Senior Data Engineer

RBC•Toronto, ON

11d•Onsite

About The Position

We’re looking for an experienced Senior Data Platform Engineer who will bring focus and subject-matter expertise around designing and implementing data platform infrastructure and automation tools (CI/CD and DevOps). This is a unique opportunity to grow in the world of data platform infrastructure and work with a team of passionate individuals committed to the mission of bringing data to enterprise. At RBC Borealis, you’ll be joining a team that works directly with leading researchers in machine learning, has access to rich and massive datasets, and offers the computational resources to support ongoing development in areas such as reinforcement learning, unsupervised learning and computer vision. You can find out more about our research areas at rbcborealis.com. RBC Borealis is the driving force behind Royal Bank of Canada’s AI and data innovation. As part of Canada’s largest financial institution, we bring together a team of architects, engineers, scientists, and product experts on a mission to revolutionize finance through world-class research, solutions, and a resilient data platform. With locations across Toronto, Waterloo, Montreal, Calgary, and Vancouver, we’re at the forefront of AI research and platform development. With a focus on cutting-edge research in areas like time series forecasting, causal machine learning, and responsible AI, we are seamlessly integrating AI research and data engineering, to solve critical challenges in the financial industry. We are building intelligent, and scalable, data-driven solutions that will help communities thrive and drive innovation for our customers across the bank.

Requirements

Strong and relevant experience designing and implementing distributed systems and data platform systems
Working with building and maintaining DevOps pipeline such as Jenkins, GitHub actions
Previous experience with orchestration tools such as AirFlow, KubeFlow, Dagster, or Temporal
In-depth knowledge of technologies and tools like Apache Iceberg, Trino, Spark, Hadoop
Experience with building tools and applications to automate various infrastructure and DevOps tasks
Proficiency with programming languages such as Python, Bash, or JavaScript
Solid experience and understanding of the UNIX operating system
Implementing monitoring solutions to identify system bottlenecks and production issues
Knowledge of professional software engineering best practices for the full software development life cycle, including testing methods, coding standards, code reviews and source control management
Hands-on experience building and deploying hybrid environments on-prem and major cloud environments, such as AWS and Azure
Independently resolving data engineering related problems by identifying areas for improvement and implementing solutions that enhance efficiency within scope of responsibility

Responsibilities

Designing, building, and optimizing data platform deployment tools and automation systems that operate the business’s data and applications
Designing and implementing best practices and standards for data and pipelines across the organization
Collaborating with engineers, and researchers to automate code analysis, build, integration and deployment of data platform applications
Supporting applications and projects with infrastructure design decision, and monitoring solution
Building highly scalable, resilient cloud and on-premises systems for hosting data platform systems using state-of-the-art technologies