Director, CX Data and Service Analytics

HealthEquity•Remote,

7d•$137,500 - $182,000

About The Position

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.

Requirements

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 Service and CX performance measures, including call center metrics, queue performance, handle time, first-contact resolution, abandonment, transfer rates, quality measures, CSAT, NPS, and member experience trends.
Experience with CX survey or Voice of Customer platforms such as Qualtrics or similar tools, and 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 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 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 Service or Operations leadership to translate business objectives into measurable analytics products and enterprise reporting.
Experience making prioritization decisions using business value, urgency, reuse potential, risk, and delivery capacity, while maintaining a supportive 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.
Bachelor degree in Analytics, Data Science, Computer Science, Information Systems, Statistics, Mathematics, Economics, Business, Finance, Operations, or related field required; master degree preferred.
10+ years of progressive experience in analytics, business intelligence, reporting, data science, CX analytics, service analytics, operations analytics, or related disciplines.
5+ years of leadership experience managing analytics, BI, reporting, insights, or analytics engineering teams.
Experience leading analytics transformation, reporting modernization, KPI standardization, self-service analytics, dashboard rationalization, or semantic model adoption.
Experience supporting senior leaders through operating reviews, performance management routines, executive dashboards, and strategic decision support.
Strong communication skills with the ability to make complex data actionable and to 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.

Nice To Haves

Experience in healthcare, financial services, benefits administration, contact center operations, regulated environments, or consumer-facing service operations preferred.

Responsibilities

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 analytics into clear business narratives, recommendations, and decision support for Service leadership and executive audiences.
Measure analytics effectiveness through adoption, cycle time, stakeholder satisfaction, reporting quality, reduced manual effort, improved decision speed, and business impact.