Top Data Jobs in Toronto

Build and maintain scalable data pipelines, data platforms, reporting infrastructure, reconciliation systems, and self-service analytics tools. Develop customer-facing and internal applications, integrate internal and external data sources, optimize near-real-time processing, ensure data quality and auditability, and evaluate emerging technologies. Collaborate with engineering, product, analytics, security, and data science teams while participating in on-call support.

Validate Financial Crime Risk Management models for Trading Surveillance by performing validation testing and analysis, producing validation reports, communicating findings to stakeholders, and developing validation tests for advanced ML-based surveillance models while collaborating with Big Data developers and maintaining modeling best practices.

Leads analytics engagements for Small Business Banking, translating business needs into insights and strategic recommendations. Modernizes Power BI and Tableau reporting, develops migration roadmaps, and ensures data quality, governance, and compliance. Partners with data engineering and AI/ML teams on data curation, model monitoring, executive dashboards, and performance reporting. Provides analytics thought leadership, mentors team members, and promotes standardized, enterprise-grade reporting and visualization practices.

Build and maintain marketing data pipelines connecting Salesforce, Marketo, and cloud data warehouses. Automate campaign reporting, develop SQL datasets and views, improve attribution and data quality, and support executive marketing pipeline reporting. The role also involves Salesforce and Marketo configuration, documentation, and AI-assisted analytics using tools such as Claude. Candidates will collaborate with Marketing Analytics, Demand Generation, and Marketing Operations.

Lead Relay’s Data Platform team and senior data engineers. Own platform health, Snowflake cost optimization, real-time data strategy, observability, infrastructure reliability, AI-enabled data capabilities, and technical prioritization. The role includes developing engineers, managing operating rhythms, balancing technical debt with business value, and partnering across the company to improve speed, efficiency, and risk management.

Own the architecture, governance, and hands-on development of Rodan Energy’s Databricks lakehouse platform. Build and optimize ingestion and transformation pipelines across energy market, facility, and vendor data using Python, PySpark, SQL, Delta Lake, and Databricks tools. Establish medallion architecture, schemas, access controls, data quality standards, lineage, and monitoring. Collaborate with vendors, business stakeholders, and senior leadership while guiding platform standards and future capabilities.

Design, build, and maintain Azure-based data pipelines, data models, KPI marts, and reporting layers for financial and operational analytics. Translate business requirements into scalable solutions, perform reconciliation and root-cause analysis, implement data quality controls, execute QA and UAT, and document data flows and metric definitions. Collaborate with Finance, Business, Analytics, Technology, and infrastructure teams to deliver accurate, governed, production-ready datasets and improve automation and reliability.

Designs and maintains Azure-based ETL/ELT pipelines, data models, KPI marts, and reporting datasets for financial and operational analytics. Investigates data discrepancies, performs reconciliation and root-cause analysis, implements data quality controls, and supports QA, UAT, and production releases. Partners with Finance, Business, Analytics, and Technology teams to translate requirements into scalable solutions, document metrics and data flows, and automate processes.

Design, build, and deliver scalable Salesforce solutions across Sales Cloud, Data Cloud/Data360 and Agentforce. Lead data integration, identity resolution, data modelling, AI-assisted workflows, Flow/Apex development, LWC/Visualforce components, and platform administration. Own end-to-end delivery, governance, data quality, and release management while partnering with sales and data teams to implement automation and AI-enabled sales workflows.

Develop and deploy machine learning pipelines and advanced AI models for drug discovery using complex biomedical and DNA-Encoded Library datasets. Responsibilities include data preprocessing, predictive modeling, deep learning, exploratory analysis, visualization, MLOps, experiment tracking, and reproducible workflows. Collaborate with structural biologists, medicinal chemists, and bioinformaticians, communicate findings to diverse audiences, and evaluate emerging approaches for molecular property prediction and de novo drug design.

Build and own battery and price-responsive load optimization models using energy market and meter data. Backtest models, track experiments in MLflow, integrate forecasting inputs, and partner with data engineering and data science teams. Expand optimization capabilities across North American energy markets, including IESO, AESO, PJM, NYISO, MISO, and ERCOT.

Develop marketing mix and econometric models to measure channel effectiveness, ROI, marginal returns, and budget optimization. Manage data preparation, ETL pipelines, statistical modeling, forecasting, machine learning, scenario simulations, and incrementality testing. Translate complex findings into actionable recommendations and communicate insights to technical and non-technical clients. Collaborate with project leads and stakeholders while managing timelines, deliverables, and expectations.

Develop marketing mix and econometric attribution models for advertising clients. Responsibilities include data preparation, ETL automation, statistical and machine learning modeling, scenario analysis, incrementality testing, budget optimization, and model validation. The role translates analytical findings into strategic recommendations and communicates insights through visualizations and presentations to technical and non-technical stakeholders.

Conduct market research and analyze investment fund and structured product data to support regulatory processes, risk analysis, policy initiatives, and operational oversight. Maintain datasets, models, dashboards, and performance reports; assess data quality and market trends; identify emerging risks; and present findings to management, policy leads, and committees. Collaborate with enterprise data teams to obtain and manage data access.

Owns enterprise data-services product roadmaps, backlogs, specifications, prioritization, delivery, adoption, and success metrics. Partners with UX, engineering, AI, data, and energy-domain experts to transform advisory workflows and deliver secure, integrated, AI-enabled platform capabilities. Leads discovery, cross-functional delivery, rollout, stakeholder communication, and continuous improvement across global user groups.

Develop customer-specific AI solutions for consequential business decisions. Investigate internal and public information, evaluate evidence, identify uncertainty, and design reliable AI workflows. Work directly with customers to understand needs, prototype solutions, test them with users, and improve their usefulness. Translate successful workflows into repeatable product capabilities while partnering with engineers on integrations and missing technical functionality.

Build and maintain streaming and batch data pipelines, data lakes, warehouses, and transformation systems using AWS and modern data tools. Collaborate with engineers on data integrity, testing, validation, documentation, and production code. Develop prototypes for uncertain technical solutions, support data visualization infrastructure, lead projects independently, advocate for trustworthy data, and mentor junior engineers.

Owns Partner Marketing operations across Salesforce reporting, campaign data, MDF budgeting and reconciliation, partner portal administration, training content, and system integrations. The role maintains data accuracy, builds dashboards, tracks partner engagement and campaign performance, troubleshoots portal issues, manages partner access, and coordinates with Marketing, Sales, Finance, IT, and partners. It requires strong Salesforce, Excel, project management, data analysis, and partner portal experience.

Leads Medline Canada's data platform, business intelligence, analytics, governance, and data engineering strategy. Oversees Microsoft Fabric architecture, enterprise integrations, Power BI products, data security, residency, quality, and semantic modeling. Builds and manages a multidisciplinary team, directs implementation partners, collaborates with global data organizations, and delivers trusted data products supporting reporting, executive decisions, advanced analytics, and future AI capabilities.

Develop and deploy machine learning and statistical models for commercial property and casualty insurance pricing. Responsibilities include transforming large datasets into insights, building claims and pricing models, managing the full model lifecycle, monitoring performance, improving reproducibility, maintaining data-mining tools, and communicating recommendations to business and technology partners.

Build and optimize scalable data pipelines, models, integrations, and workflows supporting analytics, loyalty strategy, and enterprise decision-making. Maintain Snowflake-based data infrastructure, improve automation, CI/CD, observability, data quality, lineage, metadata, and governance. Collaborate with analytics, data science, marketing, product, privacy, security, compliance, and external vendors while helping shape cloud-native architecture and technical standards.

Lead enterprise client reporting and manage Proof’s data team. Own report accuracy, delivery, client relationships, data pipelines, shared models, BI tooling, and internal data insights. Coach team members, guide technical decisions, review SQL and data models, partner with Engineering and Product, resolve reconciliation issues, and improve automation. The role will establish data quality standards and help the company use trusted data to support growth, internal tools, and AI systems.

Leads marketing and loyalty analytics strategy, measurement frameworks, forecasting, experimentation, campaign reporting, retention analysis, churn modeling, and segmentation. Partners with Marketing and Finance to establish performance goals and translate complex data into actionable recommendations. Automates reporting using analytics platforms, marketing automation tools, experimentation methods, and AI-enabled workflows while identifying opportunities to grow and retain high-value consumers.

Leads company-wide measurement and causal strategy, defining canonical metrics, semantic data architecture, experimentation standards, and strategic models such as marketing mix, elasticity, incentive sensitivity, and forecasting. Partners with engineering and analytics teams to build scalable, production-ready analytical frameworks, improve scientific rigor, and guide executive decisions. Provides organization-wide technical leadership, develops modeling standards, and applies advanced statistical and causal methods to high-impact business problems.

Own the scientific and product roadmap for Fetch’s monetization data products, including audience creation, expansion, causal measurement, and audience improvement. Lead methodology, evaluate evidence, balance reach, precision, revenue, and cost, and convert bespoke studies into scalable products. Partner with Product, Engineering, Sales, and GTM on pilots, positioning, and customer value. Remain hands-on with SQL, Python, statistical modeling, and causal inference while mentoring scientists and influencing senior stakeholders.