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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior Vice President, Data Product Architect - **Company:** The Bank of New York Mellon Corporation - **Location:** New York, NY, United States - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Data Analysis, Data Profiling, Reference Data, Standard Sql, SQL Databases, Information Technology, Data Management - **Published:** August 29, 2026 - **Apply:** https://jobs.localjobnetwork.com/apply/add/88180240/1 ## About the Role * Significant experience in Reference Data, Master Data, Business Data Architecture or Information Architecture. * Demonstrated experience designing complex party, client, account or relationship models. * Experience defining authoritative-source and lifecycle-management models, global identifiers, entity resolution, golden-record or registry patterns. * Experience translating business scenarios into engineering-ready requirements. * Experience partnering directly with Product, Operations, Architecture and Engineering. * Strong conceptual, logical, hierarchy, relationship and effective-dated modeling skills. * Experience designing Reference Data lifecycle, stewardship, data-quality and control processes. * Strong SQL and hands-on data-profiling capabilities. * Ability to connect business outcomes, operating processes, data models and engineering requirements. * Ability to lead focused design sessions, influence without authority and move stakeholders toward decisions. * Pragmatic judgment and the ability to distinguish working hypotheses from approved decisions. * Bachelor's degree in computer science or a related discipline, or equivalent work experience required * 10-12 years of related experience in data management services required; experience in Asset Servicing, Fund Accounting, Custody, Middle Office, Transfer Agency, Wealth Management or Capital Markets ## Description * Define conceptual and logical models for Party, Client, Legal Entity, Account, Contract, Product, Service, Instrument and related hierarchies. * Model complex legal, commercial, servicing and operational relationships across Asset Servicing. * Define enterprise identifier, cross-reference, entity-resolution, hierarchy and effective-dating patterns. * Translate business outcomes and representative scenarios into testable rules and requirements. * Define authoritative-source, registry and Reference Data distribution patterns. Operating Model and Adoption * Define how Reference Data is requested, created, matched, approved, enriched, changed, linked and retired. * Establish ownership and decision rights across Product, Operations, Data, UCM, CRM and servicing platforms. * Design match, resolve or create rules, including stewardship, controls, exceptions and escalation. * Develop reusable transition and adoption patterns for additional business domains. Data Analysis and Engineering Partnership * Use SQL and data profiling to validate relationships, hierarchies, business rules and data-quality assumptions. * Analyze duplicate, incomplete and conflicting identifiers and trace representative records across systems. * Develop lightweight prototypes to test architecture hypotheses and accelerate decisions. * Translate the architecture into functional requirements, mappings, acceptance criteria and controls. * Partner with Engineering on options, dependencies, sequencing and implementation trade-offs. ## Related Videos - [Data Science in Retail](https://www.wearedevelopers.com/videos/586-data-science-in-retail) - [How building an industry DBMS differs from building a research one](https://www.wearedevelopers.com/videos/768-how-building-an-industry-dbms-differs-from-building-a-research-one) - [Fault Tolerance and Consistency at Scale: Harnessing the Power of Distributed SQL Databases](https://www.wearedevelopers.com/videos/1146-fault-tolerance-and-consistency-at-scale-harnessing-the-power-of-distributed-sql-databases) - [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works) - [Data Fabric in Action - How to enhance a Stock Trading App with ML and Data Virtualization](https://www.wearedevelopers.com/videos/253-data-fabric-in-action-how-to-enhance-a-stock-trading-app-with-ml-and-data-virtualization) - [Crypto-secure Data Management with In-Database Blockchain](https://www.wearedevelopers.com/videos/632-crypto-secure-data-management-with-in-database-blockchain) ## Related Articles - [Data Analyst Salary in the UK](https://www.wearedevelopers.com/magazine/278-data-analyst-salary-in-the-uk) - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production) - [Top Big Data Technologies That You Need to Know](https://www.wearedevelopers.com/magazine/108-top-big-data-technologies-that-you-need-to-know) - [Graph and AI Trends 2026: Why Is AI Running but Not Yet Delivering?](https://www.wearedevelopers.com/magazine/680-graph-and-ai-trends-2026-why-is-ai-running-but-not-yet-delivering) - [Making Data Warehouses Fast: A Developer’s Story](https://www.wearedevelopers.com/magazine/107-making-data-warehouses-fast-a-developer-s-story) - [Everything a Developer Needs to Know About MCP with Neo4j](https://www.wearedevelopers.com/magazine/604-everything-a-developer-needs-to-know-about-mcp-with-neo4j)