Top Data Jobs in Toronto

Leads the design, architecture, and delivery of scalable data platform solutions using Databricks, Microsoft Fabric, and Snowflake. Provides technical guidance, facilitates client workshops, supports strategic solution sales, authors proposals and RFP responses, and develops client opportunities. The role also mentors data architects, engineers, and consultants, manages team capacity, conducts code reviews, and collaborates across practices to develop marketable data modernization capabilities.

Leads enterprise data governance operations, risk management, assessments, control testing, audit support, and governance policy development. Maintains data risk, process, and control libraries; promotes data quality, privacy, security, and compliance; aligns data strategy with business objectives; and manages cross-functional teams and stakeholders. The role develops governance roadmaps, drives organizational change, conducts audits, and serves as a senior specialist and people leader.

Creates and implements enterprise data governance roadmaps, policies, standards, and procedures. Ensures data security, privacy, quality, integrity, availability, and regulatory compliance. Conducts governance audits, harmonizes data across business units, manages metadata and master data practices, supports privacy technology enablement, and drives organizational change. Provides analytical, technical, and consulting support while aligning data management practices with business strategy and risk frameworks.

Leads Agile delivery teams by facilitating Scrum ceremonies, coaching teams, removing impediments, managing stakeholders, tracking Agile metrics, and driving continuous improvement. Partners with Product Owners on backlog quality and prioritization, supports Agile transformation, manages risks and dependencies, and promotes consistent Agile practices across multiple Scrum teams.

Lead the end-to-end data science lifecycle for multiple business use cases, including data extraction, wrangling, feature engineering, statistical modeling, machine learning, evaluation, deployment, and change management. Partner with business stakeholders to identify opportunities, recommend solutions, assess model fairness and risk, and integrate predictions into underwriting, claims, customer experience, and risk processes. Collaborate with IT Services to establish MLOps for batch and real-time prediction services.

Leads the end-to-end development and deployment of AI/ML models for enterprise financial reconciliation and data engineering. Designs Agentic AI and Generative AI solutions, analyzes structured and unstructured financial data, defines model roadmaps, and integrates models into production. Partners with engineering and business teams on scalability, risk mitigation, documentation, and adoption while translating complex findings into visualizations and strategic recommendations for senior leaders.

Designs enterprise-scale data processing frameworks and Azure-based data solutions, develops AI-powered automation, leads complex technical initiatives, mentors engineers, and influences data engineering strategy. The role requires advanced Python, Azure data services, data warehousing, distributed computing, and architecture expertise, along with stakeholder collaboration and technical leadership.

Designs and leads end-to-end AI and data science solutions, with a focus on NLP, machine learning, LLM integration, and actionable insights from unstructured data. The role includes deploying software in AWS, collaborating cross-functionally, serving as a technical lead, mentoring junior staff, and enforcing data science best practices across development, experimentation, testing, and peer review.

Own analytical data models and KPI standards for fintech products supporting fraud prevention, transaction flows, customer journeys, and product decisions. Partner with product, engineering, and business teams to deliver reliable insights across financial workflows. Mentor junior analysts, promote data literacy, improve analytical practices, and ensure data quality and consistency in a regulated fintech environment.

Designs and trains computer vision models for vehicle damage detection, builds high-throughput containerized model-serving microservices, and manages the end-to-end ML lifecycle. The role includes developing Kafka-based streaming data pipelines, optimizing models for real-time inference, maintaining annotation and evaluation frameworks, and collaborating with stakeholders to deliver scalable production solutions.

Develops end-to-end computer vision and machine learning solutions for vehicle damage detection. Responsibilities include training robust models, building high-throughput containerized REST/gRPC model-serving microservices, managing the full ML lifecycle, optimizing real-time inference, collaborating with stakeholders, and maintaining Kafka-based streaming data pipelines. The role also involves annotation and evaluation frameworks, cloud backend engineering, and technical design leadership.

Senior software developer responsible for building and operating scalable backend services for a VIN Decoder platform. The role develops Python APIs, services, and workers using PostgreSQL, Redis, AWS, and GCP BigQuery; designs data models and optimized SQL; integrates third-party APIs; and drives reliability, observability, testing, incident response, and operational maturity. The developer also contributes to architecture, code reviews, documentation, Agile delivery, on-call support, and technical mentorship.

Develops, deploys, and monitors production data and machine learning pipelines. Responsibilities include data preparation, exploratory analysis, predictive modeling, feature and algorithm selection, model deployment, infrastructure development, and monitoring model performance, data quality, drift, latency, and reliability. The role also requires communicating insights, collaborating cross-functionally, and translating business problems into scalable data and ML solutions.

Lead enterprise data integration and migration engagements across ERP, CRM, HCM, and other business systems. Supervise delivery teams, oversee extraction through cutover, define integration and data quality standards, collaborate with technical and business stakeholders, manage project execution, support proposals, and develop reusable migration frameworks. The role requires strong ETL/ELT, data modeling, Azure, integration architecture, leadership, and consulting expertise.

Owns the technical and statistical integrity of payment data analytics workflows. Responsibilities include SQL and Python data ingestion, transformation, validation, quality controls, feature engineering, time-series analysis, model readiness, back-testing support, GCP analytics environment management, workflow automation, documentation, and translating findings for product and executive stakeholders.

Collects, extracts, analyzes, interprets, and validates data to support business decisions. Develops and maintains management indicators, reports, data analytics solutions, quality-control procedures, documentation, and training materials. Participates in SAS ETL development, supports data governance and publication quality, and assists users with data requests and interpretation.

Lead Payments Data Science and Analytics for eBay Financial Services. Manage analysts, define analytics strategy and roadmaps, measure product performance, conduct A/B testing and causal inference, build predictive analytics and dashboards, and partner with Engineering, Product, and Business teams to drive cost savings, revenue growth, and product decisions.

Maintain and improve Item Master Data by processing setup and modification requests, updating SAP, reviewing upload files and resolving data errors. Prioritize mailbox requests, ensure data quality, follow product and approval processes, support system testing, create training documentation, and collaborate with Information Systems, product divisions, requestors, and management.

Leads Group Benefits data strategy, governance, protection, prioritization, and delivery. Establishes data usage direction, manages data requirements and reusable data assets, holds delivery teams accountable, identifies resource gaps, and partners with Analytics, IT, Product, Operations, Legal, Privacy, and Business Development. Communicates progress to senior leadership and supports data protection education, program development, and special projects.

Designs and maintains data models and structures within Guidewire Data Studio, develops ETL/ELT processes, improves data and query performance, and ensures data quality, integrity, traceability, and accessibility. Advises business teams and data users, supports platform administration and adoption, establishes data modeling and governance best practices, mentors colleagues, and contributes to modernization of data platforms and information systems.

Leads the strategy, architecture, engineering roadmap, and operational excellence of TD’s Databricks-powered Global Transaction Banking data platform. Oversees distributed engineering teams building data products, APIs, streaming services, cloud applications, analytics, and AI capabilities. Establishes architecture, governance, security, reliability, DevSecOps, and delivery standards while managing budgets, vendors, risks, and transformation initiatives. Develops engineering leadership and technical talent across global teams in Toronto, New York, and Bangalore.

Leads platform-level execution of software and AI/ML systems across multiple teams. Oversees shared infrastructure, development tooling, DevOps, CI/CD, cloud cost optimization, architecture, and system integration. Manages the full software development lifecycle, tracks engineering metrics, mentors engineers and managers, and aligns technical priorities with business goals. The role supports scalable, secure, high-availability data and intelligence platforms while driving continuous improvement and operational efficiency.

Own end-to-end data science for growth, product, AI and automation, or strategic partnerships. Define business problems, metrics, reports, analyses, and experiments; communicate insights; and drive data-informed decisions and measurable business outcomes with cross-functional stakeholders.

Own end-to-end data science for growth, product, AI and automation, or strategic partnerships. Define business questions, metrics, reports, analyses, and experiments; generate actionable insights; and influence product, growth, finance, and operations leaders. The role requires independent problem solving, strong SQL or Python skills, clear communication, and ownership of ambiguous, high-impact initiatives.

Owns end-to-end data science for growth, product, AI and automation, or strategic partnerships. Responsibilities include defining business problems, developing metrics and reports, analyzing data, designing experiments, communicating insights, influencing leadership decisions, and driving measurable business outcomes. Requires strong SQL or Python skills, independent analytical ability, comfort with ambiguity, persuasive communication, and ownership of strategic initiatives.