Staff Data Engineer
RBC is hiring a Staff Data Engineer in Toronto, Canada. Level rates it ; you can apply on RBC.
Level's read on this job
- Works on AI ●●●○ is how Level rates this data engineer role: the daily work is on AI products, without building the model.
- Tools and skills the posting cites include Databricks, GitHub and RAG.
- Level classifies it as a lead role.
- Level lists 863 other open roles at RBC.
AI in this role
Architect and maintain robust data foundations and analytical environments supporting downstream BI and AI/ML consumers.
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 ModelAdditional Job Details
Address:
RBC CENTRE, 155 WELLINGTON ST W:TORONTOCity:
TorontoCountry:
CanadaWork hours/week:
37.5Employment Type:
Full timePlatform:
WEALTH MANAGEMENTJob Type:
RegularPay Type:
SalariedPosted Date:
2026-03-26Application Deadline:
2026-11-06Note: 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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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.
How we rate this
Staff Data Engineer at RBC rates 65 out of 100 for how much of the daily work is AI. That makes it Works on AI (AI Level 3 of 4). The level is about AI in the job, not seniority.
Works on AI. The daily work is on AI products, without building the model.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.
Prepare for this job
Built from this posting and from similar jobs on Level.
Skills and AI tools this role asks for
Pay
- Typical data engineer job
- $149k
This posting doesn't list pay. Use the typical data engineer figure when pay comes up. See data engineer jobs
Questions you could be asked
- How would you design a retrieval step so the model answers from real data instead of guessing?
- How do you decide when an AI agent can act on its own versus asking for approval first?
- Tell me about a project where data engineering was part of your work. What did you do?
- Tell me about a project where etl was part of your work. What did you do?
- Tell me about a project where data pipelines was part of your work. What did you do?
Adapt your resume
- List these exact terms on your resume: RAG, AI agents, Data Engineering, ETL, and Data Pipelines. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
- Show where AI is part of your daily process, not a one-off project. This role expects it to be a running habit.
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