Director, CX Data and Service Analytics

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How You Can Make a Difference

The Director, CX Analytics & Reporting is the analytics leader for the Service organization, responsible for helping Service leadership understand, explore, define, and deliver scalable analytics and enterprise reporting that enables effective performance management and continuous improvement in member experience.

This role is both a strategic partner and a hands-on analytics leader. The Director works closely with Service leadership to translate business priorities into measurable outcomes, operating metrics, reporting products, and analytic solutions that improve visibility, accountability, decision quality, and action.

The Director also partners with Data Engineering, Data Governance, Data Science, AI, and Technology teams to build durable semantic models, governed reporting assets, and reusable data products that support Service analytics at scale, including gold-layer data assets in Databricks.

This is a hands-on leadership role. "hands-on" may be needed to advise analysts in their code, decision logic, or optimizing performance. The Director is expected to be credible in the work, not only accountable for managing the work.

• Manages and carries out personnel actions for direct reports, including hiring, scheduling, coaching, training, performance management, compensation recommendations, and corrective or disciplinary action as appropriate.
• Builds, leads, and develops a high-performing analytics and reporting team that combines business partnership, technical depth, curiosity, accountability, and measurable impact.
• Creates role clarity, delivery standards, operating rhythms, and development plans that improve team execution, stakeholder trust, and analytics maturity.

• Serve as the primary analytics and reporting partner to Service leadership, helping leaders define performance questions, member experience measures, operating metrics, and decision needs.
• Own the Service analytics roadmap, balancing executive priorities, operational reporting needs, regulatory and risk considerations, automation opportunities, and measurable business value.
• Develop scalable analytics, dashboards, scorecards, and enterprise reporting that help the Service organization monitor performance, identify risk, evaluate trends, and improve member experience.
• Establish intake, prioritization, and delivery practices that focus the team on the highest-value work while communicating tradeoffs in a supportive, transparent, and business-oriented manner.
• Negotiate competing priorities with Service stakeholders based on value, urgency, reuse potential, risk, effort, and strategic alignment, including the ability to say not right now without damaging trusted partnership.
• Distinguish between ad hoc analysis, repeatable operational reporting, certified analytics, and scalable data products; ensure valuable ad hoc work is converted into operational analytics, semantic models, and engineering-backed data assets when appropriate.
• Partner with Data Engineering and Technology teams to design, sponsor, and mature semantic models that enable governed, consistent, and performant analytics and reporting at scale.
• Partner with Data Governance to improve metric definitions, data quality, lineage, stewardship, certification, and trust in Service reporting and member experience analytics.
• Provide hands-on guidance to analysts on SQL, Python, Databricks notebooks, dashboard logic, business rules, data validation, query performance, and reusable analytic patterns.
• Use CX platforms, survey data, call center metrics, agent performance data, CSAT, NPS, contact drivers, quality signals, and operational data to identify trends, root causes, opportunities, and risks.
• Apply data science fluency to increase pattern recognition, segmentation, driver analysis, prediction modeling, forecasting, anomaly detection, and proactive performance management in Service.
• Partner with Data Science and AI teams to identify, prioritize, and operationalize advanced analytics and AI-enabled use cases, including agentic AI capabilities built against governed semantic models and Databricks gold-layer data assets.
• Drive report rationalization, automation, dashboard performance improvement, self-service enablement, and retirement of low-value or duplicative reporting assets.
• Translate complex…