Corporate Vice President - Data Architecture

New York Life•New York, NY

3d•$185,000 - $264,500•Hybrid

About The Position

Within the Tech, Data, AI, Ventures (TDAV) organization, our work is guided by a shared vision: deploying the power of technology, data, AI and ventures to accelerate sustainable competitive advantage for New York Life's businesses. We build solutions that power how we serve policy owners, agents, advisors and employees while delivering measurable business outcomes. Across technology, data, AI, cyber, product, digital experience, architecture and infrastructure, TDAV combines the scale and investment of an industry leader, access to leading-edge technologies and the opportunity to help shape how a world-class financial services company competes in the AI era - all backed by the stability and purpose of a mutual company built to last. New York Life is advancing two strategic transformations designed to reimagine critical enterprise capabilities through technology, data, analytics, automation, and AI: Enterprise Plan to Perform (EPP) and AI-led Portfolio Management. EPP is focused on reimagining how the enterprise plans, projects, understands, and manages financial performance across capabilities including Expense Management, Planning & Projections, Performance Management, Capital, NII, Driver-Based Modeling and Scenario Analysis, and the NEXUS cross-EPP experience. AI-led Portfolio Management is focused on reimagining investment and portfolio-management capabilities across portfolio construction and markets, macro research, private markets, investment-to-FP&A connectivity, business leadership reporting, client reporting and distribution, and related investment decision workflows. Both transformations depend on trusted, governed and reusable data. At the same time, AI and agentic architectures are changing how enterprise data can be discovered, accessed, combined and used. This creates an opportunity to rethink where physical consolidation is required, where governed federation is appropriate, how deterministic and agentic access patterns coexist, and how data products, semantics, lineage, provenance and entitlements are designed for both applications and AI agents. The Corporate Vice President, Data Architecture provides senior data architecture leadership across EPP and AI-led Portfolio Management, with accountability for defining and governing the target-state data architecture that enables both strategic transformations. The role is the senior data architecture authority for the two bets and determines how trusted enterprise data is organized, modeled, governed, connected, accessed and made usable by applications, analytics and AI agents. The leader establishes cross-bet data architecture principles, canonical and semantic models, data-product boundaries, source-of-truth patterns, lineage and provenance requirements, and the architectural approach to consolidation, federation, replication, APIs and runtime access. This is not a traditional data-modeling or architecture-review role. The leader is expected to make consequential architecture decisions, resolve cross-domain data issues, influence senior business and technology leaders, and work across federated Data Engineering, Solution Engineering & Architecture, Domain Technology Leads, enterprise platforms, Security, Risk and Control functions, and strategic partners. A critical mandate is to establish an AI-native data architecture that preserves trust, quality, governance and deterministic access where required while enabling agents to securely discover, retrieve, interpret and compose information across structured and unstructured enterprise sources. The role will translate emerging architectural shifts into practical, production-ready patterns for New York Life. The role reports directly to the Executive Technology Leader for the two strategic bets and provides architectural direction to federated data-engineering teams without requiring all data-engineering resources to report directly into the role.

Requirements

15+ years of progressively responsible experience in data architecture, data engineering, enterprise architecture, technology architecture, data platforms, analytics or related disciplines, including significant leadership responsibility for complex enterprise data ecosystems.
Proven experience defining target-state data architecture across multiple domains, applications and business capabilities within a large, complex enterprise.
Demonstrated expertise with enterprise data products, canonical and semantic modeling, metadata, lineage, data quality, governance and source-of-truth patterns.
Strong experience designing modern data architectures spanning cloud data platforms, APIs, integration, streaming or real-time patterns, data replication, federation and analytical consumption.
Experience making architecture decisions across structured and unstructured data and balancing centralized, distributed and federated data patterns.
Demonstrated understanding of generative AI and agentic architectures and the data-access, retrieval, provenance, security, entitlement and governance patterns required to support production AI solutions.
Experience operating within federated or matrixed enterprises and influencing Data Engineering, application engineering, architecture, platform and business teams without relying solely on formal authority.
Strong experience partnering with senior business and technology executives and communicating consequential architecture decisions and tradeoffs in business terms.
Experience leading architecture across major transformations involving multiple concurrent workstreams, complex dependencies and strategic technology partners.
Experience working within enterprise Security, Privacy, Risk, Compliance, Audit and data-governance frameworks.
Demonstrated ability to move from architecture strategy into practical solution proving, reference implementations and production adoption.
Metrics- and outcomes-oriented leadership experience, with the ability to connect data architecture investments to delivery speed, reuse, reliability, risk reduction and measurable business value.
Enterprise Data Architecture - Defines coherent target-state architectures across domains and balances strategic direction with pragmatic delivery needs.
Data Product & Semantic Architecture - Establishes durable data-product boundaries, canonical models, semantic consistency and reusable metric patterns.
AI-Native Data Architecture - Designs governed data-access and retrieval patterns that enable AI agents while preserving trust, provenance, entitlements and deterministic access where required.
Systems Thinking - Understands the interaction among business processes, applications, data, analytics, AI, architecture, controls and operating models.
Architecture Judgment - Makes sound tradeoffs across consolidation, federation, replication, APIs, latency, scalability, cost, quality and risk.
Executive Communication & Influence - Translates complex architecture topics into clear choices and influences senior stakeholders across organizational boundaries.
Cross-Functional Leadership - Aligns Solution Architecture, Domain Technology, Data Engineering, enterprise platforms, Security, governance and strategic partners around common patterns.
Cloud & Modern Data Platform Fluency - Strong understanding of cloud-native data platforms, integration, APIs, analytical architectures and modern engineering patterns.
Governance, Security & Controls - Designs for data quality, lineage, privacy, security, entitlements, resiliency, auditability and regulatory expectations.
Solution Proving - Uses prototypes and engineering evidence to validate architecture assumptions and accelerate adoption of reusable patterns.
Organizational Savvy - Navigates complex federated environments and resolves architecture conflicts constructively.
Learning Agility & Technology Curiosity - Maintains an external perspective and rapidly evaluates emerging data and AI technologies for practical enterprise value.
People Management – Develops and empowers talent through clear expectations, actionable feedback, effective coaching, and accountability, while fostering an inclusive, high-performing team environment.

Nice To Haves

Experience within insurance, asset management, banking or broader financial services.
Experience with Finance, FP&A, enterprise performance management, investment data, portfolio management or related financial and investment capabilities.
Experience establishing enterprise data-product strategies or data-mesh/federated data architectures in a large organization.
Experience with Databricks or comparable modern data platforms, semantic layers, data catalogs, APIs, event-driven architectures and enterprise integration patterns.
Experience designing data architectures for generative AI, agentic AI, retrieval-augmented generation, enterprise search, knowledge systems or multi-agent solutions.
Experience with MCP or other emerging agent-to-data/tool connectivity patterns where appropriate.
Experience leading data architecture across greenfield transformation initiatives while integrating with significant legacy estates.
Experience working with strategic technology vendors, consulting organizations and external engineering partners.

Responsibilities

Own the target-state data architecture across EPP and AI-led Portfolio Management and ensure the two transformations evolve on coherent, reusable and enterprise-aligned data foundations.
Establish data architecture principles, reference patterns, decision frameworks and guardrails covering data products, semantic models, data movement, access, integration, storage, federation and consumption.
Drive consequential decisions regarding authoritative sources, canonical models, source-of-truth patterns, consolidation versus federation, real-time versus replicated data, and reuse across domains.
Identify and resolve cross-domain and cross-bet data dependencies before they become delivery constraints, including the data architecture required to connect investment decisions with FP&A, NII, capital, earnings and planning capabilities.
Provide senior architecture leadership on major data investments and ensure architecture decisions balance business value, speed, scalability, reliability, cost, risk and long-term sustainability.
Define the architecture and boundaries for governed enterprise data products supporting the two strategic bets.
Establish canonical and semantic models, business definitions and reusable metric patterns so critical information is interpreted consistently across applications, analytics and AI experiences.
Define expectations for lineage, provenance, freshness, quality, traceability, metadata and source attribution, particularly for business-critical financial and investment information.
Partner with business data owners, Finance, Investments and enterprise data-governance teams to clarify ownership and stewardship of critical data and metrics.
Ensure critical deterministic use cases have reliable, governed and production-ready data paths while avoiding unnecessary duplication or consolidation.
Establish data-access patterns for AI and agentic solutions, including how agents securely discover, retrieve, interpret and combine trusted enterprise information.
Define when data should be curated and persisted as a deterministic data product versus accessed dynamically through APIs, governed retrieval, federation, MCP or other appropriate integration patterns.
Shape architectures for combining structured and unstructured information, including retrieval, search, vector and semantic capabilities where appropriate.
Define architecture patterns for agent identity, entitlements, provenance, citations and traceability so AI-generated insights remain grounded in authorized and trusted enterprise information.
Partner with AI, platform, Security and Risk teams to ensure AI data access supports responsible AI, privacy, control and audit requirements.
Continuously evaluate emerging data and AI architecture patterns and determine their practical applicability to New York Life rather than adopting technology for its own sake.
Own the cross-domain data architecture supporting Expense Management, Planning & Projections, Performance Management, NEXUS, Capital, NII, Driver-Based Modeling, Scenario Analysis and related EPP capabilities.
Ensure consistent definitions and architectural patterns for enterprise and business performance metrics, financial drivers, plans, forecasts, actuals, scenarios and management insights.
Partner with the Performance Technology Lead and EPP Domain Technology Leads to translate business and technology requirements into scalable data architecture.
Define how NEXUS accesses deterministic metrics, analytical data, contextual information and agentic data sources while preserving lineage, quality, performance and appropriate entitlements.
Shape data architecture for source platforms such as planning and financial systems and determine appropriate ingestion, API, replication and runtime-access patterns.
Own the cross-domain data architecture supporting portfolio construction and markets, macro research, private markets, investment data, business leadership reporting, client reporting and distribution, and related investment workflows.
Define how structured investment data, proprietary information, research, market information and unstructured content can be governed and made accessible to applications, analytics and AI agents.
Partner with Portfolio Management Domain Technology Leads and the Solution Engineering & Architecture Lead to establish reusable data patterns across investment capabilities.
Ensure investment data required by downstream Finance and EPP capabilities can be connected through governed, traceable and scalable patterns.
Balance the distinctive data needs of public and private markets with opportunities for common enterprise architecture and reuse.
Partner closely with the two Solution Engineering & Architecture Leads, who own end-to-end solution architecture within each strategic bet, while retaining accountability for cross-bet data architecture and data patterns.
Jointly resolve architecture decisions where application, agent, integration and data architecture intersect, ensuring neither solution design nor data design evolves in isolation.
Partner with Domain Technology Leads to ensure data architecture supports the end-to-end technology capability and business outcomes within each domain.
Provide architectural direction to the federated Data Engineering Lead and TDAV data-engineering teams responsible for building and operationalizing data pipelines, products and services.
Work with enterprise data architecture, governance, platform and cloud teams to align strategic-bet needs with enterprise standards while constructively challenging standards when transformation outcomes require new patterns.
Use targeted prototypes and proofs of concept to validate critical data-architecture assumptions, connectivity patterns, latency, scalability, semantic approaches and agentic access patterns before broad implementation.
Partner with engineering teams to turn architecture into reusable, production-ready patterns rather than limiting architecture output to diagrams and standards.
Create clear architecture decisions, reference implementations and guidance that allow outcome pods and domain teams to move quickly with appropriate autonomy.
Review major data designs for alignment with the target architecture and intervene where bespoke patterns create unnecessary duplication, risk or long-term complexity.
Continuously incorporate evidence from delivery into the evolution of data architecture principles and patterns.
Communicate complex data architecture choices and tradeoffs clearly to senior business and technology executives and influence decisions across organizational boundaries.
Partner with Security, Privacy, Risk, Compliance, Audit and control functions to ensure data architectures incorporate appropriate governance, entitlements, resiliency, auditability and regulatory requirements from inception.
Create transparency around material data dependencies, architecture risks, technical debt and investment decisions across the two transformations.
Help shape the broader enterprise perspective on how AI changes data-product and consolidation strategies by grounding emerging concepts in practical experience from the strategic bets.
Maintain an external perspective on modern data architecture, data products, semantic technologies, AI-native data patterns and financial-services practices.
Build, lead and develop high performing teams with strong domain knowledge and modern technology and engineering capabilities.
Attract, develop and retain forward deployed and other high caliber technology talent, while building technology leadership and domain expertise across the organization.
Establish clear accountability and a culture of collaboration, innovation, engineering discipline and continuous improvement.