Staff ML Data Engineer

RBC • Toronto, ON

1d • Onsite

About The Position

RBC Wealth Management (WM) Data & AI is responsible for driving data driven decision making end-to-end across our global businesses. We’re modernizing our data architecture, building out BI and analytics capabilities, and developing cutting-edge AI/ML solutions—from traditional machine learning to Generative AI – to support our Global Executive Leadership as well as our Digital and Global Investment teams. We’re looking for a ML Data Engineer who can bridge the worlds of ML and traditional data engineering. You’ll be the connective tissue for our AI and BI/analytics teams – equally comfortable preparing feature stores, training datasets, and ML-ready infrastructure as you are building analytics-ready data pipelines. Your work will power enhancements in our investment and portfolio management capabilities, transformational improvements in our operations that drive efficiency and elevate client experience, and the insights and analyses that support strategic decision making for our Global Executive Leadership. This is a unique opportunity to support work that is defining the future of our business, while operating across the full spectrum of modern data engineering.

Requirements

• Bachelor’s degree in Computer Science, Software Engineering, Data Engineering, or a related technical field
• 5+ years of professional experience in ML data engineering or related roles
• Experience with ML tooling and the ML data lifecycle, including feature engineering, feature stores, training data preparation, and model integration/deployment support
• Strong programming skills in Python and SQL, with hands-on experience building and optimizing MCPs, APIs, and data pipelines at scale
• Deep experience with modern data platforms and tools such as Databricks, Snowflake, Spark, and cloud-native data services (AWS, Azure, or GCP)
• Familiarity with Generative AI workflows, including RAG pipelines, vector databases, and LLM serving infrastructure
• Solid understanding of data warehousing, data lakehouse architectures, and data modelling concepts—and when to apply each
• Proficiency with DevOps/DataOps practices and CI/CD tooling (e.g., GitHub Actions, Jenkins, Azure DevOps) for data and ML pipelines
• Strong experience with data governance, data quality, and data security best practices
• Passion for problem-solving and tackling challenges in real-world contexts, leveraging large-scale datasets and modern data and AI approaches
• Excellent communication and collaboration skills, with the ability to work effectively across AI scientists, BI analysts, technology teams, and business stakeholders

Nice To Haves

• Knowledge of streaming data technologies (e.g., Kafka, Kinesis, or Spark Streaming)
• Experience with infrastructure-as-code tools such as Terraform or CloudFormation
• Knowledge of financial services, wealth management, or investment management domains
• Experience building or enabling AI-driven automation such as AI agents, workflow orchestration, or decision engines

Responsibilities

• Architect, establish, maintain, and evolve the data foundation and analytical environments that will support both AI/ML and BI/analytics workloads with high performance and reliability
• Build and maintain feature stores, training datasets, and ML-ready data infrastructure that enable our AI/ML scientists and engineers to develop and deploy models efficiently
• Develop and optimize data pipelines for the full ML lifecycle—from data ingestion and feature engineering through to model serving, monitoring, and continuous improvement
• Build and implement integrations that connect AI/ML capabilities to production systems and business workflows, leveraging modern integration patterns such as Model Context Protocols (MCPs) and APIs
• Design, build, and maintain scalable, production-grade data pipelines that ingest, transform, and serve both structured and unstructured data across WM LoBs
• Implement monitoring, logging, and observability for data pipelines and ML models to ensure reliability, performance, and compliance
• Create, maintain, and develop data assets across Dev, UAT, and Production environments, ensuring consistency across
• Identify, source, stage, and model improvements to partially/completely automate the most common, repeatable and tedious manual data preparation and integration tasks, and optimize data delivery for greater scalability, as part of the end-to-end data lifecycle
• Implement data quality frameworks, automated testing, and monitoring to ensure data integrity and trust across all downstream consumers
• Collaborate with business stakeholders to translate data requirements into robust engineering solutions that power reporting, dashboards, analytics, and AI solutions
• Support DevOps and DataOps best practices, including CI/CD, infrastructure-as-code, and automated testing across data and ML pipelines
• Actively contribute to our data governance and security posture, ensuring compliance standards are met across all data activities
• Operate within an Agile framework, collaborating effectively in sprints, standups, and cross-functional team ceremonies
• Stay current with emerging data and AI engineering technologies, evaluate new tools and approaches, and share knowledge with the broader team

Benefits

• Flexible work/life balance options
• Access to a variety of job opportunities across business
• A comprehensive Total Rewards Program including competitive compensation, bonuses, flexible benefits, and stock where applicable

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level