Top Data Analyst Jobs in Montreal

Staff Data Analyst responsible for raising analytical standards across the team, building user segments, improving dashboards and automated monitoring, and connecting behavioral insights to revenue, retention, cost, and growth goals. The role partners on experimentation, standardizes analytical methods, reviews complex work, mentors analysts, and communicates decision-ready narratives to senior stakeholders. Advanced SQL, Python, experimentation expertise, and strong user analytics skills are required.

Develop and maintain ETL/ELT data pipelines within a cloud-based data lakehouse architecture. Responsibilities include data transformation, software lifecycle participation, testing, documentation, third-level support, proof-of-concept development, operational reporting, and semantic-layer support. The role requires advanced SQL and Python/PySpark, experience with Spark, Delta Lake, Parquet, multiple databases, privacy-regulated data, and multi-cloud environments.

Design, build, and maintain scalable ETL/ELT data pipelines and lakehouse architectures on GCP. Implement orchestration (Prefect), CI/CD, Terraform IaC, and DataOps practices. Collaborate with data scientists and product teams to enable analytics and AI while ensuring governance, security, and reliability.

Analyze and extract data from enterprise databases, optimize SQL queries, and design reporting models using Excel and Power BI. Support data architecture, validation, integration, requirements gathering, data modeling, governance, process documentation, and automation recommendations. Collaborate with business intelligence and IT teams on reporting solutions, system changes, and data product roadmaps. The role requires strong communication skills in French and English.

Supports data architecture and analytics through SQL query development, data extraction, validation, modeling, reporting, and visualization. Builds and manages Excel and Power BI reports, collaborates on enterprise database integrations, analyzes system impacts, documents processes, gathers reporting requirements, and recommends automation and BI solutions. The role also contributes to data governance, solution design, roadmap prioritization, and communication with business, BI, IT, and development teams.

Collects, processes, analyzes, and interprets data to support business decisions. Identifies stakeholder needs, develops and updates reports, improves data methods, advises on data management tools, validates analytical datasets, and provides recommendations. The role also supports data-related projects, operational initiatives, and stakeholder guidance across business functions.

Business Intelligence Data Analyst responsible for data profiling, warehouse solution design, impact analysis, technical documentation, SQL analysis, Power BI reporting, Microsoft Fabric semantic modeling, and data quality initiatives. The role bridges business processes, operational systems, and enterprise data warehouses while coordinating data engineers through integrations, migrations, and implementation. It also involves stakeholder management, business process analysis, and consulting on enterprise IT solutions.

Analyze data from multiple channels and databases, develop reporting and interpretation tools, identify process improvements, and advise stakeholders on complex analytical initiatives. Perform descriptive and diagnostic analyses, translate business needs into data solutions, coordinate implementation, and provide recommendations to support strategic decision-making.

Analyzes operational data using Python, Excel, SQL, Power BI, and Databricks to identify trends, anomalies, and process improvement opportunities. Builds and maintains dashboards, validates data across stakeholders and systems, performs root-cause analysis, and presents actionable findings to senior management. The role supports a lead analyst and focuses on hands-on analysis rather than architecture, engineering, or people management.

Supports data extraction, management, quality assurance, cleaning, validation, harmonization, statistical and qualitative analysis, and public health program evaluation. Develops analytic datasets and contributes to technical memoranda, reports, presentations, tables, and figures. Analyzes implementation measures and mixed-methods data, interprets findings, and recommends program improvements. Requires collaboration with government and public health stakeholders, use of Microsoft Office and CDC administrative systems, and the ability to obtain and maintain required customer clearance.

Coordinate client data migrations onto Computershare’s SCRIP platform, including requirements gathering, data inspection, mapping, validation, testing, business sign-off, and collaboration with developers and operations teams. Plan and configure large or extraordinary corporate actions while managing high-risk operational events, concurrent projects, stakeholders, change processes, and tight deadlines.

Designs, builds, maintains, and validates Sisense dashboards and tabular reports for customers. Translates complex business and compliance requirements into accurate reporting solutions, models data, troubleshoots production incidents, and ensures performance and consistency with application data. Collaborates with Product Owners, QA, engineering, data, and customer-facing teams to define requirements, validate results, maintain reporting standards, document solutions, and support agile delivery.

Analyzes and valorizes data to support business insight and decision-making. Requires a bachelor’s degree, at least four years of relevant experience, and French-language proficiency. The role is hybrid and includes standard employee benefits, pension, insurance, vacation, and wellness reimbursements.

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.

Collects, extracts, analyzes, interprets, and validates data to support business decisions. Develops and maintains management indicators, reports, data analytics solutions, quality-control procedures, documentation, and training materials. Participates in SAS ETL development, supports data governance and publication quality, and assists users with data requests and interpretation.

Owns the lifecycle of international data products from business requirements through engineering delivery, release, and production monitoring. The role investigates data issues using SQL and Python, validates data quality, defines acceptance criteria, manages requirements and traceability, and translates findings into actionable business insights. It partners closely with stakeholders and data engineering teams to resolve incidents, improve data trust, and establish monitoring and stewardship practices.

Analyze and document source data, business rules, functional specifications, data models, and ETL requirements for a large-scale Azure data platform. Support architects, developers, business experts, and QA through data profiling, Power BI platform configuration, test case creation, validation scripts, and execution. Quickly assume ownership of an ongoing project while managing complexity and meeting deadlines.

Own regulatory analytics and reporting by building audit-ready datasets, pipelines, dashboards, compliance metrics, and automated workflows. Analyze complex data, monitor quality, support audits and regulatory exams, and deliver actionable insights to compliance teams and leadership. Translate regulatory requirements into accurate reports using Python, SQL, dbt, and Airflow while partnering cross-functionally to manage data issues and remediation.

Own end-to-end financial data analysis, including SQL and dbt data modeling, Looker dashboard development, data quality validation, reconciliation, and stakeholder-facing insights. Partner with Finance and Payments to support reporting, planning, revenue analysis, cost insights, and transaction-level analytics. Translate ambiguous business needs into reliable technical solutions while improving documentation, QA, modeling, and data architecture practices.

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.

Build and maintain BI dashboards, reporting tools, and automated data workflows. Analyze customer behavior, funnels, retention, engagement, and product adoption to deliver actionable insights across Product, Operations, Marketing, Finance, and Leadership. Use AI tools and APIs to improve analytics workflows, self-service exploration, data-quality checks, and insight generation while validating accuracy, privacy, and business impact.

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.

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.

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.