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Work Location: Toronto, Ontario, Canada Hours: 37.5 Line of Business: TD Securities Pay Details: $155,000 - $215,000 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: Leads the Data Products and Engineering function, accountable for creating reusable, trusted, business-ready data products that enable front office teams and other stakeholders to make better decisions, accelerate analytics and AI use cases, reduce duplicated data builds, and strengthen governance, lineage, quality, and controls. Core accountabilities Develop and execute the Data Products and Engineering strategy, roadmap, and operating model in alignment with the broader Data, Analytics and AI vision. Design reusable, trusted business-ready data products with clear ownership, schemas, refresh cadence, quality expectations, and adoption paths. Engineer modern data models and AI-ready data structures to support analytics, agentic workflows, and decision support. Develop and govern semantic layers that define business metrics and dimensions consistently across consumption channels. Create graph-based ontologies and business relationship models that make client, product, book, account, revenue, risk, and related concepts explicit and machine-readable where warranted. Partner with Technology on production implementation, industrialization, platform patterns, controls, and sustainable support models. Establish data quality, lineage, provenance, metadata management, observability, and source-correction expectations for products under the function. Ensure data products consume approved reference and master data. Escalate missing shared hierarchies or attributes rather than allowing local forks to persist. Build a product-led delivery model that separates rapid discovery and validation from scalable technology industrialization. Lead and develop a multidisciplinary team spanning product ownership, data engineering, modelling, semantics, ontology, business analysis, and front office domain expertise. Represent front office data product needs in cross-functional governance forums and enterprise data, AI, and technology working groups as required. Decision authorities and scope: Prioritize the Data Products and Engineering roadmap in partnership with business sponsors, Technology, and governance stakeholders. Set standards for registered data products, including product specifications, data contracts, semantic definitions, quality measures, versioning, and change management. Recommend architectural decisions for data assets based on consistency, domain expertise, reuse, control, and front office value. Guide build versus reuse decisions to reduce duplicated engineering effort and long-term reconciliation costs. Business sponsors and front office leaders for prioritization, prototyping, fit-for-purpose validation, adoption, and outcomes. Technology partners for production engineering, platform implementation, resiliency, operational support, access controls, and industrialization. Reference Data and Master Data teams for client hierarchies, product taxonomy, book and business line hierarchy, securities and instrument master, standard definitions, and reference models. AI Products and Decision Sciences for Agentic AI, workflow orchestration, experimentation, prototyping, business validation, and AI-enabled product adoption. Central data governance, ontology, semantics, risk, and compliance functions for standards, lineage, metadata, regulatory traceability, and risk-based oversight. Adoption of registered, reusable data products by front office and partner teams. Reduction in manual reconciliation, duplicated local builds, and one-off data extracts. Data quality, availability, freshness, lineage completeness, and SLA performance against published data contracts. Cycle time from opportunity discovery through prototype, business validation, product build, technology industrialization, and scale. Measured business value from analytics, AI, and decisioning use cases enabled by the data product estate. Team health, talent development, delivery predictability, and stakeholder satisfaction. This role is not expected to be directly customer-facing. It drives customer and shareholder value by simplifying and strengthening the front office data foundation used for analytics, decisioning, AI-enabled workflows, risk insight, relationship coverage, revenue attribution, and operational efficiency. Enhances the speed, consistency, and confidence of business decisions by making trusted data products easier to discover, understand, and reuse. Creates authoritative business context that makes analytics and AI outputs more explainable, traceable, and governed. Reduces friction across business, Technology, Risk, Finance, Compliance, and control functions by aligning common definitions and reusable product components. Enables faster validation of new business opportunities while preserving the production controls required for scale. Delivery of priority roadmap milestones Reuse of semantic definitions, ontology fragments, product specifications, and engineered components across domains. Skills, Education & Experience: · 15+ years of experience in capital markets, front office data, data product management, data engineering, analytics, AI, or technology delivery including experience at the senior leadership level. · Undergraduate degree required or equivalent practical experience. Advanced degree in data, computer science, engineering, mathematics, finance, business, or related field preferred. · Deep understanding of front office domains such as Sales and Trading, Corporate Banking, Investment Banking, and Payments, with ability to translate business problems into data and AI product capabilities. · Strong experience leading multidisciplinary teams, setting operating models, building roadmaps, managing priorities, and developing senior talent. · Demonstrated knowledge of modern data architecture, Lakehouse platforms, data modelling, data contracts, metadata, data quality, lineage, provenance, observability, and production engineering controls. · Working knowledge of semantic layers, metric governance, ontological thinking, knowledge graphs, and AI-ready data structures. · Experience partnering with Technology to industrialize prototypes into production-grade solutions with controls, resiliency, and support models. · Strong stakeholder management capability across business, Technology, Risk, Compliance, Finance, and enterprise governance groups. Excellent executive communication skills, including ability to simplify complex technical and operating model decisions for senior audiences. Who We Are: TD Securities offers a wide range of capital markets products and services to corporate, government, and institutional clients who choose us for our innovation, execution, and experience. With more than 6,500 professionals operating out of 40 cities across the globe, we strive to make every interaction, product and experience remarkably human and refreshingly simple. Our services include underwriting and distributing new issues, providing trusted advice and industry-leading insight, extending access to global markets, and delivering integrated transaction banking solutions. In 2023, we acquired Cowen Inc., offering our clients access to a premier U.S. equities business and highly-diverse equity research franchise, while growing our strong, diversified investment bank. Together, we are reimagining what banking can be for our clients, colleagues and communities. Our Total Rewards Package Our Total Rewards package reflects the investments we make in our colleagues to help them and their families achieve their financial, physical, and mental well-being goals. Total Rewards at TD includes a base salary, variable compensation, and several other key plans such as health and well-being benefits, savings and retirement programs, paid time off, banking benefits and discounts, career development, and reward and recognition programs. Learn more Additional Information: We’re delighted that you’re considering building a career with TD. Through regular development conversations, training programs, and a competitive benefits plan, we’re committed to providing the support our colleagues need to thrive both at work and at home. Please be advised that this job opportunity is subject to provincial regulation for employment purposes. It is imperative to acknowledge that each province or territory within the jurisdiction of Canada may have its own set of regulations, requirements. Colleague Development If you’re interested in a specific career path or are looking to build certain skills, we want to help you succeed. You’ll have regular career, development, and performance conversations with your manager, as well as access to an online learning platform and a variety of mentoring programs to help you unlock future opportunities. If you’re passionate about helping clients and building deep, lasting relationships, TD offers diverse career paths where you can grow your expertise and make a meaningful impact. We're committed to your success and foster a respectful workplace where diverse perspectives are valued, everyone has fair opportunities to grow, and you can unlock your full potential to achieve your career goals. Here at TD, we hire and develop the best. Training & Onboarding We will provide training and onboarding sessions to ensure that you’ve got everything you need to succeed in your new role. Interview Process We’ll reach out to candidates of interest to schedule an interview. We do our best to communicate outcomes to all applicants by email or phone call. Accommodation Your accessibility is important to us. Please let us know if you’d like accommodations (including accessible meeting rooms, captioning for virtual interviews, etc.) to help us remove barriers so that you can participate throughout the interview process. We look forward to hearing from you! Language Requirement (Quebec only): Sans Objet
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.
