Senior Data Engineer - Scotiabank, Toronto

AI Summary

The Senior Data Engineer at Scotiabank in Toronto, Ontario, will develop and implement technical solutions for Group Treasury, focusing on building reusable data assets. Key duties include designing ETL/ELT pipelines using Azure Data Factory and BigQuery, and modernizing legacy financial data systems to cloud-native architectures on Azure and GCP. This role requires over five years of data engineering experience with Microsoft Azure and GCP data tools, alongside strong proficiency in SQL Server and financial data domains.

About the Role and Team

Join Scotiabank's Global Risk and Treasury Technology team, a purpose-driven unit committed to delivering results within an inclusive and high-performing culture. This team provides essential development and support services for Group Treasury, encompassing structural interest rate risk management, asset/liability management, funds transfer pricing, liquidity risk, and stress testing. Additionally, it supports Global Risk Management, covering global retail, corporate, commercial credit risk, counterparty credit risk, and operational/non-operational risk management. As a Senior Data Engineer, you will be instrumental in developing and implementing robust technical solutions to support the Group Treasury business, with a particular focus on advanced Data Engineering practices to build reusable data assets for the entire group and the bank. Your role involves active participation in multiple projects, contributing to technical design, use cases, and process workflow for complex business processes serving internal clients. You will design, develop, and optimize sophisticated ETL/ELT pipelines using leading tools such as Azure Data Factory, SQL Server Integration Services (SSIS), and BigQuery. Furthermore, you will build and maintain critical data workflows across Azure Synapse Analytics, Azure Data Lake, and GCP BigQuery, ensuring seamless integration of structured and semi-structured financial data sources. A key responsibility will be to lead the modernization of legacy financial data systems, transitioning them to cutting-edge cloud-native architectures on Azure and GCP, thereby enhancing efficiency and scalability. You will also develop scalable data models and transformation logic crucial for supporting finance, treasury, and regulatory reporting requirements. Implementing robust data governance, lineage, and quality frameworks across multi-cloud environments will be paramount to ensure data integrity and compliance. Collaboration with finance, treasury, and risk teams will be essential to translate complex data requirements into effective technical solutions, ensuring adherence to financial data standards, including auditability, traceability, and regulatory controls. This technical leadership role also involves mentoring junior engineers and promoting best practices in data engineering, DevOps, and CI/CD.

What You Will Bring to Succeed

To excel in this Senior Data Engineer position at Scotiabank, candidates are expected to possess a Bachelor's degree in a technical field such as Computer Science, Information Systems, Computer Engineering, or Data Science. A minimum of five years of progressive experience in data engineering is required, with significant hands-on experience across Microsoft Azure and GCP data tools. Strong proficiency in SQL Server, T-SQL, SSIS, and Azure Data Factory is essential for managing and optimizing data pipelines. Experience with BigQuery, Cloud Storage, and Dataflow or Apache Beam will be highly valued. A solid understanding of diverse financial data domains, including general ledger, cash management, liquidity, regulatory reporting, and risk analytics, is critical for success in this banking environment. Prior experience working within or alongside financial institutions, banks, or treasury departments will provide a distinct advantage. Familiarity with CI/CD tools such as Azure DevOps, GitHub Actions, and Cloud Build, along with infrastructure-as-code principles like Terraform, demonstrates a commitment to modern development practices. Beyond technical expertise, candidates must exhibit excellent problem-solving and analytical skills, coupled with the ability to effectively manage multiple concurrent tasks and initiatives. Strong interpersonal and verbal communication skills are necessary to collaborate effectively with peers and business stakeholders, translating complex technical details into understandable insights. Preferred qualifications include experience with Databricks, Delta Lake, MLflow, and Apache Airflow, as well as knowledge of visualization tools like Power BI or Looker. Relevant certifications, such as Azure Data Engineer Associate or Google Professional Data Engineer, are also highly regarded. Experience with financial compliance standards, including SOX, Basel, and IFRS, and working within Agile/Scrum environments will further enhance your candidacy for this challenging and rewarding technical role.