Data Engineering & Platform Enablement Practice Lead (US)

Work Location: Mount Laurel, New Jersey, United States of America Hours: 40 Pay Details: $123,680 - $200,200

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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. Line of Business: Technology Solutions Job Description: 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. Data 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. Depth & Scope:
• Provides people management leadership by hiring the best talent, setting goals, developing staff, managing employee performance and compensation decisions, promoting teamwork and handling any/all disciplinary actions, as required
• Recognized as an expert in a specific data design or data engineering discipline field who can provide
• People leader with expert knowledge of disciplines and practices in field of expertise
• Deep expertise and knowledge of specific domain or broad range of TD frameworks, technology, tools, best practices, processes, and procedures, as well as broader organization issues
• Proven ability in soft skills people management of large teams
• Previous experience providing guidance on the work of practitioners as related to the quality of work being produced, delivered & speed of delivery/speed to market
• Ability to develop colleagues to be masters of their craft in the market around us
• Quickly adapts to customer, stakeholder, and regulatory needs in collaboration with Platform and Journey teams
• Experienced in the continuous assessment of Data Engineering Practitioners and their craft to ensure enterprise practice standards are upheld
• Facilitates and fosters Practice Community of Interest and use of this practice across the Technology organization
• Contributes to the development of coaching strategies for individuals within their area of expertise
• Provides leadership and guidance to several teams and solves cross-department issues
• Participates in the development of business and practice strategies
• Expert collaborator and is known for bringing diverse teams together to achieve a common goal
• Collaborates with other PLs in delivery of Practice-Area objectives Education & Experience:
• University or Graduate / post graduate degree in Data Management or related Computer Science or Engineering discipline; or equivalent practical experience
• 10+ years of relevant experience in field of specialization Preferred Qualifications:
• 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
• ITIL Foundation
• Experience in da