Job Title: Data Modeler – Retail / Finance Domains
Location: Toronto, ON Hybrid
Duration: 12 months

Role Overview
Seeking an experienced Data Modeler with strong Retail (preferably Loyalty) and Finance/Capital Markets domain knowledge. The role focuses on designing scalable, consistent, and extensible data models that support complex operational lifecycles across multi‑system environments.

Key Responsibilities

Data Modeling & Architecture

• Design conceptual, logical, and physical data models across Retail, Supply Chain, Banking, and Capital Markets domains.
• Model time‑series, reference, market, and transactional data.
• Align designs with Medallion Architecture (Bronze/Silver/Gold) and cloud lakehouse environments (AWS, Spark, Parquet, Iceberg).

Standards & Governance

• Define modeling standards, templates, naming conventions, and data dictionaries.
• Establish best practices to ensure consistency, scalability, and long‑term extensibility.

Model Review & Optimization

• Evaluate existing data models for alignment with best practices.
• Identify gaps, inconsistencies, and improvement areas for multi‑system integration.

Lifecycle‑Wide Modeling Support

• Setup: Introduce new attributes for segmentation, eligibility, rules, and workflow triggers.
• Execution: Model structures supporting multi‑step workflows, state transitions, and real‑time/near‑real‑time flows.
• Financial Processing: Define data required for funding logic, allocation rules, settlement, and reconciliation.
• Analytics: Build scalable facts, dimensions, and hierarchies for performance measurement and insights.

Collaboration

• Work with business SMEs, architects, engineering, finance, and analytics teams.
• Translate business rules into normalized, logical, and physical models.

Required Skills & Experience

• 6–12 years of enterprise data modeling experience in complex, multi‑system environments.
• Strong expertise in ERwin, ER/Studio, PowerDesigner, or similar tools.
• Proficient in relational, dimensional, and lakehouse modeling.
• Hands‑on experience with cloud storage formats (Parquet/Iceberg) and distributed computing platforms.
• Solid understanding of Finance & Capital Markets data (trades, risk, positions, reference data).
• Strong communication, analytical, and documentation skills.

Preferred Qualifications

• Exposure to Azure, Databricks, Snowflake, DBT.
• Knowledge of data governance, lineage, and regulatory compliance.
• Experience working in Agile/Scrum environments.