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.

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