Gestionnaire de portefeuille

About

RBC Marchés des Capitaux is developing scalable customer analytics and AI capabilities globally to enhance customer service outcomes within Global Markets and Services. This role is central to this journey, supporting the machine learning and data foundations that make today's analytics products robust and accelerate the performance of tomorrow's AI and machine learning products.

You will be:

• The high-performing portfolio manager responsible for the customer analytics and AI platform foundations (analytics products, pipelines, operating models), under the direction of the Senior Program Lead.
• An active builder capable of elevating the design maturity of multiple initiatives while ensuring their delivery.
• A partner to the business unit stakeholders who can translate business needs into scalable, maintainable data engineering and machine learning solutions with appropriate controls.

Responsibilities

• Support the development and continuous improvement of the Databricks program's data engineering foundations (data pipelines, analytics products, orchestration, performance, cost management) that power numerous analytics and AI initiatives.
• Review and normalize existing analytics assets to make them AI and machine learning-ready foundations (clean linkages, high entity resolution, feature and label-ready tables, time-aligned datasets, reusable libraries).
• Establish pragmatic, operational machine learning operating models (centralized model and service delivery approach, MLflow/registry workflows, deployment standards) so that modeling can be tested as the roadmap evolves.
• Overall execution of priority data and engineering workstreams, ensuring coordination across Technology, Cloud Platform, and stakeholder teams to ensure solutions are production-ready and reusable.
• Collaborate with business unit stakeholders to document requirements, manage expectations, ensure product adoption, and leverage historical point-in-time designs.
• Establish appropriate control discipline from day one: data access and entitlements, handling sensitive client intelligence, and required documentation and approvals for production in a regulated environment.
• Contribute to roadmap planning with the Senior Program Lead: identify bottlenecks, propose platform foundations for next steps, and organize work to maximize composability reuse across initiatives.
• As part of this role, you will communicate and interact frequently with partners and colleagues in Canada or globally.

Requirements

• Undergraduate degree in Computer Science, Engineering, Mathematics, Statistics, or a related field (or equivalent practical experience).
• Proven ability to solve complex business situations and deliver reliable data products (data modeling, pipeline design, orchestration, testing, documentation) to demanding stakeholders.
• Familiarity with distributed computing concepts or frameworks like Apache Spark in production environments.
• Hands-on familiarity with machine learning engineering and machine learning operations concepts (model lifecycle, registries, deployment patterns, reproducible training, feature pipelines).
• Strong software engineering fundamentals (Python, SQL, modular code, code review discipline, CI/CD approach) and a demonstrated ability to elevate engineering quality within the team.
• Strong stakeholder management skills: ability to engage with business and control partners, professionally manage pushback and prioritization, and ensure delivery always aligns with business outcomes.

Preferred Qualifications

• Familiarity with Capital Markets domain (coverage workflows, portfolio/equity concepts, corporate access, sponsor coverage, client engagement).
• Strong hands-on experience in modern data engineering and "warehouse-feature" modeling; experience with Databricks and Snowflake is an asset.
• Experience with entity resolution, relational or graph data structures, or event/trigger-based data products.
• Experience implementing access control and entitlement models, and working on sensitive datasets (client communications, relationship management data) in regulated environments.
• Experience with MLflow, feature stores, serving patterns, or building shared machine learning libraries for multiple teams.

Benefits

• Global compensation program including bonuses and à la carte benefits, competitive pay, commissions and stock, if applicable.
• Management-supported development through coaching and management opportunities.
• Opportunity to make a significant contribution and have a lasting impact.
• Work within a dynamic, high-performing team focused on innovation and collaboration.
• Flexible work-life balance options.