Top Data Jobs in Toronto

Lead and develop five experienced analysts and data scientists while personally owning analytics across ecommerce, health, and supply chain. Partner with executives, establish KPI and reporting standards, prioritize the analytics roadmap, deliver strategic insights, and advance AI-native self-service analytics tools. The role combines people leadership, hands-on analysis, data governance, and business strategy in a remote-first environment.

Provide Workday HCM data conversion consulting across client implementations and post-production projects. Build, validate, and deploy data conversion, business process, and reporting solutions; guide requirements, configuration, testing, and production deployment; collaborate with engagement and sales teams; maintain project forecasts; advise on Workday best practices; and support client relationships.

The Account Executive will drive revenue by engaging mid-market and enterprise businesses in data-sensitive sectors, managing the sales process, and building relationships with C-level stakeholders.

Build and operate Ground News’s data platform, including warehouse integrations, scalable dimensional models, analytical layers, and KPI infrastructure. Own ingestion pipelines, data quality, monitoring, incident response, infrastructure as code, and deployment workflows across AWS and GCP. Partner with analysts, ML engineers, and product stakeholders to create reliable, reusable data models and ensure accurate, observable, cost-effective data systems.

Manage the operational delivery of human data for a strategic AI customer. Responsibilities include translating requirements into workflows, managing participant pools and onboarding, tracking supply and quality, leading cross-functional projects, resolving operational issues, documenting processes, and improving scalability with Product and Engineering. The role also supports participant assessment and certification programs while serving as an operational point of contact for delivery teams.

Design and build scalable data systems and pipelines, migrate legacy systems, ensure data governance, and collaborate with cross-functional teams to enhance data accessibility and reliability.

Designs, implements, and validates data masking and sanitization solutions for development, testing, and non-production environments. Responsibilities include identifying sensitive data, developing masking rules, configuring algorithms, integrating solutions with Azure pipelines and ETL processes, preserving referential integrity, testing masked datasets, and maintaining masking documentation and metadata repositories.

Build and modernize enterprise reporting solutions using Databricks and Power BI. Responsibilities include developing governed datasets, Delta-table reporting views, semantic models, dashboards, automated report distribution, and data validation processes. The role collaborates with business stakeholders, supports testing and deployments, and optimizes reporting performance. Required expertise includes advanced SQL, data warehousing, ETL/ELT, Databricks, Power BI, DAX, Power Query, and Azure services.

Conduct research and analysis to develop and maintain data on U.S. government leadership and organizational structures. Responsibilities include advanced open-source research, data collection, quality analysis, discrepancy identification, workflow improvement, and cross-functional collaboration. The role requires strong analytical, project management, communication, research, and organizational skills, along with proficiency in Microsoft Excel and familiarity with documentation tools such as Jira and Confluence.

Own the architecture and technical direction of Northbeam’s data ingestion and integration platform. Lead event-driven and batch processing decisions, establish standards for observability, freshness, and failure handling, and guide cross-functional platform design. Mentor engineers, conduct design reviews, influence teams without direct authority, and drive foundational platform improvements. The role requires deep Python and SQL expertise, cloud data warehouse experience, scalable API-based ETL design, orchestration, and containerized infrastructure experience.

Build and maintain reliable data pipelines, integrations, APIs, and event-driven or batch processing systems. Transform data from advertising, commerce, and order management platforms while improving monitoring, data freshness, performance, security, and scalability. Partner with product, infrastructure, and data engineering teams to expand the integration platform and onboard new sources in a cloud-native, multi-tenant environment.

Data Engineer Intern supporting large-scale data projects at Lyft. Responsibilities include owning an internship project, improving code quality, writing tests, participating in code reviews, and contributing to continuous integration and deployment. The role requires database experience, SQL and data modeling knowledge, strong computer science fundamentals, and effective communication. This hybrid Toronto internship is available during Summer 2027.

Data Science interns develop optimization, machine learning, and statistical models for Lyft’s transportation marketplace. Responsibilities include exploratory data analysis, production modeling code, algorithm implementation, simulated and live experiments, analysis of experimental and observational data, and communicating findings to cross-functional partners. Interns collaborate with engineering and product teams to support launch decisions.

Build and operate large-scale infrastructure and data pipeline platforms. Responsibilities include developing automation, monitoring system health, improving reliability and scalability, managing cloud infrastructure, participating in design and production readiness reviews, planning capacity, documenting processes, and responding to incidents through on-call rotations. The role requires production software development, infrastructure tooling, data streaming and storage expertise, and experience optimizing distributed systems in public cloud environments.

Build and maintain Power BI dashboards, data models, SQL queries, and Crystal Reports. Administer reporting infrastructure, validate data, document reporting processes, gather management requirements, and provide user training and support. The role also includes light HTML, CSS, and Bootstrap work, report embedding, presentation materials, version control with Git and GitHub, and collaboration with software developers on manufacturing and business systems.

The Product Manager for Data Products at Capital One will drive the design and operationalization of data products, prioritizing long-term roadmaps, defining metrics-driven strategies, and collaborating across teams to enhance customer experiences.

Develop and maintain complex software components for a data security project. Responsibilities include analyzing requirements, designing and coding solutions, testing, debugging, reviewing code and designs, supporting production, resolving application issues, and guiding development activities to ensure quality and timely delivery.

Leads enterprise DataOps and data platform engineering strategy, defining cloud infrastructure architecture, Terraform standards, CI/CD practices, governance, observability, and operational resilience. Drives multi-team platform transformations, mentors senior engineers, manages technical alignment with executive stakeholders, and develops AI-enabled automation using Cursor, Sana, and Gemini. Establishes data product, DataOps, and active governance methodologies across Workday’s enterprise data ecosystem.

Build and operate high-volume event streaming pipelines and analytical databases. Write production Elixir/Python services, integrate legacy pipelines, enforce event schemas and data quality, and provide monitoring, alerting, and SLA-driven observability for event data products.

Design, build, and operate a unified data platform: ingesting, processing, and exposing large volumes of data. Develop scalable ingestion pipelines, storage layers, observability, SLIs/SLOs, multi-tenant models, and metadata/ governance to enable secure cross-product data access and migrations from legacy systems.

Design, build, and maintain scalable data pipelines and infrastructure using Spark, Kafka, cloud platforms and containerization. Ensure data integrity, ETL, high availability, performance tuning, observability, and collaborate with AI/data science teams to enable analytics and ML solutions.

Lead industry-focused market strategy and growth for Manufacturing by engaging executives, shaping go-to-market plays, accelerating commercial opportunities, and enabling sales and delivery teams. Drive thought leadership, create repeatable industry assets, develop business cases and transformation roadmaps, and represent the company at events to position Microsoft-based Cloud, Data, and AI solutions for measurable business outcomes.

Drive industry-led growth for CPG and HLS/Professional Services by translating market trends into scalable Microsoft-based solutions. Engage executives, support strategic pursuits, build go-to-market plays, produce thought leadership, and enable sales, delivery, and marketing teams to accelerate customer transformation and commercial outcomes.

Lead industry strategy and market growth for Insurance by identifying trends, shaping go-to-market plays, engaging executives, accelerating commercial opportunities, and enabling sales, delivery, and marketing through Microsoft Cloud, Business Applications, Data, and AI solutions.

Leads TD Procurement’s data, reporting, analytics and insights function, including strategy, governance, executive reporting, data storytelling, predictive analytics, AI-enabled insights and self-service capabilities. Oversees interconnected procurement, risk, financial and operational data, defines analytical requirements, partners with technology teams, identifies trends and opportunities, and leads complex initiatives. Manages and develops a team of analytics professionals while advising senior leadership and advancing data-driven transformation.