Director, QA Engineering

Job description

# **Who are we?** At Finastra, we’re a global leader in financial services software, dedicated to expanding access to financial services and shaping what’s next for the industry. Our technology powers mission‑critical solutions across Lending, Payments and Universal Banking, supporting over 7,000 customers, including 80% of the world’s top 50 banks, in more than 110 countries. **What will you contribute?** Reporting to the **VP, Head of Payments Quality Engineering**, the **Director, QA Engineering** will lead a global, cross-functional Quality Engineering organization responsible for **quality strategy, intelligent test automation, and end-to-end validation** of the Payment To Go (P2G), Financial Messaging (FM) & US Payments. This role partners closely with the **Director, R&D Development** to jointly ensure **high-quality, scalable, secure, and resilient software delivery** through a **shift-left, AI-first, and automation-driven engineering approach**. The role is accountable for **embedding quality across the SDLC**, modernizing QA into **Quality Engineering**, and driving **predictable, metrics-driven releases with superior customer outcomes**. **Quality Engineering Leadership & Transformation** - Define and drive the **enterprise-wide Quality Engineering strategy** aligned with engineering, AI, and cloud modernization goals - Lead the transformation from **reactive QA to proactive Quality Engineering**, embedding quality early in design and development cycles. - Champion **AI-augmented Quality Engineering practices**, including: - AI-driven test generation & optimization - Intelligent defect prediction & root cause analysis - Autonomous test execution & self-healing frameworks - Establish a **quality-first engineering culture** across Development, QA, DevOps, and Product teams. - Ensure adoption of **shift-left and shift-right testing strategies**, covering functional, non-functional, and production validation. **Strategy, Governance & Metrics** - Define and institutionalize **quality frameworks, standards, and governance models** across all product lines. - Establish and monitor **engineering quality KPIs**, including: - Defect density & leakage - Release success rate & deployment stability - Test coverage (functional, performance, security) - MTTR and incident escape rates - Drive **data-driven decision making** through real-time dashboards and quality insights. - Ensure alignment with enterprise quality policies and regulatory/compliance requirements. **Technology, Automation & Architecture** - Lead the design and execution of **scalable test architecture** for: - Functional, integration, regression testing - Performance, scalability, and resilience testing - Security and compliance testing - Partner with Development to embed **testability into system design** and ensure **quality gates in CI/CD pipelines**. - Drive **100% automated regression (functional & non-functional)** to enable **release-on-demand capabilities** - Standardize tools and frameworks across teams (e.g., Selenium, JMeter, Postman, CI/CD toolchains). - Support modernization initiatives including: - Cloud-native architecture - Microservices-based platforms - ISO20022 and real-time payments transformations **Execution & Delivery Excellence** - Co-own delivery outcomes with R&D Development, ensuring: - **On-time, high-quality releases** - Reduced rework and production defects - Improved customer satisfaction - Drive measurable improvements in: - Defect leakage - Release cycle time - Release stability and deployment success rates - Ensure **fully stable nightly automation packs** with high pass rates to support continuous integration and delivery. - Lead **quality assurance for both legacy platforms and modern cloud-native systems**. **AI Driven Quality & Innovation** - Partner with the Development organization to embed AI across the SDLC, ensuring aligned adoption across build and test functions. - Drive AI-enabled quality engineering use cases, such as: - Predictive defect analytics - Test optimization using ML models - Intelligent production monitoring - Establish best practices for test data management, observability, and quality analytics. **Leadership & Talent Development** - Build, mentor, and scale **high-performing global QA engineering teams** across geographies. - Drive a culture of: - Accountability and ownership - Continuous improvement - Engineering excellence - Develop strong leadership bench strength and succession pipeline within QA Engineering. - Lead vendor and partner ecosystems effectively. **Stakeholder Management & Collaboration** - Act as a **trusted partner to Director, R&D Development**, ensuring seamless collaboration across SDLC. - Work closely with: - Product Management - Architecture & DevOps - Customer Support & Services - Influence cross-functional stakeholders to **prioritize quality as a strategic differentiator**. - Provide **transparent executive-level reporting** on quality metrics, risks, and outcomes. **Key Challenges** - Fragmented tooling and inconsistent quality practices across teams. - Balancing **legacy system quality with modernization velocity**. - Scaling **automation and AI adoption across large distributed teams**. - Driving accountability for quality across engineering (not just QA). **Required Qualifications & Experience** **Education & Technical Background** - BS/MS in Computer Science, Engineering, or related field **Leadership Experience** - 15+ years in QA/Engineering leadership roles - Experience managing large global teams (50 upto100) across geographies **Quality Engineering Expertise** - Deep expertise in: - Test automation frameworks - Performance and resilience testing - CI/CD and DevOps integration - Defect management and quality governance - Strong understanding of Agile, DevOps, and cloud-native architectures **AI & Innovation (Preferred)** - Experience driving AI/ML adop