Job Description

SUMMARY:
Join this dynamic and innovative team as a Senior Data Engineer at one of the Big 4 Banks!POSITION INFO:
We are looking for a highly skilled professional to lead and drive our client's data quality, , and operations projects. Your expertise will be pivotal in enhancing their credit and fraud risk frameworks through cutting-edge data management, modelling, and integration techniques.Responsibilities:

• Data Quality and Data Management:

• Ensure the integrity, accuracy, and consistency of data across the organization.
• Develop and implement data quality frameworks and processes.
• Perform regular data audits and remediation activities.

• Data Modeling and Integration:

• Design and maintain robust data models to support various business needs.
• Integrate data from diverse sources, ensuring seamless data flow and accessibility.
• Optimize data architecture for performance and scalability.

• Cloud and Data Virtualization:

• Leverage cloud platforms for efficient data storage, processing, and analysis.
• Implement data virtualization solutions to enhance data accessibility and real-time analytics.
• Ensure data security and compliance with regulatory standards.
• Collaboration and Team Dynamics:

• Collaborate with cross-functional teams to understand and address data requirements.
• Mentor junior team members and promote knowledge-sharing within the team.
• Foster a culture of innovation, continuous improvement, and collaboration.

Education:

• Bachelor's Degree in Information Technology / Business Commerce / or related field
• Certification(s) in MS SQL / Data Lake technologies and databases / Cloud-related data engineering tooling

Job Experience and Skills Required:

• Minimum of 8 years of relevant experience in data engineering, data quality, or related fields
• Advanced proficiency in SQL, SAS, and Python/Apache Spark
• Hands-on experience with Power BI, Qlik Sense, and Denodo
• Proven track record in building and optimizing data pipelines in financial credit and/or fraud risk environments
• Experience with DataOps and MLOps frameworks
• Familiarity with cloud platforms such as AWS, Azure, or Google Cloud
• Excellent problem-solving skills and attention to detail
• Strong communication and interpersonal skills

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