Quality Analyst

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. 

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. 

Shift-Left & Engineering Excellence 

• 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. 

• Make informed decisions on tooling strategy (e.g. what to use for modern web apps vs legacy or cross-browser needs). 

• 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. 

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. 

• Excellent stakeholder management skills, including senior leadership engagement. 

• Passion for innovation in quality engineering, particularly through automation and AI. 

• Committed to continuous learning and driving change in how quality is delivered. 

• 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 stability 

• New approaches (AI-assisted testing, modern frameworks) are successfully piloted and scaled