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Toronto, Ontario, Canada
Actively hiring, first seen by us 2 days agoDesign and maintain scalable production data pipelines for structured and unstructured data
Design data models serving BI reporting and downstream ML pipelines
Build APIs and integrations that expose data to AI/ML systems and business workflows
Define data contracts with AI/ML and BI teams covering schemas, SLAs and freshness
Implement data quality frameworks, automated testing and pipeline monitoring
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
Implement data quality frameworks, automated testing, and monitoring to ensure data integrity and trust across all downstream consumers
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
Actively hiringFirst seen by us 2 days ago, still listed, and not a repost of an earlier posting.
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Day 2
Early. Similar jobs usually come down between day 5 and day 54.
From 10,733 similar postings that came down. Coming down is not always a hire.
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- 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)
- 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
- 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
- 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 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
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We file this job under Data Engineer, by the work the posting describes.
Data engineers build and run the pipelines that copy data out of apps and outside services into a warehouse or lake, and fix them when a source changes or a load fails.
Expect SQL, Python, a cloud platform, a scheduler such as Airflow, and often tools for large data such as Spark or Kafka.
An analytics engineer works one step later, turning the raw data a data engineer lands into clean tables people can query.
The government has no code for data engineers, so these figures are for the closest group, database architects, which includes data integration and data warehousing specialists.
Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-1243.
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?
What do you need to succeed?
Must-have
Nice-to-have
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.
Job Skills
Big Data Management, Cloud Computing, Database Development, Data Mining, Data Warehousing (DW), ETL Processing, Quality Management, Requirements Analysis, Waterfall ModelAdditional Job Details
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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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Toronto, Ontario, Canada
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