Top Data Analytics Jobs in Toronto

The Data Engineer will architect and implement advanced analytics capabilities, manage data pipelines, and optimize data processing using AWS cloud and various analytical tools.

Supports daily investment risk reporting, risk-factor impact analysis, what-if simulations, trade completeness validation, and market-data quality checks. The intern analyzes investment product valuation and risk drivers, helps improve risk calculation processes, and collaborates with investment risk, data management, model vetting, financial operations, and IT teams. The role requires quantitative skills and experience with Excel, VBA, SQL, and preferably Python and Bloomberg.

Leads data architecture, integrations, product analytics, and client-specific analyses for an AI-enabled life sciences platform. Partners with engineering, product, solutions, and client services teams to build scalable, AI-ready data systems and improve measurement insights. Manages and mentors data analysts and engineers while supporting pharmaceutical and life sciences customer needs.

Supports institutional client account management through market and portfolio analysis, performance attribution, customized reporting, presentations, and client inquiries. Builds dashboards and reporting resources, identifies workflow improvements, and helps implement AI-enabled tools, automation, and business intelligence solutions. Coordinates cross-functional initiatives, documents processes, supports change management, and trains users on new technologies. Requires financial markets knowledge, coding ability, strong analytical skills, and effective stakeholder communication.

Leads Moneris’ marketing analytics function and team, developing campaign measurement strategies, statistical models, dashboards, attribution analyses, and actionable recommendations. Partners with Marketing, Sales, Product, Technology, and Finance to improve acquisition, retention, engagement, ROI, data quality, and analytics processes. Establishes an analytics Center of Excellence, manages validation and testing, prioritizes projects, allocates resources, and translates complex multi-source analyses into business decisions.

Design and build trusted commercial and financial data models, own end-to-end delivery from requirements to documentation, collaborate with commercial and finance stakeholders, apply strong SQL/dbt skills and basic Python, and use AI responsibly to accelerate analytics and foundational systems for advanced analytics.

Manage and grow strategic partnerships across US & Canada for a Google Cloud-focused AI and data consultancy. Drive co-sell initiatives, GTM planning, partner enablement, joint marketing, event execution, partner onboarding, and pipeline generation while tracking partner performance and reporting impact.

Design and build a unified analytics instrumentation platform using Go, Kubernetes, ClickHouse, and NATS. Improve data emission, transit, and quality for usage and billing, provide incident response and root cause analysis, collaborate with product and data teams to drive adoption, make tradeoffs for reliability and performance, and mentor engineers through reviews and documentation.

Build scalable GCP-based data pipelines and datasets for insurance fraud analytics. Develop and evaluate machine learning, NLP, and Generative AI models using claims and unstructured SIU data. Apply explainable AI techniques, conduct exploratory analysis, automate data workflows, document architectures, and support privacy, fairness, and compliance requirements while collaborating with investigators and business analysts.

Lead design and implementation of full‑stack authentication and account security features for Stripe Organizations. Build APIs, frontend UX/UI, and analytics to secure and simplify login, SSO, MFA, RBAC, and account workflows. Mentor engineers, drive technical decisions, collaborate with Security and partner teams, and create reusable auth building blocks used across Stripe products.

Lead change strategy and stakeholder engagement for technology and AI-enabled transformations. Design and execute adoption programs, define adoption metrics, monitor readiness and risks, and apply human-centered change frameworks. Support AI adoption, workforce impacts, and delivery excellence while partnering with clients, analytics, and product teams to link behavior change to business outcomes.

Lead development and management of raw material supply, demand, and inventory forecasts. Maintain S&OP process, create and evaluate item-level statistical forecasts, coordinate cross-functional reconciliation of variances, support inventory strategy and purchasing execution, and produce demand planning reports to align sales, operations, and supply chain.

Lead analytics engineering for TravelIQ and Arcadis Tolling by designing analytics architecture on Microsoft Fabric, building curated analytical data models, and delivering customer-facing Power BI reports. Combine near-real-time and historical operational data, ensure data quality and governance, and identify opportunities for new embedded analytics and data-driven product features.

Build high-performance API services and production-grade Generative AI systems. Design and implement end-to-end RAG pipelines, vector stores, embeddings, and latency-optimized generators. Develop agentic AI using LangChain/CrewAI/Semantic Kernel, integrate LLM APIs and AWS Bedrock, manage CI/CD and ML pipelines, and apply evaluation frameworks to detect hallucinations and optimize performance.

Perform story- and data-driven analysis to design automated solutions, translate business needs into technical designs, build Power BI dashboards, administer Atlassian tools (Jira/Confluence/JSM), support Data Center to Cloud migration, develop software features (Java/SQL/Python), troubleshoot production issues, and support SDLC, IT governance, procurement and contract management.

The Analytics Manager will lead analytics for the Payments and Identity lifecycle, manage a team of analysts, develop KPIs, and ensure data quality while collaborating with cross-functional teams.

Senior Customer Analytics Manager leads strategic analytics across the customer lifecycle—acquisition, onboarding, engagement, retention, monetization, and expansion. Partners with cross-functional teams to translate business questions into rigorous analyses, experimentation, forecasting, and predictive models; defines KPIs; builds dashboards and decision tools; mentors analysts; and drives scalable, AI-enabled analytics that inform executive decisions and improve customer value.

Leads MAU analytics and data science, managing decision scientists and data scientists while setting quality standards. Owns MAU driver modeling, forecasting, causal analysis, experimentation, and exploratory work supporting growth strategy. Establishes consistent analytical practices, partners with revenue, product, engineering, and data engineering leaders, and embeds the team in roadmap planning. Promotes AI-native workflows through coding agents, automation, and analytical tools while coaching team members into strategic advisors.

Deploys, integrates, and scales enterprise generative AI solutions in customer cloud environments. Responsibilities include architecting GCP and Vertex AI infrastructure, deploying and optimizing LLMs, building RAG data and vector search pipelines, managing GPU/TPU workloads on GKE, automating infrastructure with Terraform, and advising clients on AI safety, prompt engineering, hallucinations, and inference costs. The role also collaborates with research and platform teams and requires travel to client sites.

Manage and grow client relationships for multiple marketing accounts, coordinate campaign execution across paid media, SEO, content and email, report on KPIs, run client check-ins and strategy calls, identify upsell opportunities, and collaborate with internal teams to ensure retention and performance.

The VP of Data & AI Engineering leads Duck Creek's enterprise data and AI strategy, overseeing global teams, implementing scalable platforms, and ensuring data governance and responsible AI practices.

Design and build an AWS-based lakehouse and scalable enterprise data platform. Create governed data products and automated Python/SQL pipelines. Establish standards for data quality, governance, security, observability, and CI/CD. Partner with engineering, product, finance, and business teams to enable analytics and AI and define the platform's long-term vision.

Generate and nurture pipeline through high-touch outbound and inbound activities: segment CRM cohorts, write personalized outreach, use AI tools to create assets, qualify intent to SAL criteria, book meetings, and feed conversion insights back to improve targeting.

Senior SDET who contributes across frontend and backend systems by building and maintaining features with a strong testing mindset. Develop and maintain automated tests, improve test coverage and CI/CD, investigate bugs, support regulatory and compliance testing strategies, and balance automation with exploratory and risk-based testing.

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.