Senior Vice President, Data Leader Manager

BNY Mellon•New York, NY

22h•$104,000 - $200,000•Onsite

About The Position

This role will shape and execute the DR and GP&T commercial data agenda, combining strong governance and control discipline with a commercial, execution-oriented mandate. The role will lead strategic data workstreams, enable AI-ready and reusable data products, strengthen end-to-end data risk management, and translate data investments into measurable business value across client, product, and commercial priorities.

Requirements

7-10+ years of experience or equivalent work experience required
Proven experience leading data strategy from concept through execution with a strong track record of delivery.
Significant experience in enterprise data programs, ideally within financial services, with a strong focus on supporting commercial priorities in Payments and Depositary Receipts, including sales, client coverage, product performance, and business growth.
Strong understanding of data governance, metadata, lineage, data quality, controls, and regulatory expectations.
Experience translating governance and remediation priorities into business enablement, reusable data products, and AI-readiness outcomes.
Ability to align data investments to commercial goals, operating efficiency, control outcomes, and platform delivery.
Proven success managing complex cross-functional programs across business, product, technology, risk, controls, privacy, and data stakeholders.
Strong analytical, problem-solving, and execution skills, including experience with Agile delivery and prioritization.
Experience with data governance tooling, data profiling, remediation, and quality improvement practices.
Strong executive communication, stakeholder management, and people leadership skills, with the ability to influence across senior levels and operate strategically and hands-on.

Responsibilities

Lead strategy execution across DR and GP&T commercial data priorities
Execute the DR and GP&T commercial data strategy in alignment with BNY’s enterprise data strategy and evolving Data & AI operating model.
Steer priority data initiatives that support AI-enabled execution, platform delivery, governance, and risk-managed adoption.
Translate business and commercial priorities into a clear execution roadmap, including prioritization, dependencies, milestones, and outcomes.
Run governance and execution forums to oversee planning, escalation, OKRs, risk management, and decision-making.
Lead process re-engineering efforts that improve data flows, reduce manual intervention, and support strategic goals.
Drive commercial office and business enablement outcomes
Partner with commercial, product, client, and functional leaders to identify priority data needs that improve client insight, product management, commercialization, and business performance.
Develop reusable and scalable data capabilities that support commercial use cases, management reporting, operational transparency, and opportunity identification.
Connect data investments to measurable business outcomes, including adoption, efficiency, stronger controls, improved client outcomes, and value realization.
Act as a connector across business, data, and technology teams to ensure commercial needs are reflected in strategy and delivery.
Drive data projects, transformation, and AI/data product readiness
Sponsor or lead strategic data projects and initiatives across DR and GP&T commercial.
Support enterprise remediation, cross-platform transformation, and modernization efforts where data readiness is critical.
Partner with business and technology stakeholders to define requirements, clarify ownership, and deliver fit-for-purpose capabilities.
Advance AI-ready data products and help scale data and AI responsibilities through a phased implementation approach.
Strengthen data foundations, governance, and execution discipline
Define and prioritize critical data in partnership with governance and business leads, aligned to enterprise data management standards.
Establish authoritative datasets, clear ownership, definitions, lineage, quality expectations, and remediation paths.
Improve metadata, lineage, and rationalization practices to increase transparency, reuse, and control effectiveness.
Own readiness and execution risk across the data user journey, coordinating resolution of dependencies, issues, and adoption barriers.
Support completion of data maturity and readiness activities and track actions through closure.
Establish a strong control and responsible data use framework
Partner with Data Quality, Data Privacy, Risk, and Controls leads to strengthen data quality, access, protection, and management practices.
Support regulatory readiness and evidence control effectiveness across applicable data obligations.
Enable safe and trusted reuse through strong provenance, permissible use, fit-for-purpose standards, and responsible sharing.
Represent DR / GP&T commercial in enterprise and business data governance forums and contribute to broader data and AI governance discussions.
Deliver measurable value
Track progress across governance, remediation, delivery, and business outcomes.
Establish KPIs that demonstrate adoption, reuse, efficiency gains, control improvement, and commercial impact.
Communicate progress, risks, tradeoffs, and value delivered to senior stakeholders.