LEAD DATA ARCHITECT

Senior Data Architect / Data Modeler

MUST HAVE - Lead Data Architect/Modeler experience with hands on MDM/Reference Data in large investment management, asset management, wealth management, OCIO, custody, or buy-side enterprise environments. Role Summary Seeking a senior data architect / data modeler to support enterprise data architecture and database design across complex investment-data initiatives. This role has been recalibrated from a general Sr. Data Analyst profile to a true data architecture and data modeling role. The primary requirement is deep data modeling capability: conceptual, logical, and physical modeling; entity relationships; database design; and the ability to turn business needs into scalable data structures. Investment management experience is valuable, but it is not the primary screen. The most important requirement is proven success creating data models and data architecture for large enterprise environments. Experience with master data management and S&P EDM / Markit EDM is highly desirable.

Core Mission

• Receive business requirements and translate them into conceptual, logical, and physical data models that support enterprise-scale data solutions.
• Create database designs and data architecture deliverables that can be used by engineering, data management, EDM/MDM, and business teams.
• Mentor team members and business/data partners on modeling principles, entity relationship thinking, data definitions, and design discipline.
• Support reference data, MDM, investment data, and enterprise data delivery initiatives with clear, reusable models and design artifacts.
• Help improve data delivery strategy by bringing disciplined modeling and architecture capability into complex, ambiguous data programs.

Key Responsibilities

• Develop conceptual, logical, and physical data models from business requirements, source data analysis, business processes, and downstream consumption needs.
• Create ER diagrams, entity relationship definitions, data definitions, attribute definitions, relationship cardinality, keys, hierarchies, and database design artifacts.
• Reverse-engineer large enterprise datasets to identify business concepts, data entities, relationships, patterns, gaps, and inconsistencies.
• Define data structures, mappings, transformation logic, data quality rules, reference data usage, metadata, lineage, and business rules needed to support implementation.
• Partner with business stakeholders, data management teams, EDM/MDM resources, engineers, and architects to align business meaning with technical design.
• Support master data management and reference data design, including universal identifiers, crosswalks, entity resolution, stewardship, quality, accuracy, and timeliness principles.
• Work with SQL and relational databases to analyze source data, validate assumptions, profile data, and support design decisions.
• Create data architecture and design documentation that is clear enough for engineering delivery and business validation.
• Mentor analysts, data partners, and stakeholders on how to move from requirements and business concepts to structured data models and database designs.
• Identify reference data gaps, data management gaps, business process gaps, and propose practical, phased recommendations.

Required Experience

• 10+ years of data architecture, data modeling, enterprise data analysis, database design, or MDM/reference data experience in large enterprise environments.
• Deep experience creating conceptual, logical, and physical data models, including entities, attributes, relationships, keys, hierarchies, normalization, cardinality, and data definitions.
• Ability to take business requirements and turn them into database designs and data architecture deliverables, not just BRDs, specs, or process documentation.
• Strong SQL and relational database experience, including Oracle, SQL Server, or comparable RDBMS environments.
• Experience analyzing complex enterprise datasets, reverse-engineering data entities and relationships, and identifying business concepts from data patterns.
• Experience with ETL concepts, data pipelines, mappings, transformation logic, data quality rules, metadata, lineage, and governance.
• Experience working in large, siloed organizations and navigating incomplete or ambiguous requirements.
• Ability to communicate complex data concepts clearly to business, data, and technology stakeholders.
• Experience mentoring others or elevating team capability around data modeling and data design.

Preferred / Plus Experience

• Financial services, investment management, asset management, wealth management, OCIO, custody, or buy-side vendor experience.
• Knowledge of fund instruments, mutual funds, CITs, fund pricing, ABOR, IBOR, PBOR, TBOR, custodial data, portfolio data, pricing data, and investment operations data.
• Experience with MDM/reference data platforms, especially S&P EDM / Markit EDM, including EDM implementations, MDM use cases, and reference data lifecycle management.
• Experience with State Street Alpha, Charles River, Aladdin, FactSet, Bloomberg, Eagle, GoldenSource, or other trading/investment data management platforms.
• Experience with Erwin, ER/Studio, dbdiagram/DB Studio, Visio, PowerPoint, Excel, Word, or similar modeling/documentation tools.
• Certifications such as CFA, CAIA, or CIPM are helpful but not required.