Job DescriptionWhat is the Opportunity?You will be responsible for maintaining existing architecture, as well as designing and applying proper data architecture to optimize the ability to access and leverage data. You will maintain and create data pipelines that transform raw data, stored in a wide range of repositories, into useable datasets to enable a talented team of data scientists and data analysts to work efficiently. You will collaborate with internal stakeholders as well as Fraud IT to translate business requirements into technical designs that achieve the goals established in our data strategy.
What will you do?- Oversee the maintenance of a robust analytics environment, and maintain a mix of big and small data pipelines
- Set up and maintain monitoring jobs to ensure the health of the system is robust, investigate and troubleshoot pipeline failures and data quality issues, escalating complex issues to senior engineers as needed
- Manage, optimize, oversee and monitor data pipelines, backfill any missing data and assist in the development of new data pipelines which will enable Data Science & Analytics colleagues to conduct experiments, insights and analytics work more efficiently
- With the guidance of Senior Engineers, create data pipelines that transform raw data into useable datasets, working with data in HDFS, object storage services (i.e. S3), ElasticSearch, traditional relational databases
- Collaborate with DSA teams to gather requirements for a wide range of analytics use cases, identify opportunities for reuse and leverage expertise to offer alternative solutions
- Manage curated data sets that act as building blocks for multiple analytic pursuits and develop automated job scheduling tasks to ensure execution is resilient
- Perform unit testing and validation of new pipelines before production deployment
- Partner with project teams to seamlessly integrate new data elements into data pipelines
What must you have to succeed?Must-haves:- 2+ years' practical data engineering experience
- Experience using Hadoop/Hive/Spark/Scala or other big-data platform technology/tools
- Strong coding skills (Python, PySpark, SQL, etc.)
- Strong understanding of version control (Git/GitHub)
- Effective communication and collaboration skills
- Exceptional time management and organizational skills and ability to manage multiple projects simultaneously
Nice-to-have:- Graduate degree in a quantitative discipline
- Experience with Docker and Kubernetes, data governance tools (i.e. Collibra) or Cloud technologies (Azure, AWS, OpenShift)
- Strong problem solving, research and quantitative skills
What's in it for you?- Competitive Compensation
- A professional and supportive team environment
- A comprehensive training program on internal processes and systems
- Career progression if you have what it takes to be the best
Job SkillsBig Data Management, Cloud Computing, Database Development, Data Engineering, Data Mining, Data Warehousing (DW), ETL Processing, Group Problem Solving, PySpark, Python (Programming Language), Quality Management, Requirements Analysis
Additional Job DetailsAddress:YORK MILLS CENTRE, 36 YORK MILLS RD:TORONTO
City:Toronto
Country:Canada
Work hours/week:37.5
Employment Type:Full time
Platform:PERSONAL & COMMERCIAL BANKING
Job Type:Regular
Pay Type:Salaried
Posted Date:2026-07-13
Application Deadline:2026-07-24
Note: Applications will be accepted until 11:59 PM on the day prior to the application deadline date above
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.
