What is the prospect?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.

Job DescriptionWhat is the opportunity?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.

What will you do?

- Architect, establish, maintain, and evolve the data foundation and analytical environments that support BI/analytics workloads and downstream AI/ML consumers with high performance and reliability
- Design, build, and maintain scalable, production-grade data pipelines that ingest, transform, and serve both structured and unstructured data across WM LoBs
- Design data models and schemas that serve multiple consumer types—BI dashboards, reporting, and downstream ML pipelines—balancing performance, flexibility, and reusability
- Build and maintain APIs and integrations that expose data to downstream consumers, including AI/ML systems and business workflows
- Define clean data contracts and interfaces with downstream AI/ML and BI teams—schemas, SLAs, and freshness guarantees—to ensure reliable handoffs
- Implement monitoring, logging,



and observability for data pipelines to ensure reliability, performance, and compliance
- Optimize pipeline and storage performance and cost (e.g., query tuning, partitioning, compute/storage cost management) across Dev, UAT, and Production environments
- Create, maintain, and develop data assets across Dev, UAT, and Production environments, ensuring consistency
- 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
- Support data cataloging and metadata management practices to ensure data assets are discoverable, well-documented, and governed
- Support DevOps and DataOps best practices, including CI/CD, infrastructure-as-code, and automated testing across data pipelines
- Use version control systems (e.g., GitHub) and data/schema comparison tooling to manage code and data changes safely across environments

What do you need to succeed?

Must-have

- Bachelor's degree in Computer Science, Software Engineering, Data Engineering, or a related technical field




- 5+ years of professional experience in data engineering or related roles
- Strong programming skills in Python and SQL, with hands‑on experience building and optimizing 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)
- Solid understanding of data warehousing, data lakehouse architectures, and data modelling concepts—and when to apply each
- Knowledge of and experience implementing medallion (bronze/silver/gold) architecture patterns for structuring data lakehouse layers
- Experience designing data models and pipelines that serve multiple consumer types (e.g., BI/reporting and downstream ML use cases)
- Proficiency with DevOps/DataOps practices and CI/CD tooling (e.g., GitHub Actions, Jenkins, Azure DevOps) for data pipelines
- Experience with automated unit testing methodologies for data pipelines and transformations
- 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 approaches

Nice-to-have

- Familiarity with Generative AI workflows, including RAG pipelines, vector databases, and LLM serving infrastructure
- 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 orchestrati

📌 Staff Data Engineer (Toronto)
🏢 RBC
📍 Toronto