Top Data Analyst Jobs in Montreal

The Staff Data Analyst will lead data strategy for Compliance, analyzing regulatory engagements, and driving effectiveness through actionable insights and collaboration across various teams.

Early-career data analyst supporting Manulife’s Data Office through data solutions, automation, AI-enabled applications, dashboards, pipelines, business analysis, and process improvement. The role involves translating business needs into requirements and scalable solutions, applying data governance, supporting testing and adoption, and collaborating with stakeholders across customer experience, analytics, and continuous improvement initiatives.

Lead marketing analytics and data strategy for B2B SaaS Marketing. Design measurement frameworks, own attribution, funnel and lifecycle analysis, forecasting, and marketing investment recommendations. Partner with senior marketing leaders and cross-functional teams to build data foundations, enable AI-assisted analytics, and mentor analysts to raise analytical standards.

Analyze and integrate data from multiple systems to produce client reports, identify group benefits utilization trends, support plan design decisions, and deliver customized analytics. The role also improves existing reports and workflows, supports Account Executives and Advisors, recommends system enhancements, and maintains timely, accurate solutions for Quebec-based clients. French-English bilingual communication is required.

Demonstrate ERP reporting capabilities, analyze customer requirements, design SQL- and Excel-based reporting solutions, develop proof-of-concepts and customer reports, provide initial product support, and train clients, partners, and colleagues. The role also requires creating documentation, estimating project scope, and maintaining expertise in Velixo and integrated ERP systems.

Design, build and maintain robust Databricks data pipelines and ETL processes on cloud (Data Lake/Lakehouse). Develop transformations in Python and Spark, apply data quality best practices, integrate CI/CD (Azure DevOps), document solutions, and participate in Agile rituals to support analytical platforms and decision-making.

Analyzes pipeline, funnel conversion, campaign performance, retention, and revenue drivers across Sales, Marketing, and Client Success. Builds and maintains dbt models, data marts, quality controls, documentation, and BI-as-code assets using Salesforce and modern data-stack technologies. Partners with stakeholders to turn ambiguous questions into decisions and advances AI-agent workflows for faster analytics and reporting.

Analyze large datasets to produce dashboards, metrics, and insights supporting product, care, and business decisions. Build BI infrastructure, run A/B and multivariate tests, perform ad-hoc analyses and visualizations, and collaborate with stakeholders and data scientists to inform strategy and operations.

Manage daily Data team operations, research and analyze datasets, build internal and external reports (including an annual Startup Ecosystem Ranking), and maintain/improve Python scripts and data infrastructure to deliver actionable insights.

Partner with Product to drive strategy using deep-dive analyses, build and validate analytics tracking and data models, integrate AI-assisted workflows, and design/interpret A/B tests to inform product decisions.

Analyze player behavior, game health, engagement, retention, monetization, and live-service performance for Marvel Strike Force. Own KPI reporting, explain trends and variances, evaluate experiments, validate analytics tracking, and deliver actionable insights to product, design, marketing, live operations, and leadership teams. Use SQL, Python, reporting tools, and AI-enabled workflows while maintaining data quality, privacy, and analytical rigor.

As a Data Analyst at Stripe, you'll build data products, metrics, and dashboards, analyze large datasets, and communicate insights to drive business decisions.

The Data Analyst will build key data models and metrics for decision-making, partner with stakeholders for analytical insights, create datasets for self-service reporting, and ensure data reliability through quality checks.

Design and run evaluation frameworks to assess third-party vendor data for fraud detection. Build lift analyses, backtests, and champion/challenger tests; profile vendor datasets for quality and coverage; collaborate with fraud, product, and data engineering to define ingestion and testing requirements; translate findings into actionable recommendations and maintain an internal knowledge base; support ad hoc analyses on fraud trends and model performance.

Collects, processes, analyzes, and interprets complex data to support strategic and operational decisions. Develops reports and analytical tools, identifies process and data-management improvements, advises stakeholders, coordinates analytical initiatives, and provides recommendations to decision-making bodies. Acts as a data-use subject matter expert and coach across the organization.

Analyze go-to-market performance across marketing, sales, revenue operations, and finance. Build reporting connecting campaigns to pipeline and revenue, investigate funnel and attribution questions, support budget analysis and planning, improve data quality, and communicate insights to technical and non-technical stakeholders. Collaborate with AI and Data Engineering teams on marketing data agents, architecture, and source tables.

Collects, processes, analyzes, and interprets complex data to support strategic business decisions. Leads analytical mandates, develops reports, identifies process and data-management improvements, advises senior management and decision-making committees, and serves as a subject-matter expert and coach across multidisciplinary initiatives.

Analyzes business needs and data management initiatives within large-scale transformation projects. Partners with stakeholders to define requirements, facilitate workshops, model processes, assess change impacts, translate business rules for technology teams, support product backlogs and testing, and contribute to optimized solutions and continuous improvement.

Analyze high-volume data, develop tracking methods and frameworks with engineers, create visualizations, identify trends, and generate statistical insights to support business decisions. The role also involves prototyping solutions, understanding player behavior, and communicating findings in a dynamic gaming environment.

Own end-to-end product analytics for the Pro team: build production-grade pipelines and dbt models, define north-star metrics, design A/B tests and causal inference frameworks, create dashboards and metric frameworks, embed AI tooling, and translate insights into product recommendations for leadership.

Collects, processes, analyzes, and interprets data from multiple databases to support business decisions. Builds complex reports, identifies data and process improvement opportunities, advises clients on data management tools, conducts descriptive and diagnostic analyses, and presents recommendations to stakeholders. Coordinates across sectors to operationalize analytics solutions and serves as a data-use resource for the organization.

Lead analytics to improve player experience, engagement, retention, and monetization. Analyze player lifecycle, run cohort and funnel analyses, design and evaluate A/B tests, build dashboards, define product metrics, investigate KPI changes, and partner with product, marketing, engineering, and data teams to deliver data-driven recommendations and improve data quality.

The Product Data Analyst will analyze operational data, develop analytics frameworks, create dashboards in Tableau, ensure data integrity, and collaborate across teams to enhance decision-making and reporting.

Collects, processes, analyzes, and interprets data from multiple databases to support business decisions. Develops complex reports, identifies data and process improvements, advises stakeholders on data management tools, conducts descriptive and diagnostic analysis, and presents recommendations aligned with organizational needs.

Build and maintain financial and operational datasets and Power BI dashboards; map metrics across eCommerce and engineering sources; support AOP, ARR, pricing and retention modeling; run CI/CD pre-production checks, monitor processes, and partner cross-functionally with Finance, Marketing/Retention, and Product.