Job Title: Data Modeler
Location: Toronto, Canada | Hybrid
Duration: Long-Term Contract
Role Overview
We are looking for an experienced Data Modeler to support a Data Science and Analytics team working on AI, Machine Learning, Fraud Analytics, KYC, and data-driven initiatives.
The candidate will be responsible for data modeling, data analysis, data mapping, SQL development, data architecture, and solution design across complex banking and financial data environments.
Key Responsibilities
- Design and maintain conceptual, logical, and physical data models.
- Translate business requirements into scalable data models and structures.
- Develop and maintain data mapping specifications across source and target systems.
- Analyze data relationships, entities, attributes, and data flows.
- Perform hands-on SQL-based data analysis and data validation.
- Analyze production data to identify data quality issues and root causes.
- Participate in data architecture and solution design discussions.
- Define data transformation rules, business logic, and data lineage.
- Prepare data specifications and pseudo-code for Development and QE teams.
- Support Fraud, KYC, AI/ML, and analytics data initiatives.
- Investigate production data issues and provide detailed root-cause analysis.
- Collaborate with Business, Development, QE, Data Science, and Architecture teams.
- Review test cases from a data and business-rule perspective.
- Support technical change requests and stakeholder approvals.
- Ensure data models align with enterprise data architecture, governance, and quality standards.
Required Skills
- Strong experience in Data Modeling and Data Analysis.
- Expertise in Conceptual, Logical, and Physical Data Modeling.
- Strong SQL and hands-on data analysis experience.
- Experience with Data Mapping, Data Lineage, and Data Relationships.
- Strong understanding of relational databases and data structures.
- Experience with Data Architecture and Solution Design.
- Strong Banking / Financial Services / KYC domain knowledge.
- Experience analyzing production data and data quality issues.
- Ability to translate business requirements into data models and technical specifications.
- Experience working with Development, QE, Business, Data Science, and Architecture teams.
- Strong analytical and problem-solving skills.
