Director, Quality Engineering

Mimecast is a leading cybersecurity company that helps organizations protect their people, data, and communications from today's most sophisticated threats. The Director of Quality Engineering & Customer Operations will be responsible for overseeing customer escalation management, incident response coordination, and quality assurance across product delivery, ensuring exceptional customer experiences and driving engineering improvements.

Responsibilities:

• Own the Customer Escalation Lifecycle: Take full accountability for every customer-raised defect from the moment it is logged to the moment it is resolved and formally closed with the customer. No escalation falls into a gap between teams, and no customer waits without knowing where things stand
• Make Every Interaction an Opportunity: Define and enforce communication standards that go beyond accuracy and timeliness. Every customer touchpoint should leave the customer feeling heard, respected, and confident in Mimecast. Set this standard across the team and hold it consistently, especially in high-pressure situations
• Build the Escalation Feedback Loop: Establish a structured process for turning customer escalation insight into engineering improvement. Work with pillar engineering leads to ensure that recurring escalation patterns translate into backlog investment, not workaround documentation
• Define Escalation SLAs and Measure Against Them: Design SLAs for escalation response and resolution that are genuinely focused on reducing customer pain and improving customer experience, not optimising team throughput. Track them rigorously, report them transparently, and drive improvement over time
• Partner with Customer Support: Collaborate proactively with the customer support organisation. Pull insight from support channels rather than waiting for issues to be escalated. Build the relationship and the shared process that ensures support and engineering act as one team when it matters
• Lead Engineering Incident Response: Own the engineering-side coordination of P1 and P2 incidents across Mimecast. Ensure that the right people are engaged, that communication is clear and timely, and that engineering teams operate with urgency and structure rather than reactively
• Drive Root Cause Analysis and Post-Incident Review: Establish a rigorous and honest RCA process. Ensure that every significant incident produces clear findings, accountable actions, and visible follow-through. The goal is fewer incidents over time, not better incident reports
• Build and Maintain Incident Playbooks: Define and continuously improve incident response playbooks across the major failure scenarios. Ensure the organisation knows how to respond before an incident happens, not during it
• Track Incident Trends and Report to Leadership: Maintain visibility of incident frequency, severity, and resolution trends. Report to VP and C-suite level with clarity, including honest assessment of where engineering reliability needs investment
• Own Quality Engineering Strategy: Define and execute the quality engineering strategy across all Mimecast product lines, treating quality as a shared engineering discipline rather than a gate at the end of the cycle. Establish quality standards, testing frameworks, and coverage expectations that are embedded into how teams build from day one
• Drive Quality Gates in the PDLC: Implement quality gates and standards across the product development lifecycle, working closely with engineering pillar leads and product management. Ensure that system, platform, and performance testing are planned, resourced, and executed consistently across all major releases
• Own Defect Metrics and Visibility: Define and track the right KPIs for product quality. Measure defect escape rates, field escape rates, and defect resolution velocity. Use data to identify where quality is weakest, communicate findings to engineering leadership, and drive investment in the right areas
• Champion AI-Augmented Quality: Lead the adoption of AI-powered quality engineering practices across the organisation, including AI-driven test generation, automated regression risk scoring, and AI-assisted failure analysis. Drive measurable improvements in test coverage and automation velocity
• Lead a Distributed Team: Build, mentor, and lead a distributed quality engineering and customer operations team across the US, UK, South Africa, and India. Set clear expectations, develop leaders within the team, and create a culture of ownership and accountability
• Collaborate Across Engineering: Work closely with engineering pillar leads, product management, architecture, and SRE teams. You will not own all the engineers who touch quality, but you will influence how all of them think about it. Build credibility through results, not authority
• Report to Leadership and Influence Roadmaps: Bring quality and customer operations insight into engineering leadership discussions. Translate data into clear investment cases. Influence engineering roadmaps to address quality and reliability gaps alongside feature delivery

Requirements:

• 15+ years of quality engineering experience with progressive leadership responsibilities across complex, multi-product environments
• 7+ years in engineering management, leading QE teams of 20 or more across onshore and offshore functions
• Proven track record managing customer escalations directly, including high-stakes accounts and executive-level customer engagement
• Experience owning or closely partnering on incident management, release management, or SRE functions in a SaaS environment
• Strong understanding of CI/CD integration, DevOps quality gates, and modern test automation frameworks at scale
• Demonstrated ability to define and implement quality KPIs that drive real engineering improvement rather than reporting theatre
• Experience leading distributed teams across multiple geographies and time zones, with an effective and inclusive remote leadership approach
• SaaS product environment experience is required. Cybersecurity domain knowledge is helpful but not a prerequisite. Learning agility and curiosity about the domain matter more
• Hands-on experience adopting AI-powered quality engineering tools, including AI-assisted test generation, automated regression risk scoring, or AI-driven failure analysis, with measurable outcomes in coverage, velocity, or defect detection
• Ability to define and execute an AI adoption roadmap for quality engineering, establish best practices, and build a culture where AI is embedded in how the team works every day
• Practical understanding of where AI adds genuine value in QE and where it does not. We need thoughtful adoption, not AI for its own sake
• Customer first. You see every customer interaction as an opportunity to build trust and deliver a great experience. That instinct shapes how you lead the team, design process, and measure success
• Ownership orientation. You treat every unresolved customer issue as your problem until it is resolved. You do not wait to be told
• Bias for action. You make decisions with conviction and move fast rather than waiting for perfect information or perfect process
• Data-driven. You measure what matters and use evidence to drive improvement and prioritisation. You are equally comfortable with engineering metrics and customer experience data
• Transparent communicator. You share information openly, deliver difficult messages directly, and build trust through honesty. You communicate clearly to customers, to engineers, and to executives
• Change oriented. You challenge the status quo when the reason for doing things the current way is simply that it has always been done that way. Process should enable progress, not protect itself
• Collaborative. You build credibility across a large engineering organisation through results and relationships, not through authority