Senior Director, Engineering Quality & Customer Experience

Achievement Areas

• AI-driven Quality Transformation & Continuous Delivery
• Automation Platform & Test Architecture
• Customer Journey Quality & Segmentation
• Production Observability & Organizational Leadership

Key Responsibilities

Lead the enterprise shift to AI-native quality engineering focusing on automation-first strategies, agentic test generation, and LLM-assisted validation that expands test coverage at scale.

Drive the release model from bi-weekly cadence to continuous deployment by embedding CI/CD quality gates, intelligent test selection, and release confidence scoring that enable safe, frequent production delivery.

Establish the AI Quality Engineering Center of Excellence: standards, patterns, adoption frameworks, and tooling strategy (select, standardize, retire) so product managers, designers, and engineers can execute quality simply and consistently.

Partner with Product, Engineering, Architecture, and Delivery to embed quality as a system-level capability as the SDLC evolves toward agentic AIDLC workflows.

Use AI-driven insights to identify defect patterns, coverage gaps, and automation priorities.

Develop agentic test harnesses with AI-generated mock data to accelerate and scale testing for lower environments and customer UAT.

Automation Platform & Test Architecture

Own the AI-powered quality platform and roadmap focusing on agentic test generation, Graph RAG context-aware testing, customer configuration-aware test packs, and multi-stack support tooling (.NET, Java, Angular, PHP).

Define enterprise test architecture standards (unit through E2E), automation frameworks, CI/CD gates, and flakiness reduction for critical payment flows and integrations.

Enable SDETs, engineers, QAs, and domain SMEs to contribute automation through Terminal and CLI workflows using agents, skills, rules, and hooks that increase coverage, reduce test execution time, improve detection and root cause analysis.

Establish data-driven quality signals. Partner with operational teams to capture automation health, defect escape trends, release readiness evidence and compliance best practices with auditability, traceability, and requirements-to-test coverage for regulated payment systems.

Develop standards and release certification criteria for AI-generated code and automated test assets, including validation required before promotion of release candidates to production.

Ensure production issues directly influence automation investment, quality gates, and test design.

Customer Journey Quality & Segmentation

Own quality across onboarding, implementation, upgrades, and support; partner with Delivery, Implementation, and Customer Success on transparent gates, SLAs, and customer-visible release confidence.

Move from generic regression to journey-based, carrier-profile-driven coverage using configuration segmentation, production data, NPS insights, and support ticket themes to prioritize test investment.

Partner with Delivery teams to define performance requirements and SLAs, communicate test results and coverage metrics, and proactively mitigate risks to support successful customer go-lives and upgrade stability.

Partner with Product and Delivery to prioritize testing based on customer usage patterns, production data payload samples, and other risk exposure.

Support customer test automation through shared frameworks and reusable, configuration-aware test packs.

Close the loop by measuring improvements against aging ticket volume, NPS, and defect escape trends.

Partner with the product team to analyze drop-off analytics and top consumer complaints to inform journey-based quality priorities and defect prevention.

Provide customer-visible release confidence signals for implementations, upgrades, and production releases.

Production Observability & Organizational Leadership

Align quality engineering with business outcomes (payments reliability, uptime, accuracy), regulatory and compliance requirements, and customer experience expectations.

Connect test results, CI/CD signals, production incidents, and customer-reported issues into end-to-end quality observability; use AI-driven insights to identify defect patterns, coverage gaps, and automation priorities.

Drive incident feedback loops into regression coverage, segmentation updates, and quality gate enhancements so production issues directly shape SDET and automation investment.

Lead and develop a multi-disciplinary organization of AI platform engineers, SDETs, QA engineers, and automation specialists; own quality metrics and executive reporting on release confidence and transformation progress.

Partner across Engineering, Product, Architecture, Delivery, Support, IT Operations and Compliance to align quality practices with customer success, payment reliability, and regulatory requirements.

Hire, mentor, and develop the team on modern quality engineering, platform thinking, and AI-enabled workflows.