Data Modeler

2 manager references with LinkedIn

Key Responsibilities:

• Design, develop, and maintain conceptual, logical, canonical, and physical data models, including ERDs and dimensional/star schemas, to support asset management, wealth management, and financial advisory platforms.

• Partner with business stakeholders, data engineers, solution architects, and technical teams to translate complex financial domain requirements into scalable, enterprise data models.

• Model data across key financial subject areas, including financial profiles, advisor teams, securities, holdings, portfolio performance, and risk analytics.

• Apply data modeling best practices, including normalization, slowly changing dimensions (SCD), data lineage, metadata management, and reference data governance.

• Develop and optimize data models across relational databases, cloud platforms, and big data environments such as Snowflake, SQL Server, AWS, and Azure.

• Ensure data models comply with financial services regulatory requirements, including data privacy, auditability, and reference data integrity.

• Collaborate with ETL and data engineering teams to validate source-to-target mappings, troubleshoot data quality issues, and support reporting and analytics initiatives.

Required Qualifications:

• 8+ years of experience in enterprise data modeling and data architecture.

• Proven experience within investment management, asset management, wealth management, or broader financial services environments.

• Strong understanding of conceptual, logical, canonical, and physical data modeling methodologies.

• Hands-on experience designing dimensional (star schema) and normalized relational data models.

• Advanced SQL skills with the ability to analyze, validate, and optimize complex datasets.

• Proficiency with enterprise data modeling tools such as Erwin, ER/Studio, Hackolade, or similar platforms.

• Experience working with cloud data platforms and databases, including Snowflake, SQL Server, AWS, and Azure.

• Knowledge of data governance, metadata management, data lineage, and slowly changing dimension (SCD) concepts.

• Experience partnering with cross-functional teams to translate business requirements into scalable data solutions.

• Familiarity with financial services regulatory and compliance requirements related to data privacy, audit trails, and data integrity.