# Staff Data Engineer at RBC

RBC is hiring a Staff Data Engineer in Toronto, Canada. Level rates it Works on AI ●●●○; you can [apply on RBC](https://jobsbylevel.com/go/2bf93c64-41f9-4330-989f-eed99259cedf?utm_medium=ai-agent).

AI Level 3, AI centrality 65 out of 100. TORONTO, Ontario, Canada.

## Details

- Company: [RBC](https://jobsbylevel.com/companies/rbc)
- AI level: AI Level 3 (score 65 out of 100)
- Location: TORONTO, Ontario, Canada
- Posted: October 1, 2026
- Apply: https://jobsbylevel.com/go/2bf93c64-41f9-4330-989f-eed99259cedf?utm_medium=ai-agent
- Free CV review for this job: https://jobsbylevel.com/cv-review?job=staff-data-engineer-at-rbc-3a839e

## Prepare for this job

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## Description

Job Description What 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

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