
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. As a Business Experience & Planning Advisor Senior within PNC's Customer Data Forensics organization, you will be based in Cleveland, OH; Pittsburgh, PA; Tyson's Corner, VA; Washington, D.C.
Job Summary:
This is a senior-level individual contributor responsible for improving the quality, governance, usability, and reliability of enterprise customer data. This 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.
Key Responsibilities:
Customer Data Strategy & Stewardship
• Serve as a subject matter expert (SME) for enterprise customer data concepts including customer identity, householding, 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 guidance.
• Enable analytics, operations, and customer-facing teams by improving accessibility to reliable customer datasets and features, advising on proper usage, and accelerating time-to-insight/time-to-decision.
Cross-Functional Leadership
• Act as a connector across business, operations, risk, and technology-translating business needs into data requirements and actionable delivery plans.
• Mentor analysts/junior data stewards and promote standards, playbooks, and repeatable practices.
• Influence without authority through clear narratives, fact-based recommendations, and proactive stakeholder engagement.
Preferred Qualifications:
• Experience with Master Data Management (MDM), customer identity resolution, probabilistic matching, or householding solutions.
• Familiarity with data governance frameworks and tools (e.g., Collibra/Alation, Archer/GRC tools, data cataloging/lineage platforms).
• Experience supporting regulatory programs, MRAs, audit readiness, and control evidence practices.
• Experience supporting analytics and data science workflows (feature engineering, cohort/segment analysis, model inputs/outputs) and translating business decision needs into data solutions.
• Familiarity with decisioning and measurement practices (e.g., segmentation strategies, champion/challenger testing, experimentation, KPI design) and working with stakeholders to evaluate impact.
• SQL proficiency and experience working in data lake / warehouse environments (cloud and/or on-prem).
• Background in financial services data management or other highly regulated environments.
Core Skills & Competencies:
• Customer data domain expertise (definitions, lifecycle, usage, risks, and controls)
• Data quality management (profiling, rule design, monitoring, thresholds, reconciliation)
• Root cause analysis & remediation leadership
• Governance & control mindset (risk-based prioritization, audit readiness, responsible data use)
• Analytics enablement (analytically ready datasets, feature definitions, metric/KPI alignment)
• Decisioning & insight orientation (connecting data work to business outcomes; supporting model and decision solution lifecycle)
• Requirements definition (business-to-technical translation; user stories; acceptance criteria)
• Executive communication (clear narratives, concise updates, decision framing)
• Collaboration & influence across business, tech, risk, compliance, and operations
PNC is an in-office company that fosters a supportive culture where employees can thrive and achieve balance. We encourage candidates to connect with their recruiter and hiring manager to understand workplace expectations and ensure the role aligns with their goals.
PNC will not provide sponsorship for employment visas or participate in STEM OPT for this position.
Job Description