Associate Director - Lead Data Modeler/Analyst

Royal Bank of Canada•Toronto, ON

11d•Onsite

About The Position

The Trading & Execution Services (TES) Data Services team within Capital Markets Technology is seeking an Associate Director - Lead Data Modeler/Analyst to lead the design and delivery of enterprise-grade data models across their cloud data lake. This is a senior leadership position requiring both technical depth and business acumen. The role involves owning the end-to-end data modeling strategy, building and managing a team of data analyst professionals, and acting as a liaison between front office stakeholders and backend engineering teams. The ideal candidate is a self-driven leader who can operate independently, engage confidently with senior stakeholders, and deliver results in a complex, multi-asset trading environment. The team is focused on ingesting and managing cross-asset trading data on a cloud data lake, consolidating pre-trade data from internal and external sources to deliver high-quality, standardized data products that support front-office decision-making. They are building a world-class data organization leveraging AI and modern cloud-native architectures.

Requirements

Bachelor’s degree in computer science, Software Engineering, Information Systems, Mathematics, or related field
10+ years of progressive experience in data architecture, data modeling, and data strategy roles
6+ years working in capital markets with hands-on exposure to multiple asset classes (equities, fixed income, derivatives, FX, commodities) & deep understanding of capital markets trading workflows, trade lifecycle, and market data structures
Demonstrated people management experience with a track record of building and developing high-performing teams
Proven ability to operate at a senior level, influencing decisions and managing relationships with executive stakeholders
Expert proficiency in data modeling methodologies (conceptual, logical, physical) and experience designing data architectures for cloud-native data lakes and lakehouses at enterprise scale
Hands-on experience with industry-standard communication protocols like FIX (Financial Information Exchange).
Advanced SQL and Python skills for data analysis, profiling, and transformation
Solid understanding of ETL/ELT patterns, data pipeline orchestration, and real-time data ingestion
Working experience with using AI tools to speed up data analysis & us AI for productivity improvement in space of data analysis, modelling & mapping generation.

Nice To Haves

Familiarity with data from major financial ISVs and market data providers
Demonstrated experience using AI/ML techniques for data analysis, data quality assessment, or automated model generation. Prior hands-on use of generative AI tools (e.g., Copilot or similar) applied to data engineering or modeling workflows
Knowledge of regulatory data requirements (MiFID II, CFTC, Dodd-Frank) and their impact on data architecture
Experience with data visualization and BI tools (Power BI, Tableau) for building executive-facing data products

Responsibilities

Analyze cross-asset data from internal RBC systems and external ISV sources to understand data lineage, relationships, and gaps.
Design and implement standardized, scalable data models that serve as the foundation for a unified presentation layer organized by asset class and product.
Engage early in project lifecycles to conduct thorough data gap analysis and identify quality issues before they propagate downstream.
Partner with upstream source teams to define data contracts, track remediation efforts, and ensure sustained data quality improvements.
Liaise with adjacent teams to diagnose, adjust, and resolve data system issues across the trading data ecosystem.
Leverage generative AI tools and techniques as the primary methodology for data exploration, profiling, pattern detection, and model prototyping.
Coach and mentor direct reports to adopt AI-first workflows in their daily analysis and modeling activities.
Drive rapid proof-of-concepts using cloud data lake resources to demonstrate value and validate approaches for front office requirements.
Contribute to the broader data strategy for the QTS division and influence architectural decisions across the platform.
Liaise with external stakeholders & ISV to establish data connectivity with RBC for exchange of data & get sample data for analysis & work with them to remediate data gaps with respect to RBC requirements.