Data Engineer to design, develop, and optimize scalable data pipelines

Data Engineer

3 days a week in office

Downtown Toronto

• Collaborate with business teams and Data Analysts to gather and understand requirements, ensuring alignment with business objectives and data needs.
• Translate business needs into detailed technical requirements in collaboration with subject matter experts (SMEs) to ensure accuracy and feasibility.
• Recommend and design scalable, efficient, and effective data architectures and workflows to support current and future business requirements.
• Design, develop, and maintain data assets to enable seamless extraction, transformation, and loading (ETL/ELT) processes from diverse data sources, making data accessible to client-facing applications, data warehouses, and internal tools.
• Implement and enforce best practices for observability, data lineage, and governance, ensuring transparency, reliability, and compliance across all data systems.

Experience

• Professional experience in data engineering, with a strong focus on designing, developing, and optimizing scalable data pipelines, ETL/ELT workflows, and data integration solutions using modern tools and technologies.

Must haves:

• Understanding of data pipeline architecture, modern data stack architecture, and cloud-based platforms, including AWS, Snowflake, and other cloud-native solutions.
• In-depth knowledge and experience with the following tools and concept:

o Data extraction - SQL, Python, API invocation

o Database systems - PostgreSQL, MySQL

o Data storage repositories - SFTP, AWS S3

o ETL/ ELT tools and workflow - Snowflake, PySpark, AWS Glue, AWS Lambda

o CI/CD pipelines - Bitbucket, Git, Jenkins, CloudFormation

• Experience in the Financial Services Industry is an asset.