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
.

• 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

• 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

• 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)

• 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

• 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

• 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

• Define enterprise standards for:
  • Data design and modeling
  • Data integration and interoperability
  • Data product structure
• Establish reusable architecture patterns that enable: