Top Data Analytics Jobs in Toronto

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.

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.

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.

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.

Design, develop, and maintain backend microservices for reporting, analytics, data export and data-driven features. Collaborate with product, data, and engineering teams, implement APIs for BI tools, ensure data quality/governance, participate in design reviews, mentor junior engineers, and support agile delivery.

Lead architecture and implementation of scalable analytics, reporting, and AI-driven data intelligence capabilities. Own end-to-end services, drive technical direction, mentor engineers, ensure operational excellence, and collaborate with product and design to deliver secure, high-performance, maintainable solutions using cloud-native and data-platform technologies.

Design, build, and maintain production data infrastructure and pipelines; automate workflows; enable cross-team data access; perform large-scale data wrangling, statistical analysis, and visualization to deliver business insights.

The Customer Success Manager will educate clients on creative strategy, build relationships, and help maximize their use of Motion's platform through engagement and tailored support.

The Machine Learning Engineer will create scalable ML systems, build production data pipelines, and ensure robust deployment and monitoring of solutions.

Lead governance, maintenance, and enhancement of Anaplan and finance data processes. Ensure month/quarter close data integrity, support budgeting/forecast cycles, deliver reporting and ad hoc financial analysis, drive process improvement and automation, and manage a team of reporting/data resources while partnering across Finance, Technology, Data, and Risk stakeholders.

The GTM Analytics Lead will drive data-driven decisions across marketing, sales, and finance, optimizing performance and ensuring sustainable revenue growth through analysis and real-time insights.

Lead end-to-end process mapping and analysis using BPMN and process/task-mining tools to identify inefficiencies, create As-Is/To-Be documentation, define BRDs, model future-state scenarios, and drive cross-functional implementation with executive reporting.

As a Senior Full Stack Software Engineer, you will own product features, design scalable systems, mentor teammates, and ensure coding excellence while collaborating with cross-functional teams.

Lead people-analytics strategy for Talent Acquisition and Talent Development. Drive end-to-end analysis of complex workforce problems, deliver evidence-based recommendations to senior leaders, ensure data governance and quality, coach junior analysts, build metrics frameworks, and conduct benchmarking and external research to inform workforce strategy.

Analyze, extract, clean, and model large datasets from multiple systems; build data warehouses and predictive models; create financial analyses, dashboards, and client reporting; implement and monitor models with client teams; support client relationships and contribute to proposals and marketing as needed.

Perform end-to-end BI and data analytics work: extract and transform large datasets, build data warehouses and predictive models, create dashboards and reporting, manage client relationships, oversee analysts, and contribute to proposals and marketing as needed.

You will manage a portfolio of accounts, working with marketing teams to optimize data usage, identify expansion opportunities, and facilitate onboarding and product fluency for eCommerce brands.

The role involves engaging with client teams to gather and analyze requirements, managing projects, acting as a point of contact, and collaborating with offshore teams to deliver analytical solutions.

The Account Executive will develop and implement sales strategies, manage relationships with vendors, and drive growth through effective prospecting and client engagement.

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

As an Engineering Manager, you'll lead a remote team, manage MLOps projects, ensure high performance, and mentor engineers while driving product strategies.