Global Head of Data Architecture, SVP

Job description

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: • Platform scalability • AI-ready data design • Provide clear guidance to engineering and platform teams without owning delivery Collaboration with Platforms & Technology • Partner deeply with: • Data Platform Products • AI Platform Products • Enterprise architects within GTS • Ensure architecture is: • Technically feasible • Consistent across environments • Scalable for enterprise AI use Enterprise Influence & Alignment • Act as the enterprise authority on data architecture • Drive alignment across business and technology stakeholders • Promote a reuse-first, domain-driven data culture across the firm Team Leadership • Lead a global team of ~10–15 data architects • Build capabilities in: • Domain architecture • Data modeling • Enterprise data design • Foster a culture of: • Deep business engagement • Practical, execution-oriented architecture • High-quality, consistent outputs Qualifications & Experience • Senior leadership experience in data or enterprise architecture within financial services • Strong knowledge of State Street–relevant domains: • Custody and fund services • Asset management • Trading and markets • Wealth and client servicing platforms • Deep understanding of: • Modern data architecture patterns • Distributed and platform-based data ecosystems • Proven ability to: • Define enterprise-wide architecture frameworks • Influence across complex, federated organizations • Strong blend of business domain expertise and technical depth Leadership Profile • Enterprise architect with strong business acumen • Able to unify fragmented landscapes into cohesive, simple architectures • Influential leader who drives alignment without direct control • Balances strategic clarity with execution realism • Strong communicator capable of articulating the “big picture” clearly Salary Range: $225,000 - $337,500 Annual The range quote