Senior Enterprise Data Analyst

First Citizens Bank•Raleigh, NC

About The Position

This role is part of the Enterprise Data organization and is primarily focused on the overarching governance and architecture of all data across the enterprise. The Senior Analyst will play a critical leadership role within an Agile data pod supporting the enterprise data environment that underpins Corporate Finance and Regulatory Reporting. This role bridges business, data, and technology to ensure delivery of high-quality, compliant, and scalable data solutions. Operating at a strategic and execution level, the individual will drive regulatory data remediation, strengthen data governance, and enable Corporate Finance and Regulatory Reporting teams to meet evolving regulatory expectations with confidence and precision.

Requirements

Bachelor's Degree and 6 years of experience in Data Management, Data Analytics OR High School Diploma or GED and 10 years of experience in Data Management, Data Analytics
Strong SQL and experience with enterprise data platforms: Snowflake, Oracle, SQL Server, Netezza
Familiarity with ETL/data pipelines and data virtualization
Familiarity with Data governance tools (e.g., Collibra)
Familiarity with Data visualization (Power BI, Tableau)
Experience with cloud platforms (AWS/Azure)
Demonstrated ability to lead cross-functional initiatives across global teams
Strong executive communication and stakeholder management skills
Ability to operate in ambiguity and drive outcomes in fast-paced environments

Nice To Haves

15+ years of experience in data management, data quality, or regulatory data within financial services
Proven leadership in regulatory reporting data remediation (FR Y-14 strongly preferred)
Experience working with large, complex banking organizations
Deep knowledge of Finance and Regulatory data in banking industry
Deep knowledge of Data quality frameworks and controls
Deep knowledge of Data governance and lineage
Strong understanding of Finance data domains and regulatory expectations
Python / advanced analytics (preferred)

Responsibilities

Lead end-to-end data remediation initiatives focused on accuracy, completeness, and timeliness of regulatory reporting data
Define and prioritize remediation roadmaps aligned with regulatory commitments (e.g., FR Y-14, Y-9C, Call Reports)
Provide thought leadership on improving data quality frameworks and control environments
Partner with senior stakeholders to drive enterprise data maturity
Own requirements lifecycle: intake, decomposition, prioritization, and traceability
Lead Agile ceremonies and guide backlog prioritization to align with regulatory and business goals
Translate complex regulatory/data requirements into structured user stories and technical specifications
Design and implement robust data quality rules, controls, and monitoring frameworks
Strengthen data lineage, reconciliation, and traceability across critical data flows
Partner with Data Governance teams to enforce standards across metadata, lineage, and critical data elements
Drive data defects to remediation and closure working with key stakeholders
Drive adoption of enterprise tools (e.g., Collibra) and governance practices
Serve as a key interface between Finance, Regulatory Reporting, Technology, and Data
Influence senior stakeholders and provide clear, actionable insights on data risks and remediation progress
Lead data and regulatory discussions, including responses to edit checks and supervisory inquiries
Communicate program status, risks, and dependencies to leadership forums
Lead deep-dive analyses of data quality issues
Identify systemic root causes and define sustainable remediation solutions
Establish issue management frameworks with clear ownership, SLAs, and escalation paths
Partner with Data and Enterprise Architecture and Engineering teams to drive migration to modern data platforms (e.g., Snowflake, AWS, Azure)
Ensure modernization initiatives improve data quality, scalability, and regulatory compliance
Promote adoption of advanced data capabilities including automation and AI/ML where appropriate
Define acceptance criteria and oversee validation of data solutions
Ensure completeness of documentation including: Business requirements, Source-to-target mappings, Data lineage and metadata
Support internal audits and regulatory reviews