Connect machine learning and BI through Python, SQL, and modern data platforms at RBC Wealth Management’s Toronto-based Data & AI organization. As a Machine Learning Data Engineer, you will support the full ML data lifecycle across global Wealth Management businesses. The work includes feature engineering, training data preparation, model integration, and scalable pipeline development. You will also translate stakeholder requirements into engineering solutions for reporting, dashboards, analytics, and AI. Key Responsibilities:
Design scalable pipelines for structured and unstructured data Prepare datasets and features for AI/ML scientists Support model deployment, monitoring, and continuous improvement Translate business requirements into robust engineering solutions Implement data quality testing and observability frameworks Requirements:
Five-plus years of professional ML data engineering experience Python and SQL programming skills at scale Understanding of warehousing, lakehouse, and data modelling Experience with DevOps, DataOps, CI/CD, and automated testing Strong data governance, quality, security, and collaboration skills Bring machine learning lifecycle knowledge, pipeline expertise, and stakeholder communication skills to RBC’s Wealth Management Data & AI team.
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Machine Learning Data Engineer RBC in Toronto employer: ODAIA
RBC is an exceptional employer that fosters a culture of innovation and collaboration, making it an ideal place for professionals passionate about AI and banking. With a commitment to employee growth, RBC offers extensive training programs and career advancement opportunities, all while providing a supportive work environment in the heart of Canada’s financial district. Join us to be part of a forward-thinking team that values your contributions and empowers you to make a meaningful impact on the future of banking.
