Top Data Jobs in Toronto

Owns data strategy, architecture, governance, modeling, pipelines, and modernization for a wealth management platform. Leads migration from monolithic systems to distributed, cloud-enabled services; establishes DataOps, MDM, data quality, privacy, security, and lifecycle practices; and enables analytics, BI, AI, and external data products. Advises engineering teams on database performance, integration patterns, and scalable data platforms while collaborating with governance stakeholders and executive sponsors.

Designs advanced cloud and on-premises technology solutions for clients, evaluating architectures for cost, performance, scalability, and functionality. Develops technical plans, specifications, roadmaps, reports, and proof-of-concept implementations while resolving complex problems and adapting to changing requirements. Partners with clients and cross-functional teams, applies enterprise architecture methodologies, stays current with emerging technologies, and mentors junior colleagues.

Lead research for AI-native data platforms, including vector data lakes, intelligent caching, RAG retrieval, agent memory, knowledge bases, indexing, hybrid search, and context freshness. Design prototypes, publish at leading venues, secure patents, collaborate with product and engineering teams, represent the organization externally, and mentor researchers and PhD interns.

Lead data science initiatives by developing, validating, and deploying scalable machine learning models. Apply statistical techniques, experimentation, and advanced analytics to generate actionable insights and support strategic decisions. Collaborate with engineering, product, analytics, and business teams while exploring AI approaches such as RAG and multimodal LLMs. Use Python, SQL, Databricks, Snowflake, and PySpark for analysis and model development. Promote reproducible, well-documented, production-quality data science practices.

Designs, builds, and operates scalable batch and streaming data pipelines and cloud data infrastructure using Databricks and related technologies. The role focuses on data modeling, orchestration, quality checks, lineage, monitoring, access controls, and reusable engineering frameworks. The engineer partners with Data Scientists, ML Engineers, and platform teams, contributes to architectural decisions and technical standards, troubleshoots production issues, reviews code, documents practices, and mentors junior engineers.

Provides technical leadership for a scalable data engineering platform on Databricks. Designs reusable frameworks, production pipelines, engineering standards, monitoring, and operational practices using Python, SQL, and Spark. Partners with Architecture, Data Science, ML Engineering, Product, and technology teams to integrate data capabilities, translate commercial real estate requirements, mentor engineers, conduct code reviews, and influence cross-team technical direction.

Leads Gore Mutual’s data science function, managing teams that develop, deploy, validate, and govern predictive, generative AI, LLM, and agentic solutions. Sets the analytics and AI strategy, oversees production ML infrastructure and model monitoring, ensures regulatory, ethical, privacy, and risk compliance, and identifies opportunities to improve underwriting, fraud detection, pricing, operations, and business growth. Partners with executives and cross-functional teams to translate analytical findings into strategic outcomes while fostering innovation, experimentation, and advanced data science capabilities.

Owns product data strategy, roadmap, governance, quality, taxonomy, enrichment, integrations, and automation across B2B and B2C digital channels. Partners with business and technical teams to optimize PIM systems and product data flows, prioritize requirements, develop business cases, and communicate progress to executive stakeholders. The role is hybrid in Richmond Hill and requires 10% travel within Canada and the United States.

Leads enterprise AI and data architecture strategy, defining target architectures, standards, governance, roadmaps, and infrastructure requirements. Designs end-to-end AI solutions spanning data pipelines, model training, deployment, and monitoring. Advises business and technology stakeholders, evaluates emerging technologies, ensures regulatory and architectural compliance, manages risks, presents recommendations to leadership, and mentors technical teams. Requires extensive experience in AI/ML architecture, data engineering, cloud platforms, MLOps, and enterprise technology strategy.

Leads enterprise Finance data strategy, governance, stewardship, lifecycle management, data lake intake, modeling, quality, and SOX-related controls. Manages an India-based data governance and reporting controls team while partnering with Finance, Accounting, FP&A, IT, Audit, and Data Engineering executives. Establishes data standards, monitors quality, oversees remediation, drives reporting reliability, and implements process improvements, automation, KPIs, and operating mechanisms.

Leads the architecture, development, reliability, governance, and scalability of batch and real-time data platforms. Designs pipelines, self-service tooling, observability frameworks, and disaster recovery practices; mentors data engineers; establishes engineering standards; and partners cross-functionally to enable trusted data access for analytics, data science, and product teams. The role also contributes to strategic roadmaps, platform innovation, technical debt reduction, and on-call support.

Lead design, development, testing, implementation and support of data and AI products. Translate business requirements, provide technical direction, mentor developers, build automation and frameworks, improve delivery processes, and perform data analysis, profiling and regression testing.

Own domain datasets, data pipelines, business metrics, and quality checks that support Lyft’s marketplace decisions. Build reliable analysis-ready data, deliver metrics to stakeholders, and partner with product managers, engineers, and operators to translate data into action. Communicate analytical findings clearly and maintain trusted data products with executive visibility.

Lead architectural design and technical development for data center and mission critical projects from concept through construction documentation. Coordinate drawings, integrate design solutions, ensure code and regulatory compliance, manage project timelines and budgets, and collaborate with clients, consultants, contractors, and internal teams. Provide technical guidance to junior staff while maintaining design quality, client objectives, and project deliverables.

As a Data Scientist at Opendoor, you'll enhance pricing strategies through statistical models, machine learning, experiments, and interdisciplinary collaboration to drive business outcomes.

Build and own Snout’s data foundation, including PostgreSQL and dbt warehouses, ingestion pipelines, dimensional models, data quality, documentation, and reliable rebuilds. Reconcile data from veterinary practice-management systems, support LLM/AI tooling, translate stakeholder needs into technical plans, and establish the foundation for a future data team and analyst.

Leads product marketing strategy, positioning, messaging, go-to-market execution, AI adoption, product launches, market expansion, and customer-driven roadmap input. Builds and manages a high-performing product marketing team, partners with executive, product, revenue, and marketing stakeholders, and drives product adoption, retention, category growth, and revenue across programmatic advertising and martech markets.

Supports customer analytics, predictive modeling, Next Best Action strategies, campaign measurement, customer journey analysis, dashboards, and performance tracking. The role prepares and analyzes data, automates reporting processes, partners with Marketing, Product, and Analytics teams, and communicates insights to technical and non-technical stakeholders.

Own regulatory analytics and reporting by building audit-ready datasets, pipelines, dashboards, compliance metrics, and automated workflows. Analyze complex data, monitor quality, support audits and regulatory exams, and deliver actionable insights to compliance teams and leadership. Translate regulatory requirements into accurate reports using Python, SQL, dbt, and Airflow while partnering cross-functionally to manage data issues and remediation.

Design scalable human-data solutions for frontier AI clients, translating ambiguous model-training and evaluation needs into executable programs. Lead discovery, methodology, task and workflow design, feasibility estimates, expert profiling, quality strategies, and technical architecture in partnership with engineering and project teams. Advise on fine-tuning, RLHF, benchmarks, red teaming, multimodal data, coding tasks, and agent environments while guiding pilots through early delivery and developing reusable best practices.

Build and maintain cloud-based ELT/ETL pipelines using dbt, Airflow, and Cloud Composer. Implement data transformations, ingestion workflows, quality tests, monitoring, and troubleshooting. Maintain schemas and lakehouse structures across Snowflake, BigQuery, and Databricks. Document pipelines and collaborate with analytics and data science teams to deliver optimized, reliable data structures.

Lead the development of AI and machine learning solutions at Mastercard, defining strategy and mentoring teams while ensuring compliance and enhancing consumer experiences.

Leads planning and roadmap execution for data orchestration capabilities across ingestion, transformation, routing, synchronization, monitoring, and delivery. Defines requirements for reusable pipelines, data contracts, schemas, lineage, observability, and AI-ready data flows. Partners with engineering, architecture, data, security, customer teams, and stakeholders to prioritize capabilities, improve reliability and adoption, reduce custom implementations, and establish measurable outcomes for data trust and onboarding efficiency.

Owns the strategy and roadmap for semantic insurance data products supporting AI, analytics, automation, and enterprise applications. Defines canonical concepts, relationships, metadata, data quality standards, APIs, knowledge graphs, and AI-ready consumption patterns. Partners with engineering, architecture, data, AI, UX, customer success, and insurance stakeholders to translate complex P&C insurance needs into product capabilities, prioritize use cases, establish requirements, drive adoption, and measure business impact.

Executive product leader responsible for PointClickCare’s enterprise Data as a Product strategy, customer-facing data products, APIs, interoperability, marketplace capabilities, monetization, and AI-enabled solutions. The role owns portfolio strategy, investment priorities, commercial growth, ecosystem partnerships, and executive stakeholder alignment while leading a senior product organization. Responsibilities also include organizational development, succession planning, regulatory and intellectual property considerations, and representing data strategy to customers, partners, analysts, and industry organizations.