Director, CX Data and Customer Analytics

The Director, CX Data & Customer Analytics is the analytics leader for the Customer Organization, responsible for helping CX leadership understand, explore, define, and deliver scalable analytics and enterprise reporting that enables effective performance management, business growth, operational excellence, and continuous improvement in customer experience.

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

This role leans heavily toward business and strategic impact. The Director serves as an enabler of decision-making for CX leadership, helping shape where the organization focuses and what it does next—not simply reporting on what has already happened. The role is accountable for influencing business outcomes such as customer retention and growth, cost to serve, operational effectiveness, and customer experience.

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 CX 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 performance optimization. The Director is expected to be credible in the work, not only accountable for managing the work.

• Serve as the primary analytics and insights partner to CX leadership, helping leaders define strategic questions, customer experience measures, operating metrics, and decision needs.
• Build a deep understanding of HealthEquity's business model and how client, member, broker, and partner experiences connect to business performance, retention, growth, and operational efficiency.
• Quantify the value at stake—including retention, revenue, cost to serve, operational risk, and customer experience impact—and use it to build business cases, guide investment decisions, and prioritize the CX analytics roadmap.
• Translate business priorities into measurable outcomes, analytics strategies, reporting products, and action plans that drive customer and business results.
• Own the CX analytics roadmap, aligning investments to customer priorities, value at stake, strategic business objectives, 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 Customer Organization monitor performance, identify risk, evaluate trends, and improve customer experience.
• Serve as a trusted advisor to CX leadership by translating complex analytics into clear business narratives, recommendations, and decision support for executive audiences.
• 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 stakeholders based on business value, urgency, reuse potential, risk, effort, and strategic alignment, including the ability to say “not right now” without damaging trusted partnerships.
• 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 CX reporting and customer experience analytics.
• Use CX platforms, survey data, call center metrics, agent performance data, CSAT, NPS, customer journey signals, contact drivers, quality measures, and operational data to identify trends, root causes, opportunities, and risks.
• Apply data science fluency to increase pattern recognition, segmentation, driver analysis, predictive modeling, forecasting, anomaly detection, and proactive performance management across the Customer Organization.
• 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.
• Measure analytics effectiveness through business outcomes influenced, decisions changed, stakeholder adoption, reporting quality, reduced manual effort, improved decision speed, customer experience improvements, and measurable business impact.
• Build, lead, and develop a high-performing analytics and reporting team that combines business partnership, technical depth, curiosity, accountability, and measurable impact.
• Create role clarity, delivery standards, operating rhythms, and development plans that improve team execution, stakeholder trust, and analytics maturity.
• Manage and carry out personnel actions for direct reports, including hiring, scheduling, coaching, training, performance management, compensation recommendations, and corrective or disciplinary action as appropriate.
• Provide hands-on guidance to analysts on SQL, Python, Databricks notebooks, dashboard logic, business rules, data validation, query performance, and reusable analytical patterns.

• Deep business acumen with a strong understanding of how customer experience influences retention, growth, customer lifetime value, operational efficiency, and cost to serve.
• Must be able to write SQL, Python, and Databricks notebooks/workbooks well enough to guide analysts, review logic, validate results, and advise on performance optimization.
• Deep understanding of analytics engineering concepts, semantic model design, dimensional modeling, metric definition, dashboard design, and scalable reporting patterns.
• Strong familiarity with customer experience and operational performance measures, including call center metrics, queue performance, handle time, first-contact resolution, abandonment, transfer rates, quality measures, CSAT, NPS, and customer experience trends.
• Experience with CX survey or Voice of Customer platforms such as Qualtrics or similar tools, and the ability to integrate survey signals with operational and behavioral data.
• Working knowledge of Databricks, lakehouse architecture, gold-layer data assets, BI platforms, governed metrics, and reusable data products.
• Data science understanding or hands-on experience in predictive modeling, classification, forecasting, segmentation, recommendation, anomaly detection, and pattern recognition.
• Understanding of agentic AI, AI assistants, and AI-enabled analytics patterns, including the use of governed semantic models as trusted sources for AI agents and decision-support tools.
• Ability to partner with engineering teams on data model requirements, performance considerations, reusable logic, testing, data quality, reliability, and production readiness.
• Demonstrated ability to influence senior leaders and stakeholders without direct authority, using data, insights, business cases, and structured decision support to drive action and results.

• Demonstrated hands-on analytics depth, including direct experience with SQL and Python and the ability to inspect, challenge, and improve analytical logic.
• Proven ability to partner strategically with CX or operational leadership to translate business objectives into measurable analytics products, insights, and enterprise reporting.
• Experience making prioritization decisions using business value, urgency, reuse potential, risk, and delivery capacity while maintaining a strong stakeholder experience.
• Experience moving high-value ad hoc analysis into scalable reporting, governed metrics, semantic models, or data engineering-supported assets.
• Ability to operate in a resource-constrained environment with a bias toward MVP delivery, iteration, reuse, and measurable business impact.
• Comfort working across Analytics, Data Engineering, Data Governance, Technology, Data Science, AI, and business leadership teams to deliver trusted analytics at scale.
• Strong business acumen with the ability to frame opportunities and problems in business terms, quantify the value at stake, and prioritize investments based on business impact rather than request volume or urgency.
• Demonstrated success influencing executive and senior leadership decisions through analytics, insights, storytelling, and fact-based recommendations that drive measurable business outcomes.

• Bachelor's degree in Business, Economics, Analytics, Data Science, Computer Science, Information Systems, Statistics, Mathematics, Finance, Operations, or related field required; Master's degree preferred.
• 10+ years of progressive experience in customer analytics, CX analytics, business intelligence, customer insights, data science, consulting, strategy, or related disciplines.
• 5+ years of leadership experience managing analytics, BI, insights, reporting, or analytics engineering teams.
• Experience leading analytics transformation, reporting modernization, KPI standardization, self-service analytics, dashboard rationalization, customer intelligence programs, or semantic model adoption.
• Experience supporting senior leaders through operating reviews, performance management routines, executive dashboards, strategic planning, and decision support.
• Experience in healthcare, financial services, benefits administration, contact center operations, regulated environments, or consumer-facing service organizations preferred.
• Experience in consulting, customer intelligence, customer experience strategy, business intelligence, or customer insights environments preferred.
• Strong communication skills with the ability to make complex data actionable and explain technical tradeoffs to non-technical leaders.
• Strong people leadership skills with the ability to coach analysts, raise quality standards, develop talent, and create a culture of accountability and continuous improvement.

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The actual compensation offer is determined based on job-related knowledge, education, skills, experience, and work location. This position will be eligible for performance-based incentives and restricted stock units as part of the total compensation package, in addition to a full range of benefits including:  

• Medical, dental, and vision 

• HSA contribution and match 

• Dependent care FSA match 

• Uncapped paid time off 

• Paid parental leave 

• 401(k) match 

• Personal and healthcare financial literacy programs 

• Ongoing education & tuition assistance 

• Gym and fitness reimbursement 

• Wellness program incentives