Data/AI Engineer III (US)

TD Bank • Mount Laurel, NJ

3d • $98,160 - $159,270 • Onsite

About The Position

The Data/AI Engineer III provides broad data engineering services, including data modeling, data quality and profiling, data acquisition and ingestion, ETL, metadata enrichment and management, data provenance and lineage, and other specialized data management functions. The role independently performs highly complex technical work, applies expert knowledge of data engineering and TD systems, and designs and develops solutions that meet business and technical requirements. It also uses and evolves established engineering patterns, leads the development of new patterns where needed, serves as a subject matter expert, and consults across teams on research, analysis, design, and support.

The engineer analyzes data management requirements, supports data ingestion, develops complex data models and ETL jobs, and ensures data practices comply with enterprise standards, policies, governance, and regulatory guidelines. The position communicates complex technical concepts to non-technical partners and collaborates with development, testing, architecture, infrastructure, and business teams to deliver and support data solutions. It also contributes to data engineering initiatives, security practices, release and deployment activities, risk management, knowledge sharing, mentoring, and team development.

The position is based in Mount Laurel, New Jersey, and lists 40 hours. The stated pay range is $98,160–$159,270 USD. TD describes its Total Rewards package as including base salary and variable compensation or incentive awards, health and well-being benefits, savings and retirement programs, paid time off, banking benefits and discounts, career development, and reward and recognition. The company also describes training and onboarding, development conversations, online learning, and mentoring programs.

Requirements

• Degree, postgraduate degree, or technical certificate in Data Management or a related discipline such as Computer Science or Engineering, or equivalent practical experience.
• At least 5 years of relevant experience.
• Expert knowledge of data engineering frameworks, technologies, tools, processes, patterns, and procedures, including their integration with other technology areas such as Architecture or Infrastructure.
• Ability to independently perform highly complex technical tasks.
• Expert knowledge of TD applications, systems, networks, innovation, design activities, business, organization, best practices, and standards.
• Ability to design and develop to business and technical requirements, including analysis, adaptation, integration, coding, testing, debugging, and execution.
• Ability to use and evolve established patterns to solve complex problems and lead development of new patterns when needed.
• Ability to consult with peers and partners across multiple teams on research, analysis, design, and support.
• Ability to analyze data management requirements, maintain knowledge of upstream data, support ingestion, develop data models and ETL, and ensure standards and compliance.
• Ability to communicate complex technical details to non-technical business partners.
• Ability to collaborate with development, QA, technology, architecture, infrastructure, and business teams.
• Ability to identify and manage risks, follow policies and regulatory guidelines, and contribute to secure coding practices.

Nice To Haves

• Graduate degree.
• At least 5 years of hands-on data engineering experience with Azure and Databricks.
• Advanced proficiency in Python, SQL, Spark, and PySpark.
• Understanding of batch and streaming processing, dimensional modeling, Lakehouse architecture, and distributed processing.
• Hands-on experience developing GenAI or agentic applications.
• Experience with Databricks Genie, AI/BI, MLflow, vector search, RAG, or comparable technologies.
• Experience engineering data for AI/ML workloads.
• Experience in financial services or another highly regulated industry.

Responsibilities

• Perform data analysis and assess data management requirements for a platform or journey, including complex analysis across multiple pods or products.
• Maintain expert knowledge of upstream data through data profiling, data quality reporting, and metadata production.
• Support the acquisition and ingestion of data.
• Explain complex, large-scale, high-impact technical design and development details to non-technical business partners.
• Elicit, analyze, and understand business and data requirements to develop complete solutions, including data models, ETL, 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.
• Support development and testing teams in resolving data issues, including complex issue escalation support.
• Support partners and stakeholders in interpreting and analyzing data.
• Build effective working relationships within the pod and with partner teams to promote collaboration on deliverables.
• Coordinate with technology work teams, including ITS, ARE, Architecture, and Enterprise Protect, to support successful delivery.
• Support QA investigations and data analysis for complex issues and test cases during SIT, UAT, and PAT testing.
• Provide oversight of post-implementation activities during the warranty period.
• Execute and approve code check-in and check-out in the source code repository as part of source code management.
• Work with ITS and ARE teams to support code packaging and deployment through CI/CD into higher environments.
• Lead participation in design, architecture, and application reviews.
• Raise ServiceNow requests and work with change management to support release management activities.
• Lead data engineering initiatives and capabilities, including applying data governance principles across the organization.
• Ensure metadata and data lineage are captured and compatible with enterprise metadata and data management tools and processes.
• Follow and contribute to standard secure coding practices to help prevent common coding vulnerabilities.
• Document technical decisions, risks, and lessons learned, and implement enhancements accordingly.
• Identify and manage risks, escalate non-standard or high-risk activities, and follow internal policies, procedures, and applicable regulatory guidelines.
• Keep current with emerging trends and developments and expand knowledge of the business, tools, and techniques.
• Share data engineering best practices and industry knowledge, and participate in knowledge transfer.
• Keep teammates informed about project progress and relevant day-to-day information.
• Mentor and enable team members, provide guidance, and proactively identify and resolve issues.
• Develop productive relationships with internal and external business partners and stakeholders.
• Contribute to a fair, positive, equitable, and diverse work environment and act as a brand ambassador for the business area and bank.

Benefits

• Variable compensation or incentive awards, potentially including cash and/or equity incentive awards through an incentive plan.
• Health and well-being benefits.
• Savings and retirement programs.
• Paid time off, including Vacation PTO, Flex PTO, and Holiday PTO.
• Banking benefits and discounts.
• Career development.
• Reward and recognition.
• Training and onboarding.
• Online learning platform and mentoring programs.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: Associate degree