Top Data Jobs in Toronto

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.

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 recommendations; and drive decisions and measurable outcomes with cross-functional stakeholders. The role requires strong analytical thinking, independent data work using SQL or Python, persuasive communication, and ownership of ambiguous business problems.

Analyze the customer lifecycle across activation, engagement, retention, expansion, and renewal for a product-led SaaS business. Partner with Customer Success, Product, Growth, RevOps, and Data Engineering to develop analytical frameworks, identify growth opportunities, evaluate experiments, and provide actionable recommendations to senior leaders. Build data foundations and standards supporting digital success, AI, automation, forecasting, anomaly detection, and generative BI.

Conduct independent research on machine learning, knowledge representation, formal methods, AI agents, and trustworthy AI systems. Develop algorithms, models, prototypes, benchmarks, and rigorous experiments for enterprise-scale applications. Collaborate with scientists, engineers, and domain experts to translate operational constraints into deployable research systems. Evaluate reliability, robustness, privacy, interpretability, latency, cost, and process outcomes, while communicating findings through publications and internal or external presentations.

Use legal training to create and evaluate AI training data: perform legal research and analysis, provide annotation and feedback, support senior team members, communicate insights, participate in stakeholder presentations, and advocate for Responsible AI.

Executes revenue data reconciliations across financial systems and data platforms, investigates variances, validates data quality, and supports recurring pipelines. Uses SQL and Python for data extraction, transformation, automation, and troubleshooting. Develops operational reporting, documents data flows and reconciliation procedures, improves processes, and collaborates with finance, business, engineering, and analytics stakeholders to resolve data issues and enhance Commercial Banking data infrastructure.

Develop data science solutions for payments across Square and Cash App. Build AI-powered tools, ETL pipelines, dashboards, machine learning models, experiments, and cloud data systems. Partner with Product, Engineering, Finance, and Operations to improve payment success, cost efficiency, risk mitigation, and decision-making. Translate business needs into practical solutions, research questions independently, and operationalize data processes and infrastructure.

Supports INFINITI product initiatives through sales, incentive, and program performance analysis; competitive intelligence research; program communications; advertising disclaimer review; website offer testing; product marketing and launch planning; AI-driven innovation projects; and administrative coordination.

Work full time on a defined data science and analytics project using production robot, retail, and operational data. Responsibilities include SQL and Python analysis, BigQuery and dbt workflows, dimensional modeling, dashboard development, data-quality validation, analytical insights, and collaboration with technical and business stakeholders. Interns present their project and results at the end of the program.

Build and maintain Python, SQL, workflow, automation, dashboard, and AI-enabled tools for Enterprise Compliance. Identify manual and data-intensive processes, support complaint investigations, extract and analyze data, develop reporting, and ensure privacy, security, legal, records management, and compliance controls. Partner with stakeholders, document solutions, train users, manage projects autonomously, and communicate technical concepts to nontechnical colleagues. Regulatory and financial services knowledge will be developed on the job.

Supports Sales and Marketing through data analysis, reporting, dashboard development, programming, automation, and AI-enabled solutions. The role extracts and validates data, analyzes performance, defines KPIs, develops Power BI visualizations, investigates discrepancies, and translates findings into actionable business recommendations. It also partners with cross-functional stakeholders, supports dealer and marketing programs, automates processes, and improves data quality and operational efficiency.

Develop and maintain ETL processes, SQL Server databases, SSAS data models, SSRS reports, and Power BI dashboards. Build unified data models across ERP systems, improve BI performance, establish development standards, and translate cross-functional business requirements into scalable data and integration solutions. The role also supports modernization of Sofina’s data environment and strategic reporting for finance, sales, supply chain, and other stakeholders.

Owns the design, administration, security, monitoring, automation, reliability, and modernization of enterprise data platforms across SQL Server, Azure, and Microsoft Fabric. Responsibilities include high availability and disaster recovery, performance tuning, access governance, data integration, CI/CD deployments, incident response, platform standards, and cloud migration. The role collaborates with data, infrastructure, security, architecture, and application teams to maintain secure, scalable, recoverable, and cost-effective data services.

The Data Governance Analyst designs enterprise governance policies, manages data quality and metadata, maintains data dictionaries and lineage, and supports AI-ready analytics. Responsibilities include SQL-driven validation, profiling, data remediation, governance controls, and cross-functional collaboration with finance, actuarial, operations, underwriting, and IT teams. The role also performs SQL tasks supporting accounting functions and promotes secure, standardized data sharing in a regulated insurance environment.

Designs, builds, and operates scalable batch and near-real-time data pipelines across Microsoft Fabric and Azure. Develops ETL/ELT solutions, data models, monitoring, governance, security, CI/CD, and data quality controls. Leads technical design, modernization of legacy workloads, production support, performance optimization, documentation, and engineering standards while partnering with analysts, architects, developers, and business stakeholders.

Support and improve the enterprise data governance program by establishing policies, standards, controls, stewardship practices, metadata, data quality requirements, and governance metrics. Administer Microsoft Purview, including data cataloging, lineage, classification, and business glossary capabilities. Collaborate with stakeholders on compliance, risk, audits, information security, training, and remediation while promoting effective and compliant use of organizational data.

Designs and optimizes Azure SQL databases, data warehouses, ETL pipelines, data models, and Lakehouse solutions for datasets exceeding 5TB. Develops complex SQL queries, stored procedures, functions, and triggers; performs performance tuning and troubleshooting; and supports Azure infrastructure. The role also contributes to technology roadmaps, leads complex backend projects, conducts code reviews, and mentors developers while using tools including PySpark, Azure Data Factory, Databricks, and Delta tables.

Gather, process, analyze, and visualize data for personal finance and business journalism. Collaborate with reporters, editors, and visual journalists on consumer-focused data stories, verify newsroom data work, build reporting tools, and promote data literacy. The role requires strong news judgment, data analysis and visualization skills, dataset management experience, and familiarity with Python or R. Additional assets include AI, APIs, database management, AWS, and business datasets.

Leads an analytics team responsible for building governed data products, modernizing reporting, standardizing enterprise KPIs, and delivering self-service analytics and executive-ready insights. The role guides data strategy and architecture, manages governance and metadata, partners with business and IT stakeholders, maintains operational reporting, and mentors analysts and developers while using agile delivery practices.

Develop data tools, AI automations, ETL pipelines, dashboards, and machine learning models for payments. Analyze experiments, optimize payment performance and costs, mitigate risk, maintain data systems and cloud infrastructure, and translate business needs into actionable solutions while partnering with Product, Engineering, Finance, and Operations.

Lead data science initiatives supporting Wealthsimple’s Commercial team and AI Voice Agent program. Build reliable data models, develop and maintain machine-learning models, design experiments, conduct analyses, and create dashboards and metrics. Partner with Sales, Operations, Product, Design, Engineering, and leadership to improve sales processes and business outcomes. Mentor teammates and communicate insights to senior stakeholders.

Build data and evaluation systems for Glean’s AI products, including benchmark datasets, scalable eval infrastructure, agent observability, LLM-based verifiers, CI and production evaluation pipelines, and enterprise simulation environments. Analyze traces and failures, measure quality with statistical rigor, and translate findings into product improvements. Partner with research, engineering, product, and go-to-market teams to improve agent performance while balancing quality, latency, and cost.

Lead design, development, and deployment of data science and GenAI solutions. Translate business goals into ML problems, engineer features, train/evaluate models and LLMs, build RAG/agent systems, work with vector DBs, mentor juniors, and communicate results to stakeholders.

Design, build, and maintain scalable data ingestion pipelines, databases and data lakes for credit and lending datasets. Ensure accurate data mappings, monitoring and alerting, translate stakeholder requirements into analytical data flows, and integrate APIs to deliver reliable, high-quality data products.