Business & Planning Advisor Senior - Customer Data Forensics

Position Overview

At PNC, our people are our greatest differentiator and competitive advantage in the markets we serve. We are all united in delivering the best experience for our customers. We work together each day to foster an inclusive workplace culture where all of our employees feel respected, valued and have an opportunity to contribute to the company’s success.

This is a senior‑level individual contributor responsible for improving the quality, governance, usability, and reliability of enterprise customer data. The role partners across business, technology, analytics, risk, and compliance to define customer data standards, resolve data issues at the root cause, and enable trusted customer data products for reporting, analytics and modeling, business decisioning, customer experience, and regulatory needs. The role also helps shape and deliver analytically ready datasets and reusable features that accelerate insight generation and decision solutions.

• Customer Data Strategy & Stewardship
  • Serve as a subject matter expert (SME) for enterprise customer data concepts including customer identity, house holding, customer hierarchies, contact data, consent/preferences, and customer attributes.
  • Drive customer data definition alignment (business glossary, critical data elements, metadata), ensuring consistent meaning and usage across platforms and lines of business.
  • Contribute to customer data strategy and roadmap, identifying opportunities to modernize customer data capabilities and reduce fragmentation.
• Data Quality & Issue Management
  • Execute customer data quality management practices: profiling, monitoring, rule definition, threshold tuning, and performance reporting.
  • Lead investigation of data anomalies and recurring defects using structured root cause analysis; coordinate remediation with upstream/downstream partners.
  • Implement scalable controls and preventive measures (e.g., validation rules, reconciliation checks, exception handling, automation) to reduce repeat issues.
• Analytics, Modeling & Decisioning Enablement
  • Partner with analytics and data science teams to translate business problems into data requirements, analytically ready datasets, and reusable features (e.g., customer identity, household, relationship, and behavioral attributes).
  • Support model development and monitoring by improving data completeness, stability, and explainability; document assumptions, transformations, and known limitations for appropriate use.
  • Enable business decisioning use cases by defining customer data inputs for segmentation, targeting, credit/marketing decisioning, personalization, and next‑best‑action solutions.
  • Establish fit‑for‑purpose data quality checks for analytic pipelines (distribution shifts, outliers, freshness, leakage risks) and coordinate remediation when thresholds are breached.
  • Collaborate with partners to develop KPIs and measurement approaches that connect data improvements to business outcomes (e.g., conversion, retention, risk performance, operational efficiency).
• Governance, Controls & Regulatory Support
  • Support customer data governance routines including stewardship forums, issue/decision logs, and control evidence management to enable consistent, trusted use of customer data across reporting, analytics, and decisioning.
  • Ensure customer data processes and controls align to risk, audit, privacy, retention, and regulatory requirements while supporting responsible innovation and scalable analytic consumption.
  • Produce leadership‑ready reporting on customer data risk posture, control health, remediation progress, and key metrics; highlight impacts to critical reporting, models, and decision solutions.
• Data Enablement & Product Delivery
  • Partner with data engineering and product teams to define requirements for customer data solutions (MDM/EDS/APIs/data lake), including onboarding, lineage, analytic consumption patterns, and performance/availability needs.
  • Support design and operationalization of “trusted” customer data products and feature sets (e.g., curated views, golden records, identity/household features), including documentation, data contracts, and consumption…