Enterprise Data Quality Solutions Lead, Assistant Vice President - statestreet

The Enterprise Data and AI Office is seeking an execution-oriented Enterprise Data Quality Solutions lead to help advance the firm’s enterprise Data Quality Management framework, standards, control capabilities, and technology-enabled solutions.

This role will focus on translating enterprise data quality policies, standards, regulatory expectations, and control requirements into scalable business processes, platform capabilities, and adoption practices across business and technology teams.

The successful candidate will play a key role in strengthening enterprise capabilities for data quality management, controls, measurement and reporting, exception and issue management, and BCBS 239-aligned risk data aggregation and reporting outcomes using the Collibra Platform and related technologies.

The ideal candidate combines Data Quality Management expertise, product management experience, and business analysis skills, with the ability to drive cross-functional delivery in a complex global enterprise environment.

This position is part of the Enterprise Data Governance team within the Chief Data and AI Office. The role is important to strengthen the firm’s ability to define, measure, monitor, and improve data quality across critical data, key metrics, critical reports, and related business processes.

Success in this role will be measured through effective implementation of sustainable data quality management capabilities, improved controls, to enable accurate, complete, timely, and well-controlled data.

• Support implementation of enterprise Data Quality Management standards, procedures, operating practices, and supporting technology capabilities.
• Translate data quality policies, standards, controls, and regulatory expectations into clear business requirements, functional requirements, user stories, process flows, and acceptance criteria.
• Drive continuous improvement of enterprise Data Quality Error Management and Data Quality Issue Management processes, including escalation, tracking, closure evidence, and management reporting.
• Partner with Business Data Owners, Data Stewards, Data Governance Leads, and Technology teams to implement practical data quality measurement, monitoring, and governance capabilities for measuring data quality, including accuracy, completeness, timeliness, where applicable.
• Support the adoption of enterprise Data Quality tools to support data profiling, automated data quality rules and related data quality measurement and governance reporting.
• Identify opportunities to automate data quality control execution, monitoring, escalation, and evidence retention through enterprise technology platforms.
• Define requirements for workflow-enabled data quality control management, control effectiveness reporting, and management oversight dashboards.