Top Data Analyst Jobs in Toronto

Build and maintain data structures, pipelines, reporting, visualizations, and data processes using data lake and Snowflake technologies. Ensure data quality, documentation, and usability while performing investigative analysis on large datasets. Collaborate cross-functionally to identify business problems, recommend solutions, and support end-to-end deployment. The role requires a quantitative bachelor’s degree, coding experience, SQL or dbt familiarity, and data pipeline experience.

Serve as the functional expert for SAP financial data, ensuring data integrity and alignment with P&L/Balance Sheet. Support SAP BW-IP planning, Power BI and BW reporting, troubleshoot complex issues, optimize BW data models, and enable integrations with modern data platforms like Databricks and OneStream.

Build and maintain Power BI dashboards, data models, SQL queries, and Crystal Reports. Administer reporting infrastructure, validate data, document reporting processes, gather management requirements, and provide user training and support. The role also includes light HTML, CSS, and Bootstrap work, report embedding, presentation materials, version control with Git and GitHub, and collaboration with software developers on manufacturing and business systems.

Supports ALM Portfolio Management and Execution Data by troubleshooting hedging activities, automating work products, analyzing hedge performance, investigating balance-sheet risk metrics, and developing expertise in risk measurement and simulation. The role requires finance or quantitative studies, prior finance co-op experience, coding skills, and knowledge of financial products and derivatives. This is a four-month, full-time student co-op position in Toronto.

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.

Supports analytics development projects by analyzing, validating, cleaning, and extracting data; creating visualizations; and ensuring project delivery meets approved timelines, scope, budgets, standards, and guidelines. The intern will use analytics tools, help maintain data quality and integrity, and contribute to data-driven information system requirements within RBC’s CLAO Shared Services Team.

Analyze business and technical requirements, assess data quality and flows, and define reporting and dashboard improvements. Use SQL, Azure Synapse, and MicroStrategy to produce actionable insights, identify process automation opportunities, and communicate findings to stakeholders. The role requires independent work, strong analytical problem-solving, and confidence leading discussions.

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.

Analyze digital campaign data, identify trends and insights, develop recurring reports, collect data from multiple sources, and present recommendations to stakeholders. The role manages reporting timelines, supports digital strategy planning, collaborates with industry partners, and helps improve measurement processes. Candidates should be skilled in relational databases, data visualization, analytics, data cleaning, and problem-solving, with media and advertising technology experience considered an advantage.

Analyze business and product data to support marketing initiatives, product development, forecasting, experimentation, reporting, and personalized recommendations. Build and optimize SQL and dbt data models, create Looker-based insights, monitor KPIs, design statistical experiments, and translate complex findings into actionable recommendations for technical and non-technical stakeholders. Partner closely with a Product Manager and cross-functional product team.

Owns custom data solutions by triaging client requests, assessing feasibility, querying and validating data in BigQuery, and building one-off dashboards or reports. Partners with Sales, Customer Success, Product, leadership, clients, and Engineering to scope work, communicate data limitations, present findings, define requirements, and hand off validated solutions for technical implementation.

Partners with clients and internal teams to analyze complex datasets, develop measurement strategies, conduct research and statistical analyses, and deliver actionable insights. Creates reports, dashboards, and recommendations supporting sales, marketing, optimization, and go-to-market decisions. Leads research and measurement activities, tracks market trends and KPIs, communicates findings clearly, and stays engaged with program operations through meetings and field ride-alongs.

Partners with clients and internal teams to develop data-driven strategies, measurement approaches, reports, and actionable insights. Collects, cleanses, transforms, models, and analyzes complex datasets using business intelligence and statistical tools. Leads standardized measurement, research, testing, and optimization initiatives, communicates findings through compelling reports, and supports sales and marketing objectives through market analysis and recommendations.

Analyzes and transforms commercial data into trusted, AI-ready assets supporting sales and demand insights. Builds data pipelines, applies data warehousing and ETL/ELT practices, maintains data quality and governance, and uses AI, machine learning, statistical modeling, and visualization to generate business insights. The role also develops functional specifications, supports privacy and compliance requirements, collaborates with data stewards, and identifies opportunities to enrich analytics with digital data sources.

Early-career Data Analyst in Manulife’s 18-month GRO Operations program. The role supports data analytics, automation, AI-enabled solutions, dashboards, data pipelines, governance, process improvement, and stakeholder requirements. Depending on placement, responsibilities may include data solution engineering, business analysis, or development using Power Platform, Azure, Python, and Power BI. The position involves end-to-end solution delivery, testing, communication, continuous improvement, and translating business needs into actionable insights and digital solutions.

Support wholesale credit data governance by maintaining data assets and documentation, automating data monitoring tools, improving data-quality reporting, and establishing validation processes. The intern will use Python, SQL, Tableau, and AI to enhance data governance and ensure reporting accuracy. Responsibilities also include documenting quality assurance evidence and supporting data-management initiatives.

Own marketing analytics and measurement to improve acquisition, campaign performance, and marketing ROI. Lead multi-touch attribution, marketing mix modeling, A/B testing, incrementality studies, and KPI analysis. Partner with Marketing and Product to improve data quality, translate complex findings into recommendations, influence senior stakeholders, and coach peers on analytics practices.

Develop full-stack enterprise web applications using Python and Dash, build backend data-processing services with Pandas, create interactive data interfaces, integrate AI/ML models, and apply software engineering practices including testing, documentation, version control, code reviews, and deployment pipelines. Collaborate with stakeholders, product managers, and developers to refine requirements and deliver scalable data solutions.

Analyzes and improves end-to-end business and finance processes through data analysis, documentation, governance, and continuous improvement. Supports ERP and IBP transformations, process mapping, data quality remediation, UAT, change management, training, and stakeholder alignment. Monitors process performance, identifies root causes, translates business needs into functional requirements, and helps ensure financial data integrity, reporting alignment, and process adoption.

The Business Intelligence Analyst partners with finance and operational stakeholders to analyze complex data, identify trends and KPIs, and develop executive reporting. Responsibilities include data cleaning, governance, process improvement, reporting automation, and exploring new tools such as RPA. The role requires collaboration across business and IT teams, project management, troubleshooting data quality issues, and communicating insights to technical and non-technical audiences.

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.

Design, develop, and maintain reporting, dashboards, and data models to deliver financial and performance insights. Write and optimize SQL, ensure data quality, and partner with stakeholders to translate requirements into analytical solutions using BI tools and cloud data platforms.

Supports business and data analysis initiatives across insurance, including process improvement, licensing records, Salesforce data cleanup, data classification, release planning, UAT, compliance controls, and business needs analysis. Collaborates with IT, operations, product development, and distribution teams to improve applications and deliver data-driven solutions. Documents processes, maintains procedures, analyzes data, and supports risk and regulatory requirements.

Support data product delivery by building and maintaining pipelines, integrations, databases, warehouses, analytics systems, and data quality processes. Write SQL and Python automation, support testing and SDLC practices, monitor CRM and investment data, and work with cloud infrastructure. Analyze workflows, process securities and products in Eagle, and collaborate with engineering, analytics, and AI teams. This student co-op requires strong quantitative ability, software engineering fundamentals, cloud familiarity, and interest in data governance and AI/ML.