Senior Director, Data Management Manager

BNY Mellon•New York, NY

1d•$136,500 - $275,000•Onsite

About The Position

We’re seeking a future team member for the role of Senior Director, Data/AI Engineering Manager to join our team. This role is located in New York and will lead a strategic engineering function supporting enterprise and business priorities through modern data, AI, and platform capabilities. This leader will operate at the intersection of business, data, AI, technology, architecture, risk, governance, and transformation. They will be expected to strengthen engineering and solutioning capabilities, drive execution excellence, and identify opportunities to improve data accessibility, context management, interoperability, controls, and business value through AI-enabled platforms, knowledge graph capabilities, intelligent automation, and AI-led engineering practices. A core part of this role will be shaping and scaling the firm’s AI context layer, including knowledge graph, semantic, and metadata-driven capabilities that improve how data, systems, processes, and business concepts are connected, understood, governed, and used across the organization. This leader will also drive AI-led engineering and solutioning, enabling teams to modernize delivery, accelerate development, improve design quality, and unlock new business capabilities through practical application of AI. This is an opportunity to lead a strategically important function at BNY, helping shape the future of data and AI engineering while building high-performing teams and delivering measurable impact at scale. This role is located in New York City, NY.

Requirements

Bachelor’s degree or the equivalent combination of education and experience is required; advanced degree preferred.
Significant experience in financial services or another highly regulated industry, with strong exposure to data engineering, AI/ML platforms, enterprise architecture, metadata, governance, or digital transformation.
Extensive experience in a senior leadership role leading large-scale data, AI, engineering, platform, or solution delivery functions in complex enterprise environments.
Proven ability to lead large, matrixed organizations, including demonstrated success in people leadership, organizational design, and leadership development.
Strong track record of delivering transformation and modernization initiatives at scale, including platform engineering, process redesign, operating model improvement, automation, and control adoption.
Demonstrated experience building and scaling enterprise data and AI capabilities, including modern data platforms, contextual data services, metadata-driven solutions, or knowledge-centered architectures.
Experience with AI context layer concepts, such as knowledge graphs, semantic models, ontologies, metadata frameworks, lineage, graph-based relationships, or contextual retrieval patterns, and how they can be applied to enterprise business and technology use cases.
Demonstrated experience applying AI-enabled capabilities to improve engineering productivity, solution quality, workflow efficiency, interoperability, decision-making, or business outcomes.
Deep understanding of operational risk, controls, governance, and regulatory expectations in highly regulated environments.
Strong business and stakeholder leadership skills, with the ability to influence across business, technology, architecture, risk, compliance, operations, and senior leadership teams.
Experience managing budgets, workforce planning, and strategic investment prioritization.
Proven ability to establish and maintain trusted business partnerships across a matrixed organization.
Strong judgment, problem-solving capability, and execution discipline in a dynamic environment with evolving priorities.

Nice To Haves

Experience leading enterprise or domain-level data engineering programs, including data pipelines, data platforms, metadata, lineage, semantic services, and stewardship-aligned engineering models.
Strong understanding of the role of contextual data architecture in supporting AI, analytics, operations, controls, search, decision support, and client outcomes.
Experience with knowledge graph, graph-based data modeling, semantic technologies, or entity relationship frameworks in enterprise-scale environments.
Familiarity with AI solution architecture, including retrieval patterns, contextual grounding, model integration, workflow orchestration, and responsible AI implementation considerations.
Experience leading organizations through operating model transformation in support of growth, resiliency, scalability, and stronger governance.
Familiarity with modern data platforms, API-enabled architecture, cloud-based engineering patterns, governance tooling, and AI-enabled developer productivity practices.
Passion for building an AI-first, data-informed, engineering-led, and continuously improving organization.

Responsibilities

Lead a critical Data/AI Engineering function, setting direction for engineering strategy, solution delivery, platform enablement, data interoperability, AI adoption, and organizational performance.
Define and execute the strategic vision for the function, aligning data and AI engineering priorities with business goals, architectural direction, regulatory expectations, risk requirements, and long-term transformation objectives.
Build and lead high-performing teams across a large-scale organization, ensuring strong organizational design, leadership capability, workforce planning, and alignment to enterprise priorities.
Drive the design and implementation of scalable, resilient, and well-controlled data and AI engineering platforms that improve data usability, discoverability, accessibility, and trust across critical business domains.
Lead development of the AI context layer, including knowledge graph, ontology, semantic modeling, metadata, lineage, and contextual data frameworks that connect enterprise data, business concepts, and workflows to improve intelligence, reuse, and decision support.
Establish engineering patterns and architectural standards for integrating structured and unstructured data, metadata, semantic relationships, and contextual services into enterprise AI and data solutions.
Partner across Business, Technology, Operations, Risk, Compliance, Architecture, and other stakeholder groups to align investments, priorities, and execution plans with strategic data and AI objectives.
Advance AI-led engineering and solutioning by identifying and scaling practical use cases for AI across software development, architecture, workflow orchestration, intelligent automation, solution design, and platform productivity.
Drive modernization of engineering practices through automation, reusable frameworks, platform thinking, and AI-assisted delivery approaches that improve speed, quality, resiliency, and operating leverage.
Lead the design and evolution of data and AI governance-aligned engineering capabilities, ensuring strong controls around context management, data usage, explainability, traceability, security, and operational oversight.
Translate emerging AI and data engineering opportunities into executable roadmaps, target-state architectures, and prioritized investments that deliver measurable business value.
Oversee delivery of strategic programs across the function, ensuring strong execution discipline, governance routines, issue remediation, dependency management, performance tracking, and continuous improvement.
Lead risk, control, and operational oversight across the function, ensuring strong governance, resiliency, regulatory awareness, and disciplined execution in a highly controlled environment.
Manage budgets, resource allocation, and investment priorities in support of strategic plans and sustainable business performance.
Recruit, develop, and retain strong talent while fostering an inclusive, accountable, and high-performance culture grounded in collaboration, curiosity, innovation, and continuous improvement.
Represent the organization in senior leadership forums, translating complex data, AI, engineering, and transformation priorities into clear business value, strategic tradeoffs, and actionable outcomes.
Build trusted relationships with internal stakeholders and senior leaders, serving as a strategic partner who can navigate complexity, solve problems, and drive alignment across a matrixed organization.