Top Data Analytics Jobs in Toronto

Lead Oracle Analytics reporting and analytics workstreams for client implementations. Manage consulting teams and client relationships, gather requirements, design dashboards and reports, oversee data integration and governance, manage project scope and budgets, mentor consultants, and support proposals and business development. The role partners with Oracle ERP, HCM, SCM, and EPM teams to deliver integrated analytics solutions and requires up to 50% travel.

Leads global people analytics, HCM reporting, and U.S. compliance reporting using Workday and PeopleSoft. Develops reporting solutions, gathers requirements, manages testing, troubleshoots integrations and workflows, maintains data mappings, and supports data audits. Partners with HR stakeholders on projects, ad hoc requests, privacy reviews, documentation, and training. Oversees reports including EEO, AAP, VETS-4212, California pay data, and other regulatory submissions while improving enterprise reporting processes.

Supports Treasury through financial analytics, regulatory reporting, dashboards, and data-driven decision-making. Uses SQL, Excel, Kyriba, and business intelligence tools to extract and analyze treasury data, manage recurring reports, improve processes, and ensure accuracy. Collaborates with Product, Engineering, Data Science, Finance, IT, and senior stakeholders on cash and collateral management projects and translates business requirements into technical reporting solutions.

Leads analytics collaboration between the analytics department and newsrooms, translating audience data and market insights into editorial recommendations. Builds data-driven newsroom culture, develops workshops, establishes KPIs, monitors reports, analyzes trends, and writes strategic briefs. Partners with editors, reporters, product teams, and audience specialists to improve story decisions, page views, and reader engagement.

Lead analytics for international market expansion, establishing scalable measurement, reporting, dashboards, data models, and operational analytics frameworks. Identify growth and profitability opportunities, define metrics, analyze performance, design experiments, and translate findings into business recommendations. Partner cross-functionally with Product, Risk, Finance, Commercial, and Operations, lead metric reviews, influence leadership decisions, and mentor junior analysts.

Build and operate scalable database infrastructure for a multi-tenant SaaS data platform. Responsibilities include ingestion, query planning, distributed query execution, storage formats, APIs for AI-agent retrieval, tenant isolation, observability, and cloud-native deployment. The role requires architectural ownership, production software delivery, collaboration across product and infrastructure, and pragmatic decision-making in an early-stage, high-autonomy environment.

Build repeatable AI product packages that drive customer adoption from initial use to team-wide deployment. Partner with Product, Engineering, and Customer Success to solve technical blockers, translate customer needs into use cases, support Motion’s internal marketing team, and help influencers adopt Runneth. The role combines customer relationship management, marketing knowledge, hands-on AI building, technical problem-solving, and evolving customer success ownership.

Build and own AI-powered product features across Motion’s platform, agentic harness, and customer-facing Runneth product. Responsibilities include understanding user problems, making architectural decisions, shipping rapidly to production, owning significant initiatives, improving systems across the stack, and resolving blockers in a high-ownership environment. The role is an individual contributor position suited to a technical founder or early engineer who has personally built and shipped startup products.

Build customer-facing growth and engagement features across acquisition, activation, retention, and monetization. Design and analyze experiments using feature flags and A/B testing, develop instrumentation and event pipelines, improve performance and scalability, and contribute to architecture and technical strategy. Collaborate with Product, Design, Data, Marketing, and Customer Success while mentoring engineers and promoting reliable, high-quality engineering practices.

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.

Own the vision, strategy, roadmap, requirements, KPIs, and go-to-market execution for insurance-focused data products. Collaborate with engineering and data science teams on products, proofs of concept, and demos; communicate progress to executives; and ensure compliance with BFS regulatory, risk, and compliance requirements.

Support hundreds of SMB customers using Motion and Runneth through onboarding, training, proactive outreach, written support, re-engagement, and accurate HubSpot documentation. The role requires fast learning, high-volume communication, strong organization, empathy, and comfort using AI daily. Candidates should have substantial hospitality experience and demonstrate initiative toward technology, marketing, ecommerce, or building new systems.

Build trusted finance data products, scalable pipelines, analytical models, automated reconciliations, dashboards, and finance data lake capabilities. Partner with Finance, Accounting, Data Engineering, Product, and Platform teams to resolve discrepancies, improve reporting, automate close activities, establish data quality controls, and enable near-real-time insights. Evaluate AI-assisted automation and own production reliability, testing, documentation, monitoring, and exception handling.

Design, build, and optimize scalable ETL/ELT data pipelines and analytical datasets using Snowflake and Qlik. Own end-to-end delivery from requirements to support, implement data modeling and performance/cost optimizations, automate ingestion and transformations, monitor and troubleshoot pipelines and reports, and collaborate with claims, analytics, and actuarial teams to translate business needs into robust data solutions while supporting migration and modernization efforts.

Manages digital campaign reporting, analytics, tagging, pixel implementation, tracking QA, data validation, dashboards, and optimization insights. Uses AI and automation to improve reporting efficiency while maintaining data quality and human oversight. Analyzes campaign performance, identifies trends and anomalies, troubleshoots measurement issues, and translates results into actionable recommendations for media, account, digital, and client teams.

The Analytics Engineer partners with business stakeholders and data teams to translate reporting needs into scalable analytics solutions. Responsibilities include integrating disparate datasets, developing optimized SQL and reporting tables, building executive dashboards and KPI frameworks, maintaining semantic-layer definitions and governance, and improving analytics architecture. The role also applies software engineering practices, supports code reviews, and documents data models. Candidates should have strong SQL skills, analytics experience, dashboard development expertise, and proficiency in analytical programming languages such as Python or R.

Own Affirm Canada's post-delinquency collections and recoveries strategy, including digital and outbound collections, agency placement, and post-charge-off recovery. Analyze performance, design experiments, quantify initiatives, manage vendor scorecards, and improve recovery outcomes. Partner with Product, Legal, and Compliance to ensure alignment with Canadian consumer protection, collections, and privacy regulations, while collaborating with international teams.

Drive new enterprise revenue and customer expansion by selling AI-powered SaaS solutions. Manage complex 6–12 month sales cycles, build executive relationships, develop value propositions, maintain pipeline accuracy, lead virtual account teams, and close high-value deals. Translate technical AI capabilities into measurable business outcomes, guide customer adoption, collaborate with marketing, product, and customer success teams, and exceed quarterly pipeline and revenue targets.

Leads business process optimization and digital enablement for CMC Regulatory Affairs. Responsibilities include process mapping, KPI development, regulatory systems and data improvement, automation and AI initiatives, cross-functional project delivery, change management, business requirements, dashboards, and implementation of technology-enabled solutions across pharmaceutical regulatory operations.

Develop and validate machine learning and statistical models for consumer credit risk, fraud, portfolio management, IFRS 9 forecasting, and borrower behavior. Build explainable predictive models, automate analytical processes, identify quality data sources, document methodologies, and translate complex findings into actionable business insights. The role partners with cross-functional teams and balances model performance, interpretability, scalability, and commercial impact.

Analyze product and customer data to define metrics, build dashboards, measure feature performance, connect usage to revenue, and guide product, commercial, and go-to-market decisions. The role partners with Product, Engineering, Finance, Design, and Commercial teams, improves data quality, works with fragmented data, and promotes data-driven decision-making across the organization.

Build and deploy production-grade GenAI and Agentic AI solutions for enterprise clients. Responsibilities include designing LLM applications, RAG systems, AI agents, and multi-agent workflows; integrating APIs, databases, and business applications; deploying and scaling cloud solutions; optimizing reliability, security, performance, and cost; and partnering with technical and business stakeholders from initial problem definition through production deployment.

Leads enterprise market intelligence and analytics for wholesale power and commodity markets. Builds forecasting, forward-curve, fundamental modeling, scenario analysis, dashboards, and governance capabilities; supports investment, development, M&A, financing, asset optimization, hedging, and portfolio decisions. Establishes a global center of expertise, manages analysts and external providers, and communicates market insights and uncertainties to executives, investment committees, and boards.

Analyze marketplace trends, financial and product levers, budgets, and competitive insights to support Lyft’s Rideshare growth strategy. The role develops forecasting, reporting, business reviews, recommendations, and execution plans while partnering with cross-functional stakeholders and leadership. It requires strong SQL, quantitative analysis, communication, attention to detail, and the ability to improve processes and operate effectively amid ambiguity and change.

Develop and refactor Python optimization algorithms using object-oriented programming for supply chain, inventory allocation, replenishment, and pricing optimization. Build predictive models, apply advanced statistical and optimization techniques to large datasets, and deliver data-driven solutions for retail and CPG clients. Collaborate with business stakeholders and data science teams, communicate analytical insights, coach colleagues, and apply current data science practices to business problems.