Corporate Vice President - Data Architecture
New York Life
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Job summary
New York Life seeks a senior Data Architect leader to guide the target-state architecture across two strategic transformations. You will shape canonical models, data products, and source-of-truth patterns, enabling AI-enabled decisioning and regulated data usage.
You will partner with Finance, Investments, Governance and Security to ensure enterprise data quality, lineage and compliant access while building scalable data platforms across EPP and AI-led Portfolio Management.
Qualifications
- 15+ years of data-architecture leadership in complex enterprises.
- Experience delivering AI-native data architectures and data products.
- Strong collaboration with finance, governance, security and risk teams.
Responsibilities
- Own target-state data architecture across two strategic bets.
- Define canonical models, data-products boundaries and source-of-truth patterns.
- Partner with business data owners and technology leads to ensure governance and compliance.
Skills
Job description
Location Designation: Hybrid - 3 days per week
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 NewYorkLife.
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.
- 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.
- Partnerwith business data owners, Finance, Investments and enterprise data-governance teams to clarify ownership and stewardship of critical data and metrics.
- Ensure critical deterministic usecases 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 securelydiscover, retrieve, interpretand combine trusted enterprise information.
- Definewhen 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 forcombining structured and unstructured information, including retrievalsearch, vectorand semantic capabilities where appropriate.
- Definearchitecture patternsforagent identity, entitlements, provenance, citationsand traceability so AI‑generated insights remain grounded in authorized and trusted enterprise information.
- Partner with AI, platform, Security and Risk teams toensure AI data access supports responsible AI, privacy, control and audit requirements.
- Continuously evaluate emerging data and AI architecture patterns anddetermine their practical applicability to NewYorkLife 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 DomainTechnologyLeads to translate business and technology requirements into scalable data architecture.
- Define how NEXUS accesses deterministic metrics, analytical data, contextual information andagentic datasources 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 contentcanbe governed and made accessible to applications, analytics and AI agents.
- Partner withPortfolio ManagementDomainTechnologyLeads and the Solution Engineering &Architecture Lead to establish reusable data patterns across investment capabilities.
- Ensure investment data requiredby downstream Finance and EPP capabilities canbe connected through governed, traceable and scalable patterns.
- Balancethe distinctive data needsof public and private markets with opportunities for common enterprise architecture and reuse.
- Partner closelywith the two Solution Engineering &Architecture Leads, whoown end-to-end solution architecture within each strategic bet, while retaining accountability for cross-bet data architecture and data patterns.
- Jointlyresolve architecture decisions where application, agent, integration and data architecture intersect, ensuring neither solution design nor data design evolves in isolation.
- Partnerwith Domain TechnologyLeads to ensure data architecturesupports the end-to-end technology capability and business outcomes within each domain.
- Provide architectural direction to the federated Data Engineering Leadand TDAVdata‑engineeringteams responsibleforbuilding and operationalizing data pipelines, products and services.
- Workwith enterprise data architecture, governance, platform and cloud teams to align strategic-bet needswithenterprise standards while constructively challengingstandards when transformation outcomes require new patterns.
- Use targeted prototypes and proofs ofconcept to validate critical data-architecture assumptions, connectivity patterns, latency, scalability, semantic approaches and agentic access patterns before broad implementation.
- Partnerwith engineering teamstoturn architecture into reusable, production-ready patterns rather than limiting architecture output to diagrams and standards.
- Create clear architecturedecisions, reference implementations and guidance that allow outcome pods and domain teams to move quickly with appropriate autonomy.
- Review major data designs for alignment withtarget architecture and intervenewhere bespoke patterns create unnecessary duplication, risk or long-term complexity.
- Continuously incorporate evidence from delivery into the evolutionof data architecture principles and patterns.
- Communicatecomplex data architecture choices and tradeoffs clearly to senior business and technology executives and influencedecisions across organizational boundaries.
- Partner with Security, Privacy, Risk, Compliance, Audit and control functions toensure data architectures incorporate appropriate governance, entitlements, resiliency, auditability and regulatory requirements from inception.
- Create transparency around material data dependencies, architecture risks, technical debt andinvestment 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 experiencefrom 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 otherhigh caliber technology talent , whilebuilding technology leadershipanddomain expertise acrossthe organization.
- Establishclear accountabilityanda culture of collaboration, innovation, engineering disciplineandcontinuous improvement.
- 15+ years… (list continues)
- … (additional bullet items as in original text)
- … (maintain all bullets verbatim)
- 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.
- Own the data architecture across two strategic enterprise transformations and shape how information powers planning, performance and investment decision-making.
- Define how NewYorkLife balances trusted data products with new AI-enabled patterns for federation, retrieval and runtime composition.
- Create the connective data architecture between EPP and Portfolio Management, including critical cross-bet capabilities such as Investments to FP&A.
- Shape how applications, analytics and AI agents securely access and interpret trusted enterprise information.
- Influence consequential enterprise decisions across data, technology, architecture, governance and investment while working directly with senior leaders.
- Turn architecture into working patterns by partnering closely with engineering teams and strategic partners.
- Build durable data foundations and reusable patterns that can extend beyond the initial strategic bets as NewYorkLife evolves its AI-enabled enterprise architecture.
At New York Life, our 180-year legacy of purpose and integrity fuels our future. As we evolve into a more technology, data, and AI-enabled organization, we remain grounded in the values that drive lasting impact.
Our diverse business portfolio creates opportunities to make a difference across industries and communities, inviting bold thinking, collaborative problem-solving, and purpose-driven innovation. Here, you will find the rare balance of long-standing stability and forward momentum.
As a Fortune 100 mutual company, we offer a place to grow your skills, contribute to meaningful work, and delver solutions that matter. Your ideas drive what is next, and your growth powers it.
LEVELMG3
Salary Range: $185,000-$264,500
Overtime eligible: Exempt
Discretionary bonus eligible: Yes
Sales bonus eligible: No
Actual base salary will be determined based on several factors but not limited to individual’s experience, skills, qualifications, and job location. Additionally, employees are eligible for an annual discretionary bonus. In addition to base salary, employees may also be eligible to participate in an incentive program.
We provide a full package of benefits for employees – and have unique offerings for a modern workforce, including leave programs, adoption assistance, and student loan repayment programs. Based on feedback from our employees, we continue to refine and add benefits to our offering, so that you can flourish both inside and outside of work. Click here to discover more about our comprehensive benefit options or visit our NYL Benefits Site.
At New York Life, fostering an inclusive workplace is fundamental to who we are and how we serve our communities. We have a longstanding commitment to creating an environment where individuals can contribute their best and succeed together. This foundation is rooted in our core values of humanity and integrity, ensuring that every employee feels valued and supported. By embracing a broad range of perspectives and experiences, we achieve greater success and fulfill our promise of providing financial security and peace of mind to families across all communities. Click here to learn more about NewYorkLife’s leadership in this space.
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