quality Assurance

• Own quality across the full technology stack – from frontend experiences to backend services, integrations, and cloud infrastructure.
• Lead exploratory testing and risk-based validation, especially in complex, automation-heavy, or AI-driven solutions.
• Ensure robustness across performance, security, reliability, and user experience dimensions.

Metrics, Insights & Continuous Improvement

• Define and track engineering and quality metrics (e.g. lead time, deployment frequency, change fail rate, MTTR).
• Use data to identify systemic issues, improve delivery predictability, and elevate team performance.
• Embed continuous feedback loops from production usage into test strategy and product improvements.

Coaching & Practice Building

• Mentor and guide junior QAs and engineers, raising the overall quality maturity of teams.
• Coach client teams to adopt shared ownership of quality, ensuring sustainability beyond project delivery.
• Contribute to building SimplifyNext’s Quality Engineering practice, including reusable assets, accelerators, and thought leadership.

What We Are Looking For

Technical Skills

• Strong experience in quality engineering within agile, product-centric environments, working closely with developers, BAs, and architects.
• Proven ability to define and implement automation strategies across complex systems (web, mobile, API, integrations).
• Hands-on experience with modern test frameworks and CI/CD integration.
• Solid understanding of cloud-native architectures, microservices, and distributed systems.
• Experience testing AI/ML or automation solutions, with awareness of challenges such as non-determinism, data dependency, and model evaluation.
• Familiarity with performance, security, and resilience testing approaches.
• Experience working in large-scale or public sector programmes is a plus.

Professional & Leadership Skills

• Demonstrated ability to lead quality across teams and influence without authority.
• Strong consulting mindset – able to engage clients, shape thinking, and communicate risks in business terms.
• Ability to operate at both strategic and hands‑on levels, switching seamlessly as needed.
• Passion for innovation in quality engineering, particularly through automation and AI.
• Committed to continuous learning and driving change in how quality is delivered.

What Success Looks Like in This Role

• Quality is embedded from day one, not introduced late in delivery
• Automation coverage is meaningful, reliable, and accelerates releases
• Teams adopt shared ownership of quality
• Clients see measurable improvements in delivery metrics and stabilityNew approaches (AI-assisted testing, modern frameworks) are successfully piloted and scaled