Data Engineering & Platform Enablement Practice Lead

Work Location:

Mount Laurel, New Jersey, United States of America

40

$123,680 - $200,200 USD

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  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.

Technology Solutions

The Data Engineering & Platform Enablement 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.

• 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.

• 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.

• 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.

• 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.

• 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.

• 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.

• Partner with Data Architecture, Governance, Reporting, and Business…