Senior Data Modeller / Data Product Designer — Enterprise Payments 

📍 Job Overview

Job Title: Senior Data Modeller / Data Product Designer — Enterprise Payments

Company: TD

Location: Toronto, Ontario, Canada

Job Type: Full time

Category: Data & Analytics / Technology

Date Posted: 2026-09-11

Experience Level: 10+ years

Remote Status: On-site

🚀 Role Summary

• Design and implement robust data structures across complex enterprise payments ecosystems, bridging transactional and analytical platforms.

• Develop reusable, domain-oriented data products that enhance data discoverability, governance, and consumption across operational and analytical use cases.

• Drive schema design for both semi-structured (MongoDB/JSON) and structured (Databricks/Delta Lake/SQL) data environments, ensuring scalability and compatibility.

• Collaborate closely with cross-functional teams, including business analysts, data engineers, and architects, to translate business requirements into effective data solutions.

📝 Enhancement Note: This role is critical for establishing a unified and governed Payments data context layer, requiring a deep understanding of data modeling principles for both operational and analytical workloads within a large financial institution. The emphasis on data product design indicates a strategic shift towards treating data as a product, with clear ownership, documentation, and defined consumer interfaces.

📈 Primary Responsibilities

• Design and maintain JSON schemas for MongoDB-based transactional Operational Data Stores (ODS), aligning with business workflows and query patterns.

• Develop corresponding Databricks/Delta table schemas for curated, reusable data products, optimizing for analytical query performance and data integrity.

• Define and implement reusable Payments data products, encompassing individual payment types and integrated cross-payment views to support holistic analytics.

• Architect a comprehensive context layer or canonical Payments model to facilitate cross-product analytics, reporting, and downstream data consumption.

• Translate intricate business requirements into logical and physical data models for both operational and analytical use cases, ensuring alignment with enterprise data strategy.

• Manage and support schema evolution, versioning, migration, and backward compatibility across MongoDB and Databricks platforms.

• Design data products that adhere to Unity Catalog standards, focusing on access patterns, discoverability, and secure, governed sharing.

• Differentiate and implement appropriate data product designs: source-aligned, domain-aligned, and consumer-aligned, based on use case requirements.

• Partner with data engineering teams to operationalize data models through efficient ingestion, curation, and transformation pipelines.

• Uphold governance standards, including comprehensive metadata management, data quality frameworks, lineage tracking, access controls, and appropriate documentation.

• Engage with business and technology partners to ensure data products are intuitive, reusable, and fit for purpose, driving adoption and value.

📝 Enhancement Note: The responsibility for designing a "context layer / canonical Payments model" is a significant undertaking, requiring the candidate to synthesize data from diverse payment types into a unified, understandable structure. This implies a need for strong data governance understanding and the ability to drive consensus across multiple stakeholder groups.

🎓 Skills & Qualifications

Education: While not explicitly stated, a Bachelor's or Master's degree in Computer Science, Data Science, Information Systems, or a related quantitative field is typically expected for a Senior-level role in this domain.

Experience: 8+ years of progressive experience in data modelling, with a strong emphasis on designing for both operational and analytical platforms within complex environments.

Required Skills:

• Extensive experience designing JSON and semi-structured schemas, preferably for document databases like MongoDB.

• Proven expertise in designing SQL and analytical schemas for data platforms, data warehouses, or data lakehouses.

• Demonstrated ability to translate business workflows and complex query patterns into durable, scalable, and performant schema designs.

• Solid experience in managing schema evolution, versioning, backward compatibility, and implementing robust migration strategies.

• Proven track record of designing reusable datasets or data products for analytics, reporting, API consumption, or other downstream consumers.

• Strong understanding of data product principles, including usability, reusability, clear ownership, comprehensive documentation, and consumer alignment.

• Working knowledge of Databricks/Delta Lake schema design, including best practices for partitioning and performance optimization.

• Familiarity with Unity Catalog or similar governed catalog and metadata management structures.

• Advanced SQL proficiency for data manipulation, querying, and analysis.

• Ability to collaborate effectively with Business Systems Analysts (BSAs), data engineers, enterprise architects, and diverse business stakeholders.

• Exceptional communication, documentation, analytical thinking, and problem-solving skills. Preferred Skills:

• Deep understanding of the Payments domain, including transaction flows, financial instruments, and regulatory requirements.

• Familiarity with ISO 20022 Payments schemas and their relationship structures.

• Experience designing canonical data models or context layers that integrate data from multiple source systems.

• Experience with MongoDB indexing, sharding, performance tuning, or cloud-based MongoDB services like MongoDB Atlas.

• Proficiency with PySpark, Spark SQL, Delta Lake optimization techniques, and Databricks performance patterns.

• Hands-on experience with cloud data services such as Azure Databricks, Azure Data Factory, Azure Synapse, or equivalent.

• Experience with event-driven architectures, including Kafka, event schemas, schema registry, and streaming data patterns.

• Familiarity with comprehensive data governance processes, including privacy impact assessments, data access controls, metadata standards, data lineage, and regulated-environment audit expectations.

• Experience with data catalog and data governance tooling such as Collibra.

📝 Enhancement Note: The requirement for 8+ years of experience, coupled with specific technologies like MongoDB, Databricks, and Unity Catalog, positions this as a senior, specialized role. The "Good To Have" skills highlight a desire for candidates with direct payments industry experience and familiarity with advanced cloud data platforms and governance tools, indicating a high level of technical and domain expertise expected.

📊 Process & Systems Portfolio Requirements

Portfolio Essentials:

• Demonstrations of designing and implementing complex logical and physical data models for both transactional and analytical systems.

• Case studies showcasing the creation of reusable data products, detailing their design, intended consumers, and impact on data accessibility or efficiency.

• Examples of schema evolution and versioning strategies applied to real-world scenarios, highlighting backward compatibility and migration planning.

• Evidence of translating business requirements into technical data specifications, with clear documentation and stakeholder buy-in.

• Projects illustrating the application of data governance principles, including metadata management, lineage, or access control design. Process Documentation:

• Samples of logical data models (e.g., ER diagrams) and physical data model designs for operational and analytical databases.

• Documentation outlining schema design decisions, including justifications for choices related to data types, relationships, partitioning, and indexing.

• Workflows detailing the process of collaborating with business analysts and data engineers to refine and implement data models.

• Examples of data product documentation, including business glossaries, technical specifications, and usage guidelines.

📝 Enhancement Note: For a Senior Data Modeller/Product Designer role, a portfolio is crucial. It should not just list projects but demonstrate the candidate's thought process, problem-solving approach, and ability to create scalable, governed data solutions. Emphasis should be placed on how the candidate has driven data reusability and clarity through their modeling and product design efforts.

💵 Compensation & Benefits

Salary Range: $105,100 - $129,400 CAD per year.

Benefits:

• Health and well-being benefits, supporting physical and mental health.

• Comprehensive savings and retirement programs to support long-term financial security.

• Generous paid time off, allowing for work-life balance and personal time.

• Exclusive banking benefits and discounts for TD customers and employees.

• Robust career development opportunities, including training and advancement programs.

• Reward and recognition programs to acknowledge outstanding performance and contributions.

Working Hours: 37.5 hours per week.

📝 Enhancement Note: The provided salary range is a strong indicator of the seniority and specialized nature of this role within the Toronto market for the financial services industry. The listed benefits are standard for a large, established financial institution like TD, emphasizing a holistic approach to employee well-being and career growth. The specific mention of "discretionary variable compensation" suggests potential for performance-based bonuses on top of the base salary.

🎯 Team & Company Context

🏢 Company Culture

Industry: Financial Services (specifically Capital Markets and Enterprise Payments within a major bank).

Company Size: TD is a large multinational financial services corporation, employing over 6,500 professionals globally in TD Securities alone, and significantly more across the entire organization. This scale implies robust processes, extensive resources, and a structured environment.

Founded: TD Bank Group was founded in 1854, indicating a long history, stability, and deep roots in the financial industry, which influences its culture towards reliability and long-term strategy.

Team Structure:

• The role is within the TDS Payments Technology team, a specialized unit focused on technology solutions for TD Securities' payments operations.

• This team likely comprises a mix of data engineers, data modelers, business analysts, architects, and potentially project managers, all focused on enhancing the payments data ecosystem.

• Collaboration is expected to be highly cross-functional, involving direct interaction with business partners within TD Securities and potentially broader enterprise data teams. Methodology:

• Emphasis on data-driven decision-making and the development of high-quality, governed data products.

• A structured approach to data modeling, schema design, and data engineering, leveraging modern cloud technologies like Azure Databricks and Unity Catalog.

• Focus on reusability, scalability, and maintainability of data assets, aligning with enterprise data architecture principles.

• Collaboration and knowledge sharing are encouraged, with a focus on inspiring positive work environments and continuous learning.

Company Website: https://www.td.com/

📝 Enhancement Note: TD's long history and position as a major financial institution suggest a culture that values stability, trust, and a methodical approach to innovation. The "remarkably human and refreshingly simple" slogan implies a focus on user experience and client relationships, which should extend to internal data product design and usability.

📈 Career & Growth Analysis

Operations Career Level: Senior Data Modeller / Data Product Designer. This level signifies a highly experienced individual contributor responsible for complex design and strategic input, often mentoring junior team members and influencing architectural decisions. The focus on "product design" elevates the role beyond traditional modeling to strategic data asset creation.

Reporting Structure: The role reports into the TDS Payments Technology team, likely under a Data Engineering Lead, Technology Manager, or an Architecture group focused on payments. The 75% data modeling/design and 25% data engineering enablement split indicates a core focus on design leadership with hands-on support for implementation.

Operations Impact: This role directly impacts the efficiency, accuracy, and strategic value of enterprise payments data. By designing robust data structures and reusable data products, it enables better analytics, reporting, risk management, and operational efficiency within TD Securities, contributing to informed business decisions and potentially revenue growth.

Growth Opportunities:

• Specialization: Deepen expertise in payments data, ISO 20022 standards, and advanced data modeling techniques for financial services.

• Leadership: Transition into a Data Architect role, leading design initiatives for larger data initiatives or managing a team of data modelers.

• Platform Expertise: Become a subject matter expert in Databricks, Unity Catalog, and MongoDB within TD's technology stack, influencing platform adoption and best practices.

• Cross-functional Exposure: Gain broader knowledge of capital markets, risk management, and other financial services domains through collaboration.

• Mentorship: Guide and mentor junior data engineers and analysts, sharing best practices in data modeling and product design.

📝 Enhancement Note: The emphasis on "data product design" and "Unity Catalog" suggests a forward-looking approach to data management. Growth opportunities will likely involve mastering these modern data management paradigms and potentially leading initiatives that leverage them for greater business impact.

🌐 Work Environment

Office Type: The role is based at TD Terrace - 160 Front Street West Corporate, Toronto, Ontario, indicating a primary on-site work environment within a major corporate office building.

Office Location(s): Toronto, Ontario, Canada. This is a central business district location, likely offering excellent access to public transportation and city amenities.

Workspace Context:

• Working in a large corporate office suggests a professional, structured environment with access to typical corporate amenities.

• Collaboration is expected to be frequent, facilitated by meeting rooms, common areas, and digital collaboration tools.

• The role will involve working with advanced data technologies, implying access to modern workstations and necessary software infrastructure.

• Interaction with a diverse team of technical and business professionals is a key aspect of the daily work environment.

Work Schedule: Standard full-time hours of 37.5 per week. While the role is on-site, the emphasis on productivity and effectiveness suggests a results-oriented work culture.

📝 Enhancement Note: The on-site requirement at a prominent Toronto address suggests a commitment to in-person collaboration and a structured corporate culture. Candidates should be prepared for a traditional office environment within a large financial institution, with opportunities for both focused individual work and team-based collaboration.

📄 Application & Portfolio Review Process

Interview Process:

• Initial Screening: Likely a recruiter or HR screen to assess basic qualifications, experience, and cultural fit.

• Technical Interview(s): In-depth discussions focusing on data modeling concepts, schema design for MongoDB and Databricks, SQL proficiency, and problem-solving scenarios. Expect questions related to schema evolution, data product design principles, and handling semi-structured data.

• Case Study/Portfolio Review: Candidates may be asked to present their portfolio, detailing specific projects, their design choices, challenges faced, and outcomes achieved. This is a critical stage to demonstrate practical application of skills.

• Team/Manager Interview: Meeting with the hiring manager and potential team members to assess collaboration skills, communication style, and alignment with team dynamics.

• Final Interview: Potentially with senior leadership to discuss strategic alignment and overall fit.

Portfolio Review Tips:

• Structure: Organize your portfolio logically, perhaps by data modeling type (operational vs. analytical) or by data product examples.

• Case Studies: For each project, clearly articulate the business problem, your role, the data modeling/design approach taken, the technologies used, the challenges encountered, and the measurable impact or solution delivered.

• Visuals: Include relevant diagrams (ERDs, JSON schema examples, data flow diagrams) to illustrate your work. For data products, show how they are structured and how consumers would interact with them.

• Technical Depth: Be prepared to discuss the nuances of your design choices, such as why a particular schema was chosen for MongoDB, how you handled schema evolution in Databricks, or the rationale behind your data product definition.

• TD Context: Tailor your presentation to highlight experiences relevant to financial services and payments data, if possible.

Challenge Preparation:

• Data Modeling Scenarios: Practice designing schemas for hypothetical scenarios, such as modeling customer transactions, payment processing flows, or financial reporting data.

• Schema Evolution: Be ready to discuss strategies for evolving schemas without breaking existing applications or analytics.

• Data Product Definition: Prepare to explain how you would define a "data product" for a specific payment use case, including its scope, ownership, and intended audience.

• Technical Deep Dive: Review Databricks/Delta Lake architecture, Unity Catalog concepts, and MongoDB schema design best practices.

📝 Enhancement Note: The emphasis on "Data Product Designer" and "Unity Catalog" means interviewers will likely probe deeply into how candidates approach data from a product management perspective – considering users, lifecycle, and value delivery, not just technical structure. A strong portfolio showcasing these aspects will be essential.

🛠 Tools & Technology Stack

Primary Tools:

• NoSQL Databases: MongoDB (strong emphasis), with potential experience in other document databases.

• Data Lakehouse/Warehouse Platforms: Azure Databricks (core), Delta Lake. Experience with traditional data warehouses may also be relevant.

• Data Modeling Tools: Tools for logical and physical data modeling (e.g., Erwin, ER/Studio, Lucidchart, Draw.io, or built-in Databricks/MongoDB tools).

• Schema Definition Languages: JSON Schema, SQL DDL.

Analytics & Reporting:

• Data Transformation: PySpark, Spark SQL.

• Cloud Data Services: Azure Databricks, Azure Data Factory, Azure Synapse (preferred).

• BI Tools (Implied): While not explicitly listed, experience with tools like Power BI or Tableau that consume data products would be beneficial.

CRM & Automation:

• Event Streaming (Preferred): Kafka, schema registry, event schemas.

• Data Governance & Cataloging: Unity Catalog (core), Collibra (preferred).

📝 Enhancement Note: The core technology stack revolves around MongoDB for operational data and Azure Databricks/Delta Lake for analytical data products, all managed under Unity Catalog. Proficiency in these specific tools, especially the interplay between MongoDB and Databricks, is paramount. Experience with Azure cloud services is a significant plus.

👥 Team Culture & Values

Operations Values:

• Quality & Innovation: A commitment to high-quality data solutions and exploring innovative approaches to data modeling and product design.

• Teamwork & Service: Fostering a collaborative environment and prioritizing service to business and technology partners.

• Continuous Learning: Encouraging a culture of learning, knowledge sharing, and staying abreast of evolving data technologies and methodologies.

• Efficiency & Effectiveness: Driving productivity and operational efficiency through well-designed data systems and processes.

• Human-Centricity: Applying a "remarkably human" approach to internal data products, ensuring they are understandable and usable for their intended audience.

Collaboration Style:

• Cross-functional Integration: Actively collaborating with business analysts, data engineers, architects, and business stakeholders to ensure alignment and successful data product delivery.

• Process Review & Feedback: Engaging in constructive feedback loops for data models and product designs, fostering a culture of continuous improvement.

• Knowledge Sharing: Proactively sharing expertise in data modeling, schema design, and data product principles with team members and stakeholders.

📝 Enhancement Note: TD's stated values emphasize a blend of professionalism, continuous improvement, and a human-centric approach. For this role, it translates to building data solutions that are not only technically sound but also user-friendly and well-documented, reflecting a mature approach to data as a strategic asset.

⚡ Challenges & Growth Opportunities

Challenges:

• Bridging Operational & Analytical Silos: Designing data structures that effectively serve both real-time transactional needs (MongoDB) and complex analytical queries (Databricks) requires careful consideration of trade-offs.

• Standardization in a Complex Ecosystem: Developing a unified "canonical Payments model" across diverse payment types and systems within a large financial institution is a significant integration challenge.

• Schema Evolution Management: Ensuring backward compatibility and smooth transitions as data schemas inevitably change over time, impacting numerous downstream consumers.

• Data Governance Implementation: Effectively embedding governance principles (metadata, lineage, access) within data product design and development in a regulated environment.

Learning & Development Opportunities:

• Payments Domain Expertise: Gaining deep knowledge of ISO 20022 standards and the intricacies of global payment processing.

• Advanced Cloud Data Platforms: Mastering Azure Databricks, Delta Lake, and Unity Catalog for large-scale data management.

• Data Product Management: Developing skills in defining, launching, and managing data assets as products.

• Cross-functional Collaboration: Enhancing communication and influence skills by working with diverse teams across technology and business units.

• Mentorship & Thought Leadership: Opportunity to guide junior colleagues and influence data strategy within TD Securities.

📝 Enhancement Note: The primary challenge lies in harmonizing diverse data sources and requirements into a cohesive, governed data product strategy for enterprise payments. Growth will come from mastering these complex challenges and becoming a leader in data product design within the financial sector.

💡 Interview Preparation

Strategy Questions:

• "Describe a complex data modeling project where you had to design schemas for both operational and analytical purposes. What were the key challenges, and how did you address them?" (Focus on JSON/MongoDB vs. SQL/Databricks design)

• "How would you approach designing a reusable 'Payments Data Product' that serves multiple downstream analytics teams? What key considerations would you include in its definition and structure?" (Focus on data product principles and Unity Catalog)

• "Walk me through your process for managing schema evolution and ensuring backward compatibility for data products in a production environment." (Focus on versioning, migration, and impact assessment)

• "How do you ensure data governance requirements (e.g., lineage, access control, metadata) are integrated into your data modeling and product design process?" (Focus on Unity Catalog, Collibra, and governance best practices) Company & Culture Questions:

• "What interests you about TD Securities and specifically the Enterprise Payments Technology team?" (Research TD's role in capital markets and payments innovation.)

• "How do you see your role contributing to TD's commitment to 'remarkably human' experiences, even within a technical data context?" (Focus on usability, clarity, and stakeholder impact.)

• "Describe a time you had to collaborate with non-technical stakeholders to define data requirements or explain complex data concepts. How did you ensure understanding and alignment?" (Focus on communication and bridging technical/business gaps.) Portfolio Presentation Strategy:

• Narrative Flow: Structure your portfolio presentation around a clear narrative for each project: problem, solution, your contribution, technologies used, and impact.

• Highlight Key Skills: Explicitly call out examples demonstrating JSON/MongoDB schema design, Databricks/Delta Lake modeling, data product definition, and schema evolution.

• Data Product Focus: For each relevant project, explain how it functions as a "data product" – who its consumers are, what value it provides, and how it's governed.

• Quantify Impact: Whenever possible, use metrics to demonstrate the success of your work (e.g., improved query performance, reduced data retrieval time, increased data reusability, successful migration).

• Ask Insightful Questions: Prepare thoughtful questions about the team's current data challenges, their roadmap for data products, and their approach to data governance.

📝 Enhancement Note: Given the role's focus on "Data Product Design" and "Unity Catalog," interviewers will likely probe for strategic thinking beyond pure technical modeling. Be prepared to discuss how your designs contribute to business value, data democratization, and a governed data ecosystem.

📌 Application Steps

To apply for this operations position:

• Submit your application through the provided Workday link on the TD Careers portal.

• Prepare Your Portfolio: Curate and organize your best examples of data modeling projects, focusing on JSON/MongoDB schemas, Databricks/Delta Lake models, and data product designs. Ensure you can clearly articulate the business problem, your solution, the technologies used, and the impact.

• Tailor Your Resume: Highlight your 8+ years of experience in data modeling, specifically mentioning MongoDB, Databricks, Delta Lake, JSON schema design, SQL, and any experience with payments data or ISO 20022. Emphasize your ability to design reusable data products and manage schema evolution.

• Practice Your Presentation: Rehearse presenting your portfolio and answering common interview questions, especially those related to data product strategy, operational vs. analytical modeling, and data governance. Be ready to discuss your approach to building a canonical data model.

• Research TD: Familiarize yourself with TD Bank Group's business, particularly TD Securities, its role in capital markets, and its technology initiatives. Understand their stated values and how they apply to a data-centric role.

⚠️ Important Notice: This enhanced job description includes AI-generated insights and operations industry-standard assumptions. All details should be verified directly with the hiring organization before making application decisions.