Data Engineer II - CPB Data Curation

Work Location: Toronto, Ontario, Canada

Hours: 37.5

Line of Business: Technology Solutions

Pay Details: $81,600 - $115,200 CAD

TD is committed to providing fair and equitable compensation opportunities to all colleagues. Growth opportunities and skill development are defining features of the colleague experience at TD. Our compensation policies and practices have been designed to allow colleagues to progress through the salary range over time as they progress in their role. The base pay actually offered may vary based upon the candidate's skills and experience, job-related knowledge, geographic location, and other specific business and organizational needs.

As a candidate, you are encouraged to ask compensation related questions and have an open dialogue with your recruiter who can provide you more specific details for this role.

Job Description

JOB DESCRIPTION SUMMARY

Provide a broad range of data engineering functions, including data modeling, data quality, data profiling, data acquisition and ingestion, extract, transform and load (ETL), metadata enrichment and management, data provenance and lineage, and other specialized data management functions.

Responsibilities

• Perform data analysis and assess data management requirements for a specific Platform or Journey, including complex analysis involving multiple pods or products.
• Maintain expert knowledge of upstream data, including knowledge provided through data profiling, data quality reporting, and the production of metadata.
• Support the acquisition and ingestion of data.
• Articulate complex, large-scale and high-impact technical design and development details to non-technical business partners.
• Elicit, analyze and understand business and data requirements to develop complete business solutions, including data models (entity relationship diagrams and dimensional data models), ETL and business rules, data lifecycle management, governance, lineage and metadata.
• Ensure data is maintained in compliance with enterprise data standards, policies and guidelines.
• Develop and maintain complex data models using industry-standard modeling tools.
• Develop and maintain complex ETL jobs and frameworks using the Bank's standard tools.
• Provide support to development and testing teams to resolve data issues, including escalation support for complex issues.
• Support partners and stakeholders in interpreting and analyzing data.
• Analyze and understand business and data requirements to develop complete ETL solutions under the guidance of senior peers and tech leads.
• Build effective working relationships within own pod and across partner teams to encourage collaboration on all pod deliverables.
• Coordinate with technology work teams and stakeholders to ensure overall delivery success.
• Support the QA team with data analysis and investigations of complex issues/test cases as part of SIT/UAT/PAT testing.
• Provide oversight on post-implementation activities during the warranty period.
• Execute and approve code check-in/check-out into the source code repository as part of source code management.
• Work closely with Release Management teams to support code packaging and deployment (CI/CD) into higher environments.
• Be the lead participant in application design and code reviews.
• Raise ServiceNow requests and work with the Change Management team to support release management activities.
• Lead data engineering initiatives and capabilities, including data governance principles and how they apply across the organization.
• Ensure metadata and data lineage are captured and compatible with enterprise metadata and data management tools and processes.
• Adhere to and contribute toward standard secure coding practices to ensure applications are free of common coding vulnerabilities.
• Ensure technical decisions, technical risks and lessons learned are identified and clearly documented, with enhancements implemented accordingly.
• Protect the interests of the organization by identifying and managing risks and escalating non-standard, high-risk activities as necessary.
• Adhere to internal policies/procedures and applicable regulatory guidelines.
• Keep current on emerging trends/developments and grow knowledge of the business, related tools and techniques.
• Enable team members by sharing knowledge and leveraging engineering best practices.
• Participate fully as a member of the team and support a positive work environment that promotes service to the business, quality, innovation and teamwork, while ensuring timely communication of issues and points of interest.
• Provide thought leadership and/or industry knowledge for data engineering best practices and participate in knowledge transfer within the team and business unit.
• Keep current on emerging trends/developments and grow knowledge of the business, related tools and techniques.
• Participate in personal performance management and development activities, including cross-training wit