Build and Optimize Data Pipelines: Design, develop, and enhance scalable ETL/ELT pipelines to migrate, transform, and load large datasets from diverse sources (e.g., databases, APIs, flat files), ensuring seamless integration for analytics, reporting, and AI solutions.

Drive Technical Innovation: Leverage advanced tools and techniques to create reusable, secure, and effective technical solutions that align with business needs and project lifecycle deliverables, including data sharing and governance. Guide users on effective Snowflake utilization, establishing standards for data consumption, storage, and workflow integration while designing and implementing high‑impact stored procedures.

Collaborate Across Teams: Partner with cross‑functional stakeholders to translate data requirements into robust solutions that empower analytics, reporting, AI, and machine learning initiatives.

Strengthen Data Governance: Implement and maintain best practices for metadata management, access controls, and compliance to ensure data integrity and security.

Ensure Performance and Scalability: Monitor system performance, troubleshoot issues, and optimize queries/processes to maximize efficiency and scalability.

Automate and Streamline Workflows: Use Python scripting and orchestration tools (e.g., Create clear technical documentation for processes, architectures, and data models to foster team collaboration and institutional knowledge. designing stored procedures, optimizing queries, data storage/consumption best practices).

Data Pipeline Development

Proficiency in building, optimizing, and maintaining ETL/ELT pipelines for large‑scale data migration and transformation. Programming & Automation Strong scripting skills in Python for automation and tool development. Data Governance & Security Knowledge of implementing data governance practices (metadata management, access controls,



compliance).

Performance Optimization

Skills in monitoring, troubleshooting, and optimizing database/query performance for scalability.

Technical

Collaboration & Communication Ability to guide users/teams on platform best practices and present technical solutions in cross‑functional meetings. DevOps & CI/CD Practices Knowledge of version control (Git), containerization (Docker), or CI/CD pipelines for data engineering workflows.

Experience documenting technical processes, architectures, and data models for team use.

Advanced Data

Modeling star/snowflake schemas) or optimizing data warehouses for analytics 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.

A world‑class training program in financial services. Analytics, Big Data, Big Data Management, Cloud Computing, Collaboration, Critical Thinking, Database Development, Data Engineering, Data Mining, Data Modeling, Data Pipelines, Datasets, Data Warehousing (DW), ETL Processing, ETL Tools, Extract Transform Load (ETL), Git, Group Problem Solving, Quality Management, Requirements Analysis Employment Type: Full time 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. #

📌 Staff, Data Engineer (Global Security) (Toronto)
🏢 Royal Bank of Canada
📍 Toronto