Work Location:Toronto, Ontario, Canada
Hours:37.5
Line of Business:Technology Solutions
Pay Details:$115,600 - $163,200 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:The Data Platform Engineering Manager role is responsible for designing, building, and operating the enterprise technology reference data platform that serves as the foundation for technology governance, reporting, analytics, metrics, and decision-making. Reporting to the Head of Technology Data Management, this role leads the onboarding, integration, transformation, and delivery of technology data from infrastructure, cyber, cloud, AI, and enterprise systems into a Databricks-based data fabric.
The role is accountable for establishing scalable data ingestion patterns, integration frameworks, platform engineering standards, and operational controls that ensure technology data is accurate, accessible, reliable, secure, and consumable. This position plays a critical leadership role in enabling enterprise-wide data products, dashboards, KPIs, risk reporting, and operational insights through modern data engineering and platform capabilities.
KEY ACOUNTABILITIESData Platform Strategy & Enablement- Lead the design, implementation, and evolution of the Databricks-based technology reference data platform.
- Develop platform standards, engineering patterns, and operating procedures that support enterprise scalability and sustainability.
- Establish a modern data ecosystem capable of supporting governance, reporting, analytics, and AI-driven use cases.
- Drive platform modernization initiatives that improve performance, automation, resilience, and user experience.
Data Ingestion & System Onboarding - Lead onboarding of technology systems of record into the enterprise data platform.
- Design and implement scalable integration patterns including APIs, event-driven architectures, data streaming, and zero-copy data sharing.
- Establish standards for source system connectivity, transformation, validation, and data movement.
- Partner with source system owners to ensure data is delivered accurately, securely, and efficiently.
Data Engineering & Transformation - Design, build, and manage data pipelines that transform disparate source data into standardized enterprise data assets.
- Develop reusable ingestion and transformation frameworks that accelerate onboarding of new data sources.
- Implement automated controls to ensure data quality, integrity, completeness, and consistency.
- Optimize data processing and storage solutions to support large-scale technology data domains.
Data Operations & Reliability - Establish monitoring and observability capabilities to ensure platform health and data reliability.
- Define operational support processes, service-level agreements, and issue management procedures.
- Lead remediation efforts for data delivery issues, performance bottlenecks, and platform incidents.
- Ensure platform availability, resiliency, and business continuity requirements are met.
Data Security, Controls & Compliance - Ensure compliance with enterprise security, data protection, and regulatory requirements.
- Implement access controls, encryption standards, audit logging, and monitoring capabilities.
- Partner with Risk, Security, and Compliance teams to support governance objectives.
- Ensure adherence to enterprise technology and data management standards.
Enablement of Data Products - Support development and operationalization of technology data products.
- Ensure data products are sourced from authoritative systems and delivered through governed engineering processes.
- Build reusable data services supporting reporting, analytics, APIs, and business intelligence platforms.
- Enable self-service access to trusted enterprise technology data where appropriate.
Partnership & Stakeholder Engagement - Partner with Data Architecture, Governance, Reporting, and Business Intelligence teams to deliver integrated solutions.
- Collaborate with Infrastructure, Cybersecurity, Cloud, Engineering, and Enterprise Architecture teams.
- Participate in enterprise governance forums and provide technical leadership on platform capabilities and integration approaches.
- Influence platform and engineering decisions that improve enterprise data maturity.
Employee / Team Accountabilities - Build and lead a high-performing team of data engineers, platform engineers, integration specialists, and DataOps professionals.
- Establish clear team objectives, performance measures, and development plans.
- Foster a culture of accountability, innovation, automation, and continuous improvement.
- Manage team capacity, prioritization, resource allocation, and delivery commitments.
- Ensure engineering teams follow enterprise standards, controls, and best practices.
- Develop technical talent and mentoring programs to strengthen engineering capabilities.
- Promote collaboration across architecture, governance, analytics, and technology teams.
EDUCATION & EXPERIENCE- Bachelor's Degree in Computer Science, Engineering, Information Systems, Software Engineering, Data Science, or a related discipline.
- Master's Degree preferred in Computer Science, Data Engineering, Information Technology, or a related field.
- Relevant certifications preferred:
- Databricks Data Engineer Professional
- Azure Data Engineer Associate
- AWS Data Analytics or Data Engineering Certification
- Google Professional Data Engineer
- Snowflake, Kafka, or Streaming Technology Certifications
- 8-12+ years of experience in data engineering, data platform delivery, software engineering, cloud data platforms, or related technology roles.
- 5+ years leading engineering or platform teams in large enterprise environments.
- Proven experience designing and implementing enterprise data lakes, data fabrics, or cloud-native data platforms such as Databricks.
- Strong experience building APIs, event-driven architectures, streaming platforms, and modern integration solutions.
- Hands-on experience developing scalable ETL/ELT pipelines and data transformation frameworks.
- Experience with cloud ecosystems including Azure, AWS, or Google Cloud.
- Strong understanding of DataOps, observability, monitoring, automation, and platform operations.
- Experience supporting enterprise reporting, analytics, AI, and machine learning use cases through modern data platforms.
- Experience working in highly regulated organizations with strong governance, security, and compliance requirements.
- Excellent stakeholder management, communication, and leadership skills.
