Global Head of Data Architecture, SVP

Who We Are Looking For

Define and establish a unified “One State Street” data architecture, including enterprise data domains, reusable data assets, and a clear multi-year roadmap—enabling consistent, AI-ready data across all businesses and functions.

The Head Of Data Architecture Is Accountable For Creating a Cohesive Enterprise Data Architecture That Spans State Street’s Full Business Landscape, Including

• Investment Services



• Investment Management



• Wealth



• Alpha platform



• Global Markets



• Corporate and control functions

This role works deeply across business and technology to understand domain-level data structures, flows, and usage , and synthesize them into a single, integrated enterprise architecture view .

A core focus is to identify, standardize, and drive adoption of reusable data assets and enterprise definitions , ensuring that the organization benefits from shared, consistent, and high-quality data across use cases, platforms, and business lines.

The role defines both the target-state architecture and the practical transformation journey , ensuring that current fragmented data landscapes evolve into a well-structured, scalable, and AI-ready ecosystem.

Success is measured by clarity and adoption of enterprise data architecture, reuse of data assets across domains, and enablement of scalable data and AI platforms .

What You Would Be Responsible For

Enterprise Data Architecture Vision & “One State Street” Blueprint

• Define and maintain the enterprise data architecture vision and target state



• Develop a unified “One State Street” data architecture blueprint, integrating:
  
  
  
  
  
  • All business domains
  
  
  
  • Cross-functional data flows
  
  
  
  • Platform-aligned data structures
  
  
  
  
  



• Create clear architectural representations that simplify the enterprise data landscape

Deep Business Domain Alignment

• Partner closely across:
  
  
  
  
  
  • Investment Services
  
  
  
  • Investment Management
  
  
  
  • Wealth
  
  
  
  • Alpha platform
  
  
  
  • Global Markets
  
  
  
  • Control functions (Finance, Risk, Compliance, Operations, etc.)
  
  
  
  
  



• Build deep understanding of:
  
  
  
  
  
  • Business processes
  
  
  
  • Domain data models
  
  
  
  • Data usage and dependencies
  
  
  
  
  



• Translate domain complexity into standardized enterprise data models and structures

Enterprise Data Domains & Modeling

• Define and standardize:
  
  
  
  
  
  • Enterprise data domains and sub-domains
  
  
  
  • Domain ownership boundaries
  
  
  
  • Conceptual and logical data models
  
  
  
  
  



• Ensure consistency and interoperability across domains



• Enable domain-oriented architecture aligned to modern principles (e.g., data products and reuse-first design)

Reusable Data Assets & Enterprise Definitions

• Lead identification and standardization of reusable data assets across the firm



• Define and promote enterprise-level data definitions and canonical data structures



• Drive reuse of:
  
  
  
  
  
  • Core data entities (e.g., client, instrument, transaction, position)
  
  
  
  • Data products and datasets
  
  
  
  
  



• Partner with Data Platform Products (Role 4) to ensure reusable assets are:
  
  
  
  
  
  • Easily discoverable
  
  
  
  • Accessible and consumable
  
  
  
  
  



• Drive adoption across businesses to maximize enterprise value from shared data

Data Asset Mapping, Classification & Transparency

• Establish a comprehensive view of enterprise data assets across all domains



• Define consistent frameworks for:
  
  
  
  
  
  • Data asset classification
  
  
  
  • Domain tagging
  
  
  
  • Business vs. technical metadata
  
  
  
  
  



• Ensure visibility into:
  
  
  
  
  
  • What data exists
  
  
  
  • Where it resides
  
  
  
  • How it is used
  
  
  
  
  



• Partner with Governance (Role 1) on classification alignment without owning policy

Data Architecture Roadmap & Transformation Journey

• Define a multi-year data architecture roadmap from current to target state



• Identify:
  
  
  
  
  
  • Redundant and fragmented data assets
  
  
  
  • Opportunities for consolidation and reuse
  
  
  
  • Critical architecture gaps
  
  
  
  
  



• Sequence transformation in alignment with:
  
  
  
  
  
  • Strategy & Portfolio (Role 2) priorities
  
  
  
  • Platform delivery roadmaps
  
  
  
  
  



• Ensure architecture is actionable and tied to real execution

Standards, Patterns & Architectural Guidance

• Define enterprise standards for:
  
  
  
  
  
  • Data design and modeling
  
  
  
  • Data integration and interoperability
  
  
  
  • Data product structure
  
  
  
  
  



• Establish reusable architecture patterns that enable: