Data Modeler - Snowflake

Snowflake Data Modeler Investment Analytics We're seeking a Snowflake Data Modeler to design and govern the analytical data foundations supporting our investment processes. You will focus on building high quality Snowflake data models and semantic layers that power reporting, analytics, and AI-enabled insights across portfolio management, risk, and research. This role is ideal for someone who excels at data modeling, understands analytics consumption, and enjoys working closely with investment professionals in a highly analytical environment. This role focuses on data modeling, semantic design, and data quality within Snowflake to ensure that investment data is consistent, trusted, and analytics ready. The Snowflake Data Modeler will play a central role in enabling scalable analytics, self-service reporting, and AI-enabled use cases across investment teams.

Role Overview:

You will serve as a Snowflake focused data modeler, responsible for designing, evolving, and governing data models that power investment analytics, reporting, and downstream applications. The role emphasizes dimensional and domain modeling, semantic layer design, and data quality, enabling research analysts, portfolio managers, risk teams, and quantitative users to access clean, well-structured data. This role partners closely with data engineers, architects, and investment stakeholders but is primarily focused on modeling and consumption, not pipeline engineering. Key Responsibilities Snowflake Data Modeling & Design Design and maintain dimensional, domain oriented, and analytics optimized data models in Snowflake. Develop fact and dimension models supporting portfolio management, risk, attribution, research, and performance analytics. Optimize Snowflake schemas for performance, scalability, and ease of consumption. Semantic Layer & Analytics Enablement Build and maintain semantic layers and curated views that abstract underlying complexity for BI and analytics tools. Ensure Snowflake models align with Power BI datasets, metrics, hierarchies, and business definitions. Partner with analytics teams to enable consistent metric definitions and reusable analytical patterns. Data Quality, Validation & Governance Define and implement data quality rules, validation checks, and reconciliation logic within modeled datasets. Collaborate with data governance teams to document data definitions, lineage, and usage standards. Support controlled access patterns, rolebased views, and governed data exposure. Investment Data Domain Modeling Build strong understanding of key investment data domains including reference data, pricing, terms &conditions, indices, risk inputs, and derived analytics. Model data in ways that reflect how investment teams consume and analyze information. Ensure that data relationships and grain are clearly defined and analytically sound. AI Ready &Discoverable Data Structures Design Snowflake models that are AIready, with consistent schemas, clear relationships, and rich metadata. Support semantic consistency to enable natural language querying and AIdriven analytics. Partner with architecture teams on Snowflakebased semantic and knowledge layers that support GenAI use cases. Collaboration & Cross-Functional Partnership Work closely with data engineers to ensure pipelines land data in structures optimized for modeling. Partner with investment teams to translate analytical requirements into robust data models. Contribute to modeling standards, best practices, and reusable design patterns across the organization.

Required Skills & Qualifications:

Core Technical Skills Strong expertise in Snowflake data modeling for analytics and reporting use cases. Advanced SQL skills for modeling, transformation, and performance optimization in Snowflake. Deep experience with dimensional modeling, star/snowflake schemas, and domain driven design. Analytics & Consumption Experience designing data models to support Power BI, including semantic alignment and metric consistency. Familiarity with BI consumption patterns and performance considerations. Data Quality & Governance Experience implementing data validation, reconciliation, and quality frameworks. Working knowledge of metadata, lineage, and governance concepts (e.g., Purview or similar tools). AIAware Modeling (Preferred) Understanding of how well-structured models support AI, search, and natural language analytics. Familiarity with semantic layers, business glossaries, and model discoverability. Data &Investment Knowledge Experience or strong interest in investment data domains (fixed income, equities, multiasset preferred). Soft Skills Strong analytical thinking and attention to detail. Ability to communicate modeling concepts clearly to both technical and investment audiences. Collaborative mindset and comfort working across engineering, analytics, and business teams. Technical Skills Senior Snowflake Data Modeler Snowflake & SQL (Core): Advanced SQL for analytical modeling, transformations, and performance optimization Snowflake schema design (databases, schemas, tables, views, secure views) Snowflake performance tuning (clustering, pruning, query optimization) Understanding of Snowflake virtual warehouses and workload separation Experience modeling data directly in Snowflake (ELTstyle consumption modeling) Data Modeling & Design Dimensional modeling (star / snowflake schemas, facts & dimensions) Domainoriented / subjectarea modeling for analytics Grain definition, surrogate keys, slowly changing dimensions (SCDs) Analytical model design for portfolio, risk, performance, attribution, and research use cases Translating business requirements into logical and physical data models Semantic Layer & Analytics Enablement Designing analytics-ready views and curated consumption layers Alignment of Snowflake models with Power BI semantic models Metric standardization, hierarchies, and business definitions Supporting self service analytics through well designed data structures Understanding of BI performance considerations driven by model design Data Quality & Validation Defining and implementing data quality rules and reconciliation checks Ensuring referential integrity, completeness, and consistency across models Identifying and resolving data modeling issues that impact analytics accuracy Partnering with engineering teams on upstream/downstream data issues Metadata, Governance & Discoverability Documenting data models, fields, and business definitions Experience with metadata catalogs (e.g., Microsoft Purview or similar) Designing models with clear lineage and usage context Supporting governed access patterns (rolebased / domainbased views) AIReady & SearchFriendly Modeling (Preferred) Designing consistent schemas and relationships to support AI consumption Awareness of semantic modeling concepts used for natural language querying Structuring data models for discoverability and reuse in GenAI / Copilot scenarios Familiarity with business glossaries, ontologies, or semantic abstractions Investment Data Literacy (Strongly Preferred) Familiarity with investment data domains: Reference & instrument data Pricing and market data Terms & conditions Index data Risk and derived analytics Understanding how modeling decisions affect analytical outputs and decisions Collaboration &Tooling Working closely with data engineers on ELT handoff and model readiness Experience with documentation tools (Confluence, SharePoint, Markdown, etc.) Ability to explain data models clearly to technical and investment stakeholders We look forward to reviewing your profile.