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The Client Engagement Data Analyst - AI sits at the intersection of Client Engagement operations, data infrastructure, and AI capability. This role exists to ensure that the data signals flowing through our support systems are clean, well-structured, and ready to power AI-driven insights and automation.
But this is more than a data role. We are looking for someone who has actually orchestrated AI to solve real business problems, not just someone who uses AI tools, but someone who has built the systems, data foundations, and governance frameworks that allow AI to make good decisions at scale. In most organizations, the bottleneck isn't the AI model, it's the quality and structure of the data feeding it. This person knows how to remove that bottleneck.
Working closely with the Process Optimization & Automation Team, you will co-own the logic and governance that shapes how support data is structured, tagged, and interpreted and translate that structure into meaningful reporting that drives decisions across Client Engagement and beyond.
This is a role for someone who is equally comfortable designing a data model and presenting a dashboard to a senior stakeholder. You bridge the gap between operational reality and the data architecture needed to make AI work well on top of it. Just as importantly, you bring others along with you — building confidence and capability across the team as AI becomes embedded in how we all work.
Design and coordinate AI agent workflows that solve real operational problems, going beyond off-the-shelf tools to build systems that actually drive outcomes
Identify where AI can deliver the most impact within CE operations and take ownership of making those use cases work end-to-end
Establish the data foundations, tagging logic, and system architecture that allow AI to make accurate, reliable decisions
Act as the team's first mover — testing, applying, and refining new AI capabilities before they are rolled out broadly
Design and maintain data models that support AI tagging, classification, and reporting across Client Engagement systems
Audit and optimize data tables in Zendesk and connected platforms to ensure they are structured for AI consumption
Define how support data categories, fields, and hierarchies map to desired AI output — working closely with the AI layer owner to ensure inputs are clean, consistent, and model-ready
Identify gaps between current data structure and what is needed to unlock new AI capabilities
Build and maintain dashboards and reports that surface AI performance signals, support trends, and operational health metrics
Develop the Echo Report and other AI-driven reporting outputs that translate raw data into clear client and operational insights
Monitor the feedback loop between AI outputs and human behavior — tracking when specialists accept, edit, or override AI-suggested categories, and what that tells us about model efficacy
Proactively identify emerging patterns in support data and connect them back to reporting and signal strategy
Partner closely with senior AI and engineering leaders — learning from them, contributing to what's being built, and being the first to apply new capabilities within CE
Act as the connective layer between CE operations, the AI/automation team, and engineering — translating operational data needs into clear technical requirements
Champion AI literacy across the CE team — teaching, enabling, and building confidence so that AI becomes part of how everyone works, not just a specialist function
Communicate data model changes, tagging updates, and reporting outputs to non-technical stakeholders in plain language
Contribute to backlog prioritization by sizing the data readiness requirements of proposed AI and automation initiatives
3–5 years of experience in a data analyst, operations analyst, or similar role — ideally within fintech, crypto, or high-volume customer operations
Demonstrated experience coordinating AI agents (OpenClaw, LangChain, CrewAI) or building AI-powered workflows to solve real business problems not just prompting tools, but designing systems that drive outcomes
A proven track record of setting up the data foundations, structures, and governance that allow AI to actually work — understanding that the blocker is rarely the model, it's the data feeding it
Strong SQL skills and comfort querying and transforming data across complex systems
Hands-on experience with Zendesk or a comparable CX/case management platform — understanding of data structures, ticket fields, tags, and reporting
Proficiency with BI tools such as Looker, Mixpanel, or similar — able to build and own dashboards independently
Experience designing or contributing to data taxonomy, tagging frameworks, or classification systems
Ability to translate between operational language and technical/data requirements — comfortable working with both frontline teams and engineers
Familiarity with how AI/ML systems consume structured data — understanding of what "model-ready" data looks like in practice
A genuine desire to teach and uplift others — this person raises the AI capability of everyone around them
Exposure to AI tagging pipelines, LLM-based classification, or RAG-based knowledge systems
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