Top Data Analyst Jobs in Toronto

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.

The Data Analyst will analyze large datasets, collaborate with stakeholders, develop data processes, and support data-driven decision-making to improve business efficiency.

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.

Lead the RTB reporting sub-team to ensure accurate, reliable finance and compliance reports. Build repeatable processes, documentation, and automation; own stakeholder relationships; mentor juniors; and drive cross-functional reporting initiatives from definition through execution.

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 Marketing, Sales, Operations, Strategy, and other teams to define analytical needs and deliver insights. Analyze customer behavior, product usage, engagement, retention, marketing performance, and operational outcomes using SQL, Python, Snowflake, visualization tools, and AI. Build dashboards and reports, identify trends and opportunities, and communicate practical recommendations to technical and nontechnical stakeholders.

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.

As a Data Analyst, you will support data migration initiatives by analyzing data structures, mapping source to target systems, and collaborating with stakeholders to document requirements and findings.

The Lead Financial Data Analyst manages financial instrument data collection, quality control, client inquiries, and operational risk compliance, while coaching others and improving processes.

Leads end-to-end analytics initiatives, partnering with stakeholders and clients to define metrics, analyze business and product performance, build dashboards, evaluate experiments, and deliver actionable recommendations. Collaborates with data engineers on reliable data models and reporting layers, establishes analytics standards, improves governance and data quality, communicates insights to leadership, and mentors junior analysts.

Administer Microsoft Purview catalogs and glossaries, maintain metadata and lineage, execute SQL-based data profiling and quality monitoring, investigate issues, develop governance automations and API integrations, prepare reporting materials, and maintain audit-ready SOC1 control evidence, exception logs, configurations, and procedures.

As a Data Analyst in People Analytics at Lyft, you'll manage data requests, build ThoughtSpot dashboards, and enhance insights using AI capabilities. You'll collaborate with HR to craft metrics and translate findings into actionable insights for leadership.

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.

Analyze complex datasets, build interactive dashboards, run predictive and statistical analyses, enforce data governance, automate reporting, and partner cross-functionally to deliver actionable insights.

Supports enterprise data governance across global property and client data platforms. Responsibilities include managing metadata repositories and business glossaries, profiling datasets, measuring data quality, enforcing data standards and classification policies, triaging incidents, defining critical data elements, supporting platform migration, automating governance workflows, reporting governance metrics, and training data stewards. The role bridges technical and business teams while promoting consistent data definitions, privacy, security, and stewardship.

Drive product and performance analytics for mobile app user acquisition: define core metrics, analyze campaign and product trends, build dashboards and data pipelines, support experiments and ML teams, automate reporting (including AI-powered workflows), and communicate findings and recommendations to stakeholders.

Updates machine learning training and test datasets, refines annotation guidelines, initiates model training, and evaluates model performance. The role annotates synthetic text and image data, conducts manual quality analysis, identifies error patterns, reports anomalies, and produces findings on utterance quality, LLM evaluation, ASR bugs, and customer pain points. It also applies basic NLP and linguistic knowledge to ensure data accuracy and consistency.

Audit and improve human and model-generated data outputs for accuracy, consistency, and policy compliance. Provide actionable feedback, clarify evolving guidelines, support annotators, coach new team members, monitor workflows and blockers, identify recurring issues and edge cases, participate in calibration discussions, and document findings to improve team performance and quality at scale.

Develop and scale customer-facing analytics, dashboards, reports, and data products for a healthcare AI platform. Partner with Product, Engineering, Sales, BizOps, customers, and other stakeholders to translate needs into data requirements and actionable insights. Lead reporting initiatives, resolve customer reporting issues, support high-stakes customer interactions, improve internal workflows, and help productize operational and clinical data into differentiated customer value.

Build and maintain the product analytics data foundation: design scalable dbt models, define semantic models and governed metrics, partner with Engineering on instrumentation, certify datasets for BI tools, monitor pipelines and data quality, and use AI to accelerate analytics engineering workflows.

Partner with founders and cross-functional teams to analyze financial and operational data, build executive dashboards, create P&L and margin analyses, forecast and model unit economics, identify cost leaks, reconcile transactions, and present recommendations to improve profitability, cash flow, and business performance.

Partner with Marketing, Sales, Finance, Product and other teams to turn business questions into actionable analysis. Own end-to-end analytics: build datasets and dashboards (dbt, LookML), maintain metric definitions and caveats, use and curate agent tooling for trustworthy answers, and contribute small data platform and pipeline improvements.

Owner of end-to-end data analysis and delivery for product and client solutions. Design queries, datasets, reports and dashboards, protect recurring data deliverables, partner with Product/Sales/Technology, mentor junior analysts, and apply automation/AI to improve workflows and inform product strategy.

The Forward-Deployed Data Analyst will engage with African Ministries of Health, build data management solutions, develop dashboards, and support public health decision-making.