Senior Data Engineer, GFT

Royal Bank of Canada•Toronto, ON

5d•Onsite

About The Position

The Retail Credit AI & Digital Transformation (ADT) Program aims to improve AI-enhanced underwriting capabilities, which will drive market-leading client acquisitions and best-in-class depth of client relationships. This program will enable the training and operationalization of new ML and AI models rapidly with improved deployment and maintenance patterns. This project focuses on advancing NextGen compute and engineering capabilities on public cloud and on-prem infrastructure, establishing data pipelines and feature stores with integrated data quality and governance for high-quality, traceable insights, and parameterizing the ML lifecycle to enhance delivery speed with improved controls, monitoring, and guardrails. We value a positive attitude, willingness to learn, open communication, teamwork, and commitment to clean, secure, and well-tested code.

Requirements

A degree in Computer Science, Engineering, Mathematics, Statistics, or a closely related field at the Bachelor's or Master's level.
4+ years of experience in programming, small to large-scale applications with focus on full-stack development.
High-level expertise in programming languages such as Python, PySpark, highly proficient in both data and ML frameworks, such as Spark, Pandas etc.
Experience with Data Engineering/ETL pipelines
Expert in DevOps practices and tools for CI/CD pipelines.
Excellent problem-solving skills and analytical thinking.
Strong communication and collaboration skills.
Champion best practices and in constant pursuit of engineering excellence, automation, and best in class user experience
Experience with Agile development methodologies, including Scrum or Kanban, and the ability to apply these principles to lead the team
Ability to drive technical innovation, including researching new technologies, evaluating technical options, and recommending technical solutions
Excellent communication and collaboration skills, with the ability to work effectively with cross-functional teams
Willingness to learn new technologies and adapt to new challenges

Nice To Haves

Familiarity with containerization using Docker and orchestration using Kubernetes
Experience with cloud platforms, including Amazon Web Services (AWS) or Microsoft Azure
Prior exposure in the Financial Industry, especially supporting top-of-the-house risk analytic functions (such as Market Risk, Credit Risk or Liquidity Risk), in the design and development of the regulatory frameworks.

Responsibilities

Develop a feature store with integrated data quality and governance to ensure high-quality, traceable ML insights.
Design and implement pipelines for feature extraction, transformation, and storage using scalable cloud solutions.
Ensure data consistency, lineage, and metadata management to support regulatory and governance needs.
Collaborate with data scientists to standardize feature definitions and promote reusability across teams.
Implement reusable pipelines and MLOps solutions to optimize machine learning models and other quantitative algorithms life cycle management.
E2E technical competency including conducting data analysis, data preprocessing, and feature engineering to prepare datasets for model training.
Work alongside data scientists, quantitative analysts, software engineers, data engineers, and domain experts to collect requirements and design solutions.

Benefits

bonuses
flexible benefits
competitive compensation
commissions
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
A world-class training program in financial services
Flexible work/life balance options
Opportunities to do challenging work, to take on progressively greater accountabilities, to building close relationships with clients
Access to a variety of job opportunities across business and geographies.