Head of Multi-Asset & Wealth Data

S&P Global•New York, NY

About The Position

The Head of Multi-Asset & Wealth Data owns the end-to-end Multi-Asset & Wealth data value stream within S&P Global’s Global Data Enablement and Operations organization. The role ensures data products support index construction, calculation, rebalancing, analytics, and downstream use, and translates index, product, and commercial priorities into a prioritized data product roadmap with clear delivery commitments, dependencies, and outcomes. The role leads data products through sourcing, domain definition, governance, platform adoption, operational readiness, and continuous improvement. As the primary business-facing data leader for Multi-Asset & Wealth, the Head aligns stakeholders, resolves priorities, and communicates progress, risks, and trade-offs. The position coordinates Data Domain Owners, Data Product Owners, and Data Operations Analysts, and drives vendor and content sourcing decisions, data standards adoption, and operational centralization transition plans. The role partners with business owners and teams across technology, product, platforms, design, index management and design, commercial, enterprise data, data operations, and external data and content providers. It is limited to people with indefinite right to work in the United States. Final-stage external candidates must attend at least one in-person interview, ordinarily at their nearest S&P Global office, before an offer can proceed.

Requirements

Extensive experience leading data products, data strategy, data operations, or data transformation within financial services, index, asset management, wealth management, or a closely related environment.
Demonstrated ability to own an end-to-end data roadmap and convert complex business strategy into prioritized requirements, delivery plans, and measurable outcomes.
Strong understanding of data governance, domain ownership, master data, data quality, lineage, vendor or content sourcing, and modern data-platform operating models.
Proven leadership of cross-functional teams and senior stakeholders across business, product, operations, and technology, with the ability to influence without relying on direct authority.
Commercially minded, with the ability to connect data investment to index, client, operational, and risk outcomes.
Clear, credible communication skills, including the ability to simplify technical issues, surface trade-offs, and drive decisions.
Strong prioritization, execution, and change-management skills.
Collaborative leadership skills, creating accountability and effective partnerships across global teams.
Indefinite right to work in the United States.

Nice To Haves

Experience supporting multi-asset indices, portfolio construction, model portfolios, wealth solutions, or related investment data workflows.
Knowledge of index construction and calculation processes, data entitlements and licensing, and third-party market or reference data.
Experience establishing OKRs and KPIs for data products, operational performance, data quality, and platform adoption.
Experience centralizing or transforming data operations while maintaining control, service continuity, and stakeholder confidence.

Responsibilities

Own the end-to-end Multi-Asset & Wealth data value stream, ensuring data products support index construction, calculation, rebalancing, analytics, and downstream use.
Translate index, product, and commercial priorities into a clear, prioritized, executable data product roadmap with defined delivery commitments, dependencies, and outcomes.
Lead data product lifecycles from sourcing and domain definition through governance, platform adoption, operational readiness, and continuous improvement.
Serve as the primary business-facing data leader for Multi-Asset & Wealth, aligning stakeholders, resolving priorities, and communicating progress, risks, and trade-offs.
Lead and coordinate Data Domain Owners, Data Product Owners, and Data Operations Analysts, establishing an effective domain structure and operating rhythm.
Drive asset-class vendor and content sourcing decisions, adoption of master data and data-quality standards, and transition plans for greater operational centralization.