About the Role
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.
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.
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.
- 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.
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 stability
- New approaches (AI-assisted testing, modern frameworks) are successfully piloted and scaled
