Senior Data Analyst, Customer Operations

About the Role

As the first Senior Data Analyst embedded in Customer Operations, you will build the CS Analytics function and own analytics and operational insights for the department. You will partner closely with Customer Operations leadership, Product, Finance (Rev Ops), and Data Engineering to build trusted reporting, improve operational rhythms, and drive measurable outcomes across retention, expansion, customer health, and support efficiency.

• Own Customer Success and Customer Operations measurement
  • Define and maintain core metrics and business definitions across the customer lifecycle, including onboarding milestones, time-to-value, engagement, customer health, renewals, expansions, and churn.
  • Create clear documentation and enable consistent interpretation across Customer Success Operations, Rev Ops, Finance.
  • Establish instrumentation and data quality requirements with Data Engineering to ensure reliable sources of truth.
• Build decision‑ready reporting and self‑serve analytics
  • Build and iterate on dashboards, KPI scorecards, and operational reporting that support day‑to‑day execution and executive visibility.
  • Enable self‑serve analytics with clear definitions, drill paths, and actionable views for CS leaders, managers, and operators.
  • Create automated reporting and proactive alerting for KPI movement and risk signals, such as drops in engagement, support spikes, onboarding delays, and renewal risk.
• Customer retention, churn, and expansion analytics
  • Define and maintain retention metrics, including logo and revenue churn, GRR and NRR, renewal rates, and cohort retention.
  • Build and operationalize churn and renewal risk analyses and models that surface leading indicators.
  • Develop and iterate on customer health scoring frameworks that combine usage, lifecycle events, support signals, billing signals, and qualitative inputs.
• Forecasting and capacity planning
  • Build forecasting models for key planning needs such as renewal volume, renewal risk, churn, expansion pipeline, ticket volume, and staffing capacity for our BPO partner.
  • Define evaluation approaches such as backtesting, holdouts, calibration, and monitoring, and ensure forecasts remain reliable over time.
  • Partner with Customer Operations to translate forecasts into staffing plans, coverage models, and operating cadences.
• AI‑enabled automation and productivity
  • Identify and prototype AI‑driven workflows that reduce manual analysis and speed up decision‑making, such as automated insights, narrative summaries, anomaly detection triage, and stakeholder Q&A.
  • Define success metrics and guardrails for AI‑supported analytics, including accuracy, coverage, bias considerations, data privacy, and appropriate human review.
  • Drive adoption through enablement, feedback loops, and iteration with cross‑functional partners.
• Cross‑functional partnership and storytelling
  • Translate Customer Success Operations questions into structured analyses and measurable hypotheses.
  • Communicate insights with clear narratives that influence decisions across technical and non‑technical audiences.
  • Build strong relationships with CS, Customer Ops, Rev Ops, Finance, Support, and Data teams to align priorities and execute effectively.

• 4+ years of experience in analytics, business operations, or business intelligence roles, ideally supporting Customer Success, Customer Operations, Rev Ops, Support, Sales, Growth, or similar customer‑facing functions.
• Experience working in a B2C subscription or membership‑based business with familiarity with subscription metrics like LTV, churn, refund rate, and renewal rates.
• Strong SQL skills and experience working with analytical datasets and BI tools (Looker, Tableau, etc.) with an emphasis on performance, usability, and metric governance.
• Comfortable working within an existing Databricks environment, reading gold‑layer schemas, running queries, and collaborating with Data Engineering.
• Experience with Python (or similar) for analysis, forecasting, and modeling.
• A track record of building retention, churn, renewal risk, forecasting, or related analyses and translating outputs into business action.
• Strong foundation in statistics and…