Data Architect - Investment Data & Analytics

The Data Architect will lead the design, governance, and evolution of the firm's investment data architecture, enabling scalable analytics, reporting, AI-driven insights, and self-service data consumption. This role serves as the bridge between business, investment stakeholders, and technology teams, ensuring that data is modeled, governed, documented, and structured to support modern investment processes and future AI-enabled capabilities. The role combines deep expertise in data architecture, Snowflake modeling, semantic layer design, metadata management, governance, and investment data domains.

Define and maintain target-state architecture for investment data platforms and analytical ecosystems. Design logical, physical, and conceptual data models that support portfolio management, research, trading, risk, performance, and attribution analytics. Establish architecture standards, design patterns, and best practices across investment technology. Drive domain-based architecture and data product strategies aligned with enterprise objectives.

Architect and maintain scalable Snowflake data structures optimized for analytics and AI consumption. Design dimensional, domain-oriented, and consumption-focused data models. Develop and govern semantic layers, curated views, and reusable analytical data products. Ensure data models support reporting platforms, self-service analytics, and downstream applications.

Define enterprise standards for metadata, lineage, data definitions, and business glossaries. Establish data quality frameworks, reconciliation controls, and validation processes. Partner with governance, security, and compliance teams to ensure controlled and governed data access. Promote adoption of enterprise data catalogs and metadata management practices.

Architect data structures that support natural language querying, AI-assisted analytics, and generative AI solutions. Define semantic frameworks that make business and technical context discoverable and reusable. Ensure data and metadata are structured for AI, agent-based access, and future intelligent applications. Partner with AI and analytics teams to enable contextual, cross-domain data experiences.

Establish standards for documenting data architecture, models, lineage, business definitions, and usage patterns. Create and maintain architecture diagrams, data flow documentation, data dictionaries, and technical specifications. Ensure architecture artifacts remain accurate, discoverable, and aligned with evolving business processes. Champion documentation practices that support both human users and AI-enabled knowledge retrieval.

Collaborate with portfolio managers, researchers, risk teams, operations, and technology partners to understand data requirements. Translate business objectives into scalable architectural solutions. Lead architecture reviews and provide guidance on technology and data strategy decisions. Communicate complex architectural concepts effectively to both technical and non-technical audiences.

Bachelor's degree in Computer Science, Information Systems, Engineering, Data Science, or related discipline. Master's degree preferred.

10+ years of experience in data architecture, data modeling, or enterprise data management. Significant experience designing analytical data platforms and data warehouses. Strong experience with Snowflake and cloud-based data ecosystems. Experience with in financial services, asset management, or investment technology preferred.

• Data Architecture
• Enterprise data architecture frameworks and best practices.
• Logical, conceptual, and physical data modeling.
• Domain-driven design and data product architecture.
• Data lifecycle and information management practices.
• Snowflake & Modern Data Platforms
• Snowflake architecture and performance optimization.
• Advanced SQL and analytical modeling techniques.
• Data warehouse and lakehouse architecture patterns.
• Experience with cloud data platforms and modern analytics ecosystems.
• Analytics & Semantic Modeling
• Dimensional modeling (star and snowflake schemas).
• Semantic layer design and business-friendly data abstractions.
• Power BI and analytics platform integration.
• Self-service analytics enablement.
• Governance & Metadata
• Data lineage,…