Data Engineer I

Work Location

Toronto, Ontario, Canada

Hours

37.5

Line Of Business

Technology Solutions

Pay Details

$69,700 - $98,400 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

As a Data Engineer I, you will provide a broad range of data engineering functions including data modeling, data quality, data profiling, data acquisition and ingestion, extract transform load (ETL), metadata enrichment and management, data provenance and lineage, and other specialized data management functions.

Customer Responsibilities

• Perform data analysis and assess data management requirements for a specific Platform or Journey.
• Maintain foundational knowledge of upstream data, including knowledge provided through data profiling, data quality reporting, and via the production of metadata.
• Support the acquisition and ingestion of data.
• Articulate technical design and development details to non-technical business partners.
• Elicit, analyze, and understand business and data requirements to develop complete business solutions under the guidance of senior peers and managers, including data models (entity relationship diagrams, dimensional data models), ETL and business rules, data life-cycle management, governance, lineage, and metadata.
• Ensure data is maintained in compliance with enterprise data standards, policies, and guidelines.
• Develop and maintain data models using industry standard modeling tools.
• Develop and maintain ETL jobs using the Bank's standard tools.
• Provide support to the development and testing teams to resolve data issues.
• Support partners and stakeholders in interpreting and analyzing data.
• Build effective working relationships to collaborate with peers and partners on personal deliverables.
• Provide data governance and availability support.

Shareholder Responsibilities

• Coordinate with technology work teams such as ITS, ARE, etc. to ensure overall delivery success.
• Support the QA team with any data loads and data analysis/investigations as part of SIT/UAT/PAT testing.
• Provide post implementation support to the production support team during the warranty period.
• Execute code check-in/ check-out into source code repository as part of source code management.
• Work closely with ITS/ ARE teams to support code packaging & deployment (CI & CD) into higher environments.
• Be an active participant in the design & architecture reviews of the application.
• Raise service-now requests and work with the change management team to support release management activities.
• Understand data engineering initiatives and capabilities, data governance principles and how they apply across the organization.
• Ensure metadata and data lineage is captured and compatible with enterprise metadata and data management tools and processes.
• Adhere to standard security coding practices to ensure application is free of most common coding vulnerabilities.
• Ensure privacy, security, governance, and access controls are properly adhered to.
• Ensure technical decisions, technical risks and lessons learned are clearly documented and enhancements are accordingly implemented.
• Protect the interests of the organization – identify and manage risks, and escalate non-standard, high-risk activities as necessary.
• Adhere to internal policies/procedures and applicable regulatory guidelines.

Employee / Team Responsibilities

• Participate fully as a member of the team, support a positive work environment that promotes service to the business, quality, innovation and teamwork and ensure timely communication of issues/ points of interest.
• Support the team by continuously enhancing knowledge / expertise in own area and participate in knowledge transfer within the team and business unit; Support knowledge-sharing across teams and with relevant stakeholders, ensuring knowledge is codified, monitored, tracked and managed.
• Work collaboratively with the team and leadership to proactively identify and resolve issues related to product quality.
• Work collaboratively with the data engineering team to define and validate appropriate user stories, acceptance criteria and definitions of done / ready.
• 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 within own team.
• Keep others informed and up-to-date about the status / progress of projects and / or all relevant or useful information related to day-to-day activities.
• Contribute to the success of the team by willingly assisting others in the completion and performance of work activities; provide training, coaching and/or guidance as appropriate.
• Contribute to a fair, positive and equitable environment that supports a diverse workforce.
• Act as a brand ambassador for your business area/function and the bank, both internally and/or externally.

Breadth & Depth

• Foundational knowledge of data engineering frameworks, technologies, tools, processes, patterns, and procedures.
• Performs moderately complex technical tasks independently.
• Performs complex technical tasks under general supervision.
• Foundational knowledge of TD applications, systems, networks, innovation, design activities, business, organization, best practices, and standards.
• Designs and develops to meet business and technical requirements; analyzes, adapts, integrates, codes, tests, debugs, and executes.
• Uses and evolves established patterns to solve routine problems; contributes to new patterns where necessary.
• Generally reports to a Practice Lead.

Experience And / Or Education

• Degree, Postgraduate Degree, or Technical Certificate in Data Management or related discipline (e.g. Computer Science, Engineering), or equivalent practical experience.
• 2-3 years of relevant experience with PySpark, Python, SQL.
• Experience working with ADLS, ADB, ADF and Synapse.
• A good understanding of business banking products.

Key skills/competency

• Data Modeling
• ETL Development
• Data Quality
• Data Governance
• Data Acquisition
• Metadata Management
• PySpark
• Python
• SQL
• Cloud Data Platforms (ADLS, ADB, ADF, Synapse)