Assistant Vice President - Complaints Data Strategy, Architecture and

Define & execute the Enterprise Complaints Data Strategy: Establish the target-state vision, operating model, standards, and procedures; drive a multi-quarter roadmap across complaint platforms and intake channels. Architect the complaints data ecosystem: Design and implement scalable data architectures, data models, and domain-aligned data products that power analytics, governance reporting, and issue management. Assess and onboard new data sources: Build and execute repeatable approaches to evaluate new data sources (quality, completeness, lineage, controls), and recommend/implement optimal remediation paths to address data quality gaps. Ensure alignment to Enterprise Data Policy & Standards: Own and maintain Collibra assets (business definitions, lineage, retention, access controls), steward data dictionaries, and lead intake/resolution of data issues and incidents as the complaints data domain steward. Establish and run data quality controls & monitoring: Define data quality metrics, execute periodic data quality audits, and develop/maintain Tableau control and data operations dashboards to monitor health, surface emerging issues, and track KPIs across the complaints data environment. Drive cross-functional delivery and adoption: Partner across Enterprise Complaints and with Enterprise Data Lake, Technology, Data Office, Operations, and Governance leaders to prioritize initiatives, align dependencies, and ensure solutions are adopted and sustained. Enable advanced analytics and innovation: Deliver curated/aggregated datasets that support advanced analytics use cases including trend detection, anomaly detection, and thematic risk identification. Lead projects and mentor less experienced staff member s, and p erform special projects as assigned in support of enterprise complaint program maturity, platform modernization, and regulatory commitments. Qualifications/

4 years' experience spanning data engineering, data architecture, and data governance/controls, ideally in a regulated environment (financial services preferred) OR in lieu of a bachelor's degree, a minimum of 6 years of hands-on data engineering & governance experience. Demonstrated ownership of data strategy and architecture for a business-critical domain (end-to-end from source to curated layers to consumption). 4 years Hands-on Data Engineering experience building scalable pipelines and curated datasets using Pypark/ScalaSpark/SAS Demonstrated experience with design and conduct complex analysis on large structured , unstructured data to identify and remediate data quality or integrity issues Experience with automating and consolidating data from multiple .