Top Data Analyst Jobs in Vancouver

Analyze Remitly fraud data to detect and mitigate fraud. Build models, dashboards, and risk metrics; normalize data from multiple systems; run A/B tests; partner with Product, Engineering, Compliance, and Business to improve KYC/KYB and fraud workflows; provide actionable insights to reduce false positives and operational risk.

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

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.

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.

The Senior Marketing Data Analyst will analyze customer behavior, optimize marketing spend, and improve performance, acting as the analytical engine of the marketing team.

Own GTM data accuracy, hygiene, governance, and integrity across Salesforce and related systems. Build data models, pipelines, Power BI or Tableau dashboards, and automated reporting for Sales and executive stakeholders. Analyze pipeline, forecasting, conversion, win/loss, productivity, and eventually Customer Success metrics. Document KPI definitions, train users, resolve workflow bottlenecks, and recommend improvements to data architecture and processes.

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.

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.

Support end-to-end Power BI report development: gather requirements, design data models and ETL, build dashboards, automate workflows with Power Automate, manage data sources (SharePoint, SQL, APIs), resolve data quality issues, document solutions, and collaborate with stakeholders to improve reporting and processes.

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.

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.

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.

Analyze large game and player datasets to extract insights for retention and monetization. Build dashboards, run A/B tests, measure feature performance, support cross-functional teams, and produce ad-hoc reports to inform product and business decisions.

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.

Support D2C and B2B growth by building automated dashboards, performing funnel and conversion analyses, tracking lifecycle marketing and ad performance, and partnering with cross-functional teams to scale growth initiatives. Learn from mentors and progress into owning growth and marketing analytics domains.

Own provider data operations for national provider organizations, including roster reconciliation, data-quality validation, issue resolution, provider coordination, and claims-related configuration accuracy. Analyze business processes, requirements, risks, and implementation barriers; lead user acceptance testing and process improvements. Manage provider records, meetings, presentations, decision logs, corrective submissions, and cross-functional execution while serving as a subject matter expert for provider data operations.

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.

The Senior Data Quality Analyst will analyze and improve data quality across systems, collaborate on data governance, and ensure accurate data processes, contributing to actionable recommendations and stakeholder communication.