Support Operations Data Analyst

What You'll Do

• Own recurring reporting for User Operations — weekly, monthly, and QBR-ready — tailored to ops, leadership, and cross-functional audiences
• Translate support data into clear narratives: what's happening, why, and what to do about it
• Track and maintain north star metrics: cSAT, TTR by tier, QA scores, bug escalation rate to EPD, and First Response Time
• Build and maintain self-serve dashboards that give the ops team and leadership real-time visibility into support performance
• Partner with Support Systems to ensure Zendesk is instrumented to capture the data we need
• Work with Harvey's central data team to connect support data to broader product and customer data sources
• Identify and close data collection gaps — if we can't measure it, help define how we should
• Design feedback loops that connect support signals to Product, Engineering, and Customer Success
• Quantify the operational cost of product bugs, feature gaps, and onboarding failures
• Contribute to QA analytics as the QA program matures
• Track ticket deflection, AI/chatbot performance, and self-service effectiveness
• Measure the impact of AI-driven support — containment rate, escalation rate from AI interactions, resolution quality — and surface findings that drive how we tune and invest in those tools
• Support ad hoc analytical requests from the Support Operations Manager, User Operations leadership, and senior stakeholders

What You Have

Required

• 3–5 years of experience in analytics, with at least 2 years directly in support operations, customer success operations, or a closely adjacent function
• Fluency in support platform data — you know how Zendesk (or equivalent) is structured, what data it produces, and what it doesn't
• SQL proficiency — you can write complex queries against large datasets without hand-holding (CTEs, window functions, joins across schemas)
• Dashboard experience — you've built and maintained operational dashboards in Looker, Tableau, Sigma, Omni, or equivalent
• Reporting for multiple audiences — you know the difference between what a frontline manager needs and what a CFO needs, and you build accordingly
• Strong data storytelling — you don't just present numbers, you write the narrative
• Comfort operating solo — you don't need a team around you to deliver, and you don't need a ticket to tell you what to look at

Strong Plus

• Experience with Python for data manipulation or automation
• Familiarity with dbt or similar data transformation tooling
• Experience building or contributing to QA analytics programs
• Background supporting enterprise SaaS or AI-native products
• Experience working with Zendesk APIs or extracting data beyond standard reporting

Key Attributes

• AI-native: you use AI tooling actively in your analytical workflows — not as a novelty, but as a force multiplier
• Pace: you move in hours and days, not weeks. You surface findings before anyone has to ask
• Judgment: you know which metrics matter and which are vanity. You push back when framing is wrong
• Clarity: your outputs are direct, jargon-free, and actionable. You write for the reader, not yourself
• Ownership: you treat User Operations analytics as your problem to solve, not a ticket queue to process