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Royal Bank of Canada

Staff Data Engineer

Toronto, ON, M9W 0A4, Canada

Job type: Full-time
Apply by: Nov 6, 2026
Experience: 5+ years of professional experience in data engineering or related roles
Posting language: English
Posted: Oct 1, 2026

Apply About employer

Applications open in a new tab.

Original job 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 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
Experience with data cataloging/metadata management tools (e.g., Unity Catalog, Collibra)

What’s in it for you?

We thrive on the challenge to be our best, progressive thinking to keep growing, and working together to deliver trusted advice to help our clients thrive and communities prosper. We care about each other, reaching our potential, making a difference to our communities, and achieving success that is mutual.

A comprehensive Total Rewards Program including bonuses and flexible benefits, competitive compensation, commissions, and stock where applicable
Leaders who support your development through coaching and managing opportunities
Ability to make a difference and lasting impact
Work in a dynamic, collaborative, progressive, and high-performing team
Flexible work/life balance options
Opportunities to do challenging work
Opportunities to take on progressively greater accountabilities
Access to a variety of job opportunities across business

Job Skills

Big Data Management, Cloud Computing, Database Development, Data Mining, Data Warehousing (DW), ETL Processing, Quality Management, Requirements Analysis, Waterfall Model

Additional Job Details

Address:

RBC CENTRE, 155 WELLINGTON ST W:TORONTO

City:

Toronto

Country:

Canada

Work hours/week:

37.5

Employment Type:

Full time

Platform:

WEALTH MANAGEMENT

Job Type:

Regular

Pay Type:

Salaried

Posted Date:

2026-03-26

Application Deadline:

2026-11-06

Note: Applications will be accepted until 11:59 PM on the day prior to the application deadline date above

Our Employment Opportunities

At RBC, we are guided by living shared values of Client First, Integrity, Collaboration, Respect and Excellence and winning together as One RBC. We believe an inclusive workplace that has diverse perspectives is core to our continued growth as one of the largest and most successful banks in the world. Maintaining a workplace where our employees feel supported to perform at their best, effectively collaborate, drive innovation, and grow professionally helps to bring our Purpose to life and create value for our clients and communities. RBC strives to deliver this through policies and programs intended to foster a workplace based on respect, belonging and opportunity for all.

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Expand your limits and create a new future together at RBC. Find out how we use our passion and drive to enhance the well-being of our clients and communities at jobs.rbc.com.

RBC is presently inviting candidates to apply for this existing vacancy. Applying to this posting allows you to express your interest in this current career opportunity at RBC. Qualified applicants may be contacted to review their resume in more detail.

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More about this job

Responsibilities

Architect and maintain scalable data pipelines and analytical environments to support BI, analytics, and AI/ML workloads. Ensure data integrity through robust quality frameworks, metadata management, and automated testing across development and production environments.

Requirements

Requires a bachelor's degree in a technical field and at least 5 years of professional experience in data engineering. Candidates must possess strong programming skills in Python and SQL, along with deep expertise in modern cloud-native data platforms.

Skills

Python
SQL
Data Engineering
Databricks
Snowflake
Spark
AWS
Azure
GCP
Data Modeling
DevOps
DataOps
CI/CD
Data Governance
Data Quality
Machine Learning

Benefits

Bonuses
Flexible benefits
Competitive compensation
Stock
Flexible work/life balance

Visa sponsorship

Not detected in the job text

Keywords

Data Engineering
Python
SQL
Databricks
Snowflake
Spark
AWS
Azure
GCP
Data Lakehouse
Medallion Architecture
DevOps
DataOps
CI/CD
GitHub
Data Governance
Data Quality
Generative AI
RAG
Vector Databases
Kafka
Terraform
Unity Catalog
Collibra
Wealth Management
Machine Learning
API Development
Data Modeling
Cloud Computing
Infrastructure-as-code

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