Analytics Engineer, Global Analytics Operations
Scotiabank is an international bank offering personal, business and commercial banking, wealth management, private banking, corporate and investment banking, and capital markets services. Its clients include individuals, businesses, corporations, institutions and investors across Canada, the United States, Mexico, Latin America, Europe and Asia Pacific.
About Scotiabank
Scotiabank, also known as The Bank of Nova Scotia, provides a broad range of financial products and services, including deposit accounts, credit cards, lending, mortgages, insurance, investments, wealth management, private banking, corporate and investment banking, capital markets, foreign exchange, financing, advisory services and transaction banking. The organization serves personal banking customers, small and mid-sized businesses, commercial clients, corporate and institutional clients, and investors through its global network.
Skills
Candidate Availability
Required and preferred rules are kept separate and reflect the wording in the original posting.
About the Role
You support operational excellence by monitoring data platforms, managing incidents, handling user requests, and improving processes. You build and maintain scalable data models and ETL/ELT pipelines, ensure data quality through testing and monitoring, support analysts and data scientists with reliable data, deliver actionable insights, and apply governance, security, and documentation standards.
Requirements
- Bachelor’s or graduate degree in computer science, engineering, or a related field, or equivalent experience
- Advanced to expert SQL skills
- Python experience
- ETL/ELT pipeline development experience
- Hands-on experience with GCP, AWS, or Azure
- Experience with modern data stack tools including dbt
- Experience with Apache Airflow or other workflow orchestration tools
- Understanding of data warehousing and dimensional modeling
- Knowledge of banking or finance and data governance practices
Responsibilities
- Monitor data platforms and manage incidents
- Handle user requests and data inquiries
- Identify gaps and implement scalable improvements
- Automate workflows and introduce analytical patterns
- Support analysts and data scientists with reliable data
- Partner with business teams to understand data needs
- Build and maintain scalable data models and ETL/ELT pipelines
- Maintain data quality through automated testing and monitoring
- Resolve data issues proactively
- Apply data governance, security, and documentation standards