Top Data Analyst Jobs in Vancouver

Maintains and validates investment data across systems, manages scheduled data processes, delivers data quality signoffs, investigates exceptions, supports portfolio management requests, and improves workflows to increase efficiency, reduce risk, and strengthen data quality. Collaborates with investment teams, operational staff, engineers, and stakeholders on data management activities and projects.

Analyze and improve enterprise data across Salesforce, Greenhouse, Dayforce, and related platforms. Responsibilities include data profiling, modeling, integration support, REST API testing, reconciliation, validation, quality analysis, documentation, and Agile release testing. The role partners with business, technology, engineering, and application teams to define requirements, transformation rules, mappings, and scalable data solutions that support reporting, analytics, and enterprise decision-making.

Analyzes complex datasets, maintains and improves databases, develops KPI reports and dashboards, conducts benchmarking and statistical research, and provides insights to CDL leadership. The role manages data flows across Salesforce, Airtable, and Google Drive, builds automations and visualization tools, improves data quality and processes, documents data systems, and trains staff on data management practices.

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.

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.

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.

Owns advanced reporting, dashboards, data analysis, KPI development, ETL requirements, and data quality initiatives for internal stakeholders. The role delivers recurring and ad hoc reports, builds automated workflows, documents data definitions and processes, communicates insights to senior leaders, and supports BI sprint planning. Strong SQL, data visualization, stakeholder communication, and systematic data quality problem-solving are required.

Maintain data quality and electronic client records by indexing and re-indexing documents in ECF, validating financial statement data, processing service requests and small loan file reviews, preparing client correspondence, monitoring outstanding items, and meeting service-level timelines while collaborating with business partners to support credit operations.

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.

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.

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.

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.

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.

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.

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