Top Data Analyst Jobs in Montreal

Leads ad hoc and exploratory analyses for medical and business questions across a global Medical Insights organization. Prepares and validates datasets, translates ambiguous stakeholder needs into decision-ready findings, and maintains analytical documentation. Tests new data sources, methods, and AI-enabled approaches through proof-of-concept analyses to guide platform investments and roadmap decisions. Partners with business and product stakeholders while supporting compliant, reproducible use of regulated data.

Analyzes insurance business data, develops and optimizes business intelligence tools, supports risk management and market analyses, defines KPIs, and improves data quality and governance. The role translates business needs into documented solutions and advises partners on data opportunities. It requires collaboration with pricing, claims, and business teams, along with proficiency in SQL and Power BI or Tableau.

Lead advanced analytics and data strategy for a live-service game, including telemetry systems, player behavior analysis, statistical modeling, dashboards, reporting, and data storytelling. Shape analytical best practices, mentor analysts, influence product and business decisions, and collaborate with multidisciplinary stakeholders on complex, high-impact challenges.

Analyzes, validates, cleans, enriches, and maintains commercial real estate data. Conducts market research, monitors data quality, identifies trends, prepares reports and visualizations, and provides insights supporting leasing, investment, development, and client decisions. Collaborates with research, data, GIS, and technology teams across Canada to improve data platforms, workflows, and market intelligence capabilities.

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

Analyze claims data using descriptive and diagnostic methods, develop metrics, query and prepare data, build analytical prototypes, and communicate insights. The role requires SQL, Python, R, and Microsoft Fabric, with responsibilities spanning data cleaning, reconciliation, modeling, and business problem solving. Experience in business intelligence, data management, and statistical analysis is required, while actuarial designations and ServiceNow, Jira, and Databricks experience are assets.

Analyze and enter building asset data, support AssetPlanner software implementation, configure databases, assist clients, conduct quality assurance reviews, prepare reports, and provide onboarding and training. The role also handles support requests, client interviews, software testing, bug reporting, and enhancement recommendations. Candidates need a university degree, strong Excel and MS Office skills, communication abilities, and the capacity to learn new software quickly. Facilities management, building science, engineering, asset management software, and client service experience are advantageous.

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.

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.

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.

Lead HR data capability delivery: translate business needs into functional specs and user stories, analyze HR/Workday data, design Workday reports, validate API integrations, triage data issues, and support testing and platform releases to enable workforce insights and AI-supported decisions.

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.

Collects, processes, analyzes, and interprets data from multiple sources to support business decisions. Develops complex reports, identifies data-management improvements, advises clients on analytics tools, conducts descriptive and diagnostic analyses, and presents recommendations to stakeholders. The role coordinates across sectors and supports complex business projects and initiatives.

Analyze and consolidate data from multiple systems, build scalable dashboards and executive reporting, define standardized metrics, ensure data quality, and translate complex datasets into actionable business insights. Partner cross-functionally with Sales, Marketing, Customer Success, Product, Finance, Operations, and Data Engineering to support decision-making and improve reporting processes.

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.

Conduct product and user behavior analysis, define KPIs, build dashboards and models, and design and evaluate A/B and multivariate experiments. Partner with product, lifecycle, marketing, data, and engineering teams to generate insights, improve user experiences, inform feature launches, and support growth decisions.

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.

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.

Analyze and derive value from data to support business insight and decision-making. The role requires a related bachelor’s degree, at least six years of relevant experience, and French-language proficiency. Additional combinations of education and experience may be considered.

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.

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.

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.

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.