Top Data Jobs in Toronto

Lead high-impact AI initiatives, develop ML products, collaborate with cross-functional teams, and mentor data scientists in a dynamic environment.

Own strategy, roadmap, and delivery of analytical data products. Lead client discovery, prioritize backlogs, run Agile ceremonies, and ensure launches. Collaborate with engineering and data teams on data quality, governance, architecture (Snowflake, BigQuery, ClickHouse, Databricks, Redshift), perform SQL validation, and drive product adoption and continuous improvement.

The Lead Financial Data Analyst manages financial instrument data collection, quality control, client inquiries, and operational risk compliance, while coaching others and improving processes.

Founding Staff Data Scientist to build decision science from the ground up: design and deliver forecasting, optimization, experimentation, personalization, and recommendation systems; establish modeling standards, validation and deployment playbooks; mentor data scientists and integrate reusable decision workflows with the ML platform.

As a Data Analyst at Stripe, you'll build data products, metrics, and dashboards, analyze large datasets, and communicate insights to drive business decisions.

Contributors record authentic first-person video of household tasks, hands-on work, and everyday activities using a phone mounted to a headstrap. They choose their own schedule, preferably recording at least 10 hours weekly, then upload footage through Perle’s app for review and payment. Contributors must be at least 18, reside in the United States, Canada, or Latin America, have a supported smartphone, and be comfortable wearing a headstrap.

Analyze microbiome and longitudinal clinical datasets, develop microbiome metrics and biomarkers using statistics and machine learning, create visualizations and reports, draft clinical research documents, support product and study development, and contribute to scientific publications and proposals.

Lead platform roadmaps for Matching, Activity Capture, and CRM integrations. Improve data quality, instrument metrics, triage customer issues, represent platform in customer and IT settings, and enable CS/SE teams while bringing AI tools into product workflows.

The Lead Engineer for BI will design data solutions, lead project teams, mentor, and drive BI strategy aligning with business needs.

Design and maintain data warehouse systems, develop ETL pipelines, manage datasets for analytics, and implement real-time data solutions.

The Data Scientist will design algorithms for insights, structure data, collaborate with machine learning engineers, and analyze datasets to improve workflow automation and product value.

The role involves programming and data review for real-world data analysis, supporting epidemiologists, and managing complex data sets using statistical programming languages and tools.

The Data Analyst will build key data models and metrics for decision-making, partner with stakeholders for analytical insights, create datasets for self-service reporting, and ensure data reliability through quality checks.

As a Python and Kubernetes Software Engineer, you will develop data analytics solutions using open source tools, collaborating with a distributed team.

Lead a team in developing scalable data solutions, mentor engineers, manage expectations, and contribute to code. Focus on high-quality software practices.

Lead enterprise Data & AI transformation initiatives, managing the delivery of cloud-based analytics platforms while aligning business and technology objectives. Responsibilities include program governance, stakeholder management, and overseeing implementations of Microsoft Fabric.

Design, build, and optimize cloud-based data platform components: ingest and transform varied datasets, create metadata-driven pipelines and data APIs, support AI/ML infrastructure, implement monitoring and metrics, and improve data processes to enable analytics and operational systems.

Senior data specialist responsible for leading data mapping, documenting field-level mappings and lineage, ensuring data quality and validation, performing reconciliation, collaborating with engineers and stakeholders, and building reports/dashboards to support analytics and governance across Azure/Databricks enterprise data platforms.

Lead delivery of a large-scale Enterprise Data Platform transformation, manage project plans, risks, dependencies, and cross-functional teams; drive execution, stakeholder alignment, reporting, and adoption of modern data capabilities.

Design, build, and maintain the data warehouse and data lake, schemas, and reliable ETL/ELT pipelines to support AI agents and analytics. Implement data governance, quality checks, lineage, and access controls to meet ISO27001 and SOC2 Type 2. Partner with AI, InfoSec, and product teams to enable agent orchestration, integrate with Microsoft 365/Graph/OAuth, monitor platform health and costs, and support AI program planning and documentation.

Design, build and configure scalable Big Data applications and ingestion pipelines. Translate requirements into design and code, work with architects and business stakeholders, ensure solutions meet security and data privacy policies, and support CI/Agile SDLC practices while analyzing data stores and Hadoop cluster performance.

Design and own finance solution and data architecture for a multi-year finance transformation. Define integrations across sub-ledgers, GL, IFRS17 and actuarial systems, data flows, governance, controls, reconciliations, Snowflake/AWS data platform patterns, and reusable finance data products. Lead architecture governance, vendor assessments, and measurable outcomes while partnering with finance, actuarial, IT, and group architecture teams.

Lead enterprise data product strategy and roadmaps, partner with business and technology stakeholders to prioritize and deliver trusted data capabilities, ensure governance/security/quality, drive adoption and measure product success across global teams.

As a Data Analyst in People Analytics at Lyft, you'll manage data requests, build ThoughtSpot dashboards, and enhance insights using AI capabilities. You'll collaborate with HR to craft metrics and translate findings into actionable insights for leadership.

The Senior Manager of Data Engineering leads a team in developing data and AI products, guiding architecture and design, and ensuring process improvements while managing stakeholder expectations and technical implementation.