About the role
We’re looking for a Customer Support Systems & Analytics Lead to own the data strategy and reporting function within the Customer Support organisation at Mercury. As our operations grow in complexity—spanning multiple channels, an expanding BPO model, and sophisticated AI automation—we are moving away from fragmented, project-based data support toward a model of holistic ownership. In this role, you will be responsible for how we capture, structure, and connect data across our CS systems - including Zendesk and our AI Agent - to drive informed decision-making across
the team. You will ensure our systems are configured in a way that produces reliable, high-quality data, and you will translate that data into actionable insights for CS leadership and stakeholders. You’ll sit at the intersection of data, operations, and customer experience, partnering closely with CS leadership, Strategy and Ops, and cross-functional teams like Data to make sure our reporting infrastructure is built to scale. Your work will be foundational to how the CS organisation measures performance, identifies opportunities, and drives decision-making. Key
Responsibilities Unified reporting and analytics: Actively build and maintain reports and dashboards that give the CS organisation clear visibility into key performance metrics, trends, and performance across channels, teams and projects - from our internal Support team to our BPO partners. CS Systems configuration and data governance: Work closely with systems admins to advise on the structural setup of our Zendesk instance across customer-facing teams, ensuring it is configured to generate clean, consistent, and reliable data while maintaining secure systems and efficient workflows. Data hygiene standards: Define and enforce standards for how data is captured across CS systems so that reporting is accurate, trustworthy, and reproducible AI automation data oversight: Monitor and analyse data from our AI chatbot, ensuring it is tracked in a way that supports quality reviews and decision-making across the CS organisation Insights to action: Translate raw data and reporting into clear recommendations that help CS leadership make decisions on resourcing, tooling, process improvements, and strategy Stakeholder support: Partner with cross-functional partners in areas like Product, Data and Strategic Finance to understand their data needs and deliver reporting that supports their goals Systems evaluation: Assess the data capabilities of existing and new CS tools, and make recommendations on how to optimise our systems stack for better reporting outcomes Process documentation: Document data structures, reporting methodologies, and system configurations to ensure institutional knowledge is retained and accessible across
