Scotiabank Data Quality Engineer - Cards Engineering, Toronto

AI Summary

Scotiabank is actively seeking a Data Quality Engineer in Toronto, Ontario, to lead quality assurance efforts within its Cards Engineering team. This role operates within an Agile delivery model, focusing on system reliability and driving automation-first testing strategies across various platforms. Key duties involve designing and executing robust data quality validation for complex ETL pipelines and performing critical SQL-based data analysis. Top requirements for this engineering position include advanced knowledge of banking systems, significant experience with big data platforms, and proficiency in Python for developing data quality automation frameworks.

Role Overview and Responsibilities

As a Data Quality Engineer, this individual will play a pivotal role in ensuring the integrity and reliability of critical financial data within Scotiabank's Cards Engineering team. Operating within an Agile delivery model, the role involves leading and coordinating comprehensive quality assurance efforts for initiatives ranging from small to large-scale, impacting multiple delivery channels and platforms. A core responsibility is to champion system reliability and drive automation-first testing strategies, embedding quality throughout the entire software development lifecycle. This includes assessing the risk and impact of changes across a vast portfolio of over 190 banking applications and processes that support the domestic bank network. The incumbent will lead QA planning and execution across various Agile squads, ensuring alignment with sprint goals and overall release timelines.

A significant aspect of the role involves designing, developing, and executing sophisticated data quality validation for ETL pipelines, spanning both relational and big data platforms. This requires meticulous validation of source-to-target mappings, transformation logic, and complex business rules. The engineer will perform in-depth SQL-based data analysis and reconciliation using technologies such as DB2 and Hive, along with other related data stores. Furthermore, the role necessitates the development and maintenance of Python-based data quality frameworks, validation scripts, and automation tools. Identifying, analyzing, and reporting data quality issues, performing root cause analysis, and collaborating effectively on fixes are central to daily operations. The engineer will also support IST, UAT, and regression testing for data releases, while ensuring strict compliance with data governance, control, and audit requirements, particularly for sensitive financial data. Collaboration with data engineers, business analysts, and stakeholders is essential to clarify data requirements and quality expectations, implementing advanced sampling and full-population validation techniques for large datasets, including hash-based and aggregate reconciliations, CDC verification, and idempotency checks across integrated systems.

Candidate Profile and Expertise

The ideal candidate for this Data Quality Engineer position will bring a strong academic background, with a preferred Bachelor’s degree in Computer Science, Engineering, Information Technology, or a closely related field. Relevant certifications such as ISTQB Certified Tester, Certified Agile Tester (CAT), Certified ScrumMaster (CSM), or equivalent Agile certifications, alongside automation tool certifications (e.g., Selenium, UFT), are also highly valued. Essential work experience includes an advanced understanding of the functional operation of banking applications and interfaces, particularly Credit Card and Debit Card systems.

Key technical expertise encompasses extensive QA experience across various platforms and big data technologies, including EDL, HDFS/Hive, and distributed processing frameworks like Spark. Proficiency in SQL/ETL is critical for complex pipeline validation across diverse sources and targets. Strong automation skills using Python and PySpark are required for developing scalable data quality frameworks. The role demands deep knowledge of data quality principles, including validation, reconciliation, anomaly detection, referential integrity, and the creation of rules and scorecards. While not mandatory, exposure to Mainframe/Legacy systems such as COBOL/EBCDIC is considered an asset. Familiarity with modern ways of working, including Jira/Confluence for project management, CI/CD pipelines (Azure DevOps/Jenkins), and Agile collaboration, is expected. The candidate must be skilled in defect analysis and reliability testing, adept at fault diagnosis and root cause analysis to ensure robust system reliability and resilience. Proficiency in risk-based testing and impact analysis across complex, integrated systems is also crucial. Core competencies include proven leadership and collaboration skills to coordinate cross-functional QA teams effectively, strategic thinking to proactively plan QA and mitigate risks across domestic banking initiatives, and deep expertise in Agile methodologies to adapt to evolving business needs. Strong communication and stakeholder management abilities are vital for aligning on testing objectives and resolving conflicts.