Lead Data Modeler & Analyst

Bridgenext Digital Engineering•Toronto, ON

8d•Hybrid

About The Position

Bridgenext is seeking a Lead Data Modeler & Analyst with demonstrated experience in Capital Markets, Financial Services, Asset Management, or Wealth Management to support enterprise‑scale data initiatives. The role requires hands‑on experience working with financial data domains, industry‑specific data models, and core business and operational processes common to investment, trading, and asset/wealth management environments, combined with strong data modeling expertise to design and govern the underlying data structures. The Lead Data Modeler & Analyst will lead requirements workshops, design and maintain Conceptual, Logical, and Physical data models for the Investment Data Lake, perform hands‑on SQL‑based data profiling and validation, create and maintain data documentation artifacts, and own UAT and post‑production validation to ensure delivered data assets meet business, operational, and regulatory expectations typical of financial services organizations.

Requirements

Bachelor’s degree in Computer Science, Information Systems, or related field
8–12 years of IT experience with strong focus on enterprise data programs (Data Lake / Data Warehouse / BI)
Minimum 4+ years of hands-on Data Modeling experience designing and maintaining Conceptual, Logical, and Physical data models for large-scale data platforms, with strong command of modeling fundamentals (entities, relationships, keys, constraints, normalization/denormalization)
Solid experience with business intelligence and data warehousing development technologies, working across RDBMS, Hadoop, and analytics environments
Strong data analysis and data modeling skills with proven ability to translate business requirements into robust data structures and documentation
Hands-on SQL expertise for data profiling, validation, reconciliation, and issue investigation
Hands-on experience with enterprise data modeling tools such as PowerDesigner and/or Erwin
Strong experience leading requirements gathering and UAT for data warehouse/data lake initiatives, especially around data ingestion
Excellent written and verbal communication skills; able to articulate complex data concepts to both business and technical audiences
Strong work ethic, results orientation, and high attention to detail/accuracy

Nice To Haves

Familiarity with Capital Markets data and domains (reference data, instruments, trades, positions/holdings, pricing, corporate actions)
Experience with Azure data solutions (ADLS / ADF / Synapse / Databricks) and cloud-based analytics platforms
Experience presenting complex data in simple, intuitive formats for business consumption (semantic layers, curated datasets, BI/reporting views)
Strong team and individual player
Maintains composure during all types of situations and is collaborative by nature
High standards of professionalism, consistently producing high quality results
Self-sufficient, independent requiring very little supervision or intervention
Demonstrate flexibility and openness to bring creative solutions to address issues

Responsibilities

Support the data strategy for the Investment Data Lake and drive the build-out of high-quality, business-aligned data assets.
Lead requirements workshops to gather, analyze, and document business/data requirements for enterprise Data Lake / Data Warehouse initiatives; obtain stakeholder sign-off.
Perform hands-on data analysis and discovery to understand source systems, business processes, and downstream reporting/analytics needs.
Create, maintain, and govern Conceptual, Logical, and Physical Data Models for the Investment Data Lake / DW, ensuring completeness, consistency, and scalability.
Apply strong data modeling fundamentals (entities, relationships, keys, constraints, normalization/denormalization) to enable performant and reusable data structures.
Partner with architects and engineers to translate models into implementable designs for RDBMS, Hadoop, and analytics environments, aligned to BI and data warehousing best practices.
Drive and maintain data documentation artifacts including business requirements, source-to-target mappings, data dictionaries, KPI/metric definitions, and data flow/lineage documentation.
Perform hands-on SQL-based profiling, validation, and reconciliation across ingestion and transformation layers to ensure data quality and delivery readiness.
Liaise with multiple stakeholders (Business, Data Engineering, QA, Governance) to resolve data gaps, clarify business rules, and align on common definitions.
Own UAT end-to-end: define acceptance criteria and test scenarios, coordinate execution with business users, triage/track defects, and secure formal sign-off.
Perform post-production data validation to confirm accuracy, completeness, and usability of delivered data assets.