Manager, Data Engineer - Scotiabank, Toronto

AI Summary

This Manager, Data Engineer role at Scotiabank in Toronto, Ontario, focuses on supporting the CBO Performance Enablement department. Key duties include managing the end-to-end data development lifecycle, building reliable data solutions for reporting and performance insights, and supporting underlying data platforms. Candidates require over three years of experience in data engineering, particularly with SQL Server and ETL tools like SSIS and Azure Data Factory.

About the Role at Scotiabank

As a Manager, Data Engineer within Scotiabank's CBO Performance Enablement department, you will play a crucial role in delivering on the team's business strategies and objectives. This position is central to supporting the entire data development and enablement lifecycle, including the vital tasks of sourcing and transforming data. Your expertise will be instrumental in building and maintaining reliable data solutions that effectively enable comprehensive reporting and provide critical performance insights. Furthermore, you will be responsible for supporting the underlying data platforms and infrastructure, ensuring robust and efficient operations. All activities must strictly adhere to governing regulations, internal policies, and established standards, mitigating operational risk and ensuring regulatory compliance.

You will champion a customer-focused culture, leveraging broader Bank relationships and systems. A significant part of this role involves designing, building, and maintaining scalable data architectures and pipelines using SQL Server, both on-premise and in private cloud environments. This supports periodic data integration, transformation, validation, and enablement of operational and performance reporting, always aligning with data management principles and technology strategy. Working within an Agile environment, you will adapt to evolving business and technical requirements, supporting data-driven decision making by enabling timely, well-structured, and trusted data for downstream analytics and reporting teams. Identifying opportunities to automate manual data processes and driving continuous improvement in efficiency and reliability through engineering best practices is also a key expectation. Your involvement will span all phases of data solution delivery, including data profiling, ingestion, transformation, modelling, cleansing, and aggregation, establishing a strong foundation for performance insights.

Skills and Experience for Success

To succeed in this Manager, Data Engineer position, candidates typically possess a university degree in Computer Science, Engineering, or a related field, or equivalent practical experience. A minimum of three years of hands-on experience in data engineering, business intelligence, or analytics engineering roles is essential, specifically supporting reporting and performance enablement use cases. Strong experience in designing, developing, and maintaining data solutions using Microsoft SQL Server across both on-premise and private cloud environments is a core requirement. Proven experience with ETL and data integration development, utilizing tools such as SQL Server Integration Services (SSIS) and Azure Data Factory (ADF) to build scheduled, reliable, and scalable data pipelines, is also critical.

Furthermore, experience working with Power BI, including developing and supporting semantic models, datasets, and reports in collaboration with analytics and business stakeholders, is highly valued. A solid understanding of dimensional and relational data modelling, data quality checks, data validation, and performance optimization techniques is fundamental. The role also requires experience developing and supporting SQL Server Reporting Services (SSRS) and SQL Server Analysis Services (SSAS) solutions. Candidates should be comfortable working across the end-to-end data lifecycle, including data sourcing, transformation, validation, and the enablement of downstream reporting and analytics. Experience supporting production data processes, including monitoring, issue triage, root-cause analysis, and implementing controls and guardrails to ensure data reliability, is imperative. Exposure to Python and related data libraries is considered a beneficial asset for this dynamic role within Scotiabank.