Top Data Analyst Jobs in Vancouver

Build and maintain scalable AWS-based data pipelines, ETL solutions, datasets, integrations, and analytics processes. Analyze and cleanse complex data, improve quality and governance, validate reporting layers, and automate workflows. Collaborate with data scientists, modelers, developers, and business partners to translate requirements into reliable technical solutions supporting forecasting, pricing, planning, and advanced analytics.

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.

Supports enterprise data platforms, ETL processes, reporting environments, and data integration initiatives. Responsibilities include monitoring data loads, troubleshooting database and reporting issues, developing SQL queries and procedures, validating data, creating Power BI dashboards, supporting Snowflake and Microsoft data platforms, and contributing to data governance, migration, automation, and quality improvements.

The Data & Systems Analyst will improve data governance, quality, integrations, reporting, dashboards, and visualizations across school systems. Responsibilities include supporting Blackbaud, maintaining student and learning-management system connections, validating academic reporting data, documenting processes, improving workflows through automation, and exploring AI-enabled analysis. The role collaborates with academic, operational, and technology teams to strengthen privacy, security, system usability, and data-informed decision-making.

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.

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.

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.

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.

Owns Simbe’s product data management and Arena PLM administration for robotic hardware programs. Responsibilities include authoring and driving ECOs, MCOs, and deviations through approval; maintaining item masters, BOMs, approved vendor lists, part lifecycles, and documentation integrity; enforcing revision, numbering, and change-control standards; and coordinating implementation with Engineering, Operations, Supply Chain, suppliers, and contract manufacturers.

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.

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.

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.

Partner with Marketing as the embedded data science lead to build marketing measurement, attribution, LTV/ROAS/CAC models, run experiments, analyze the full funnel, create forecasts and dashboards, and influence channel strategy and budget allocation.

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.

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.

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

Analyze product usage, feature adoption, telemetry, and financial data to generate actionable insights. Own data modeling, dashboards, KPI development, and end-to-end analytics delivery while partnering with stakeholders on product initiatives. Mentor associate-level analysts and maintain strong execution metrics for analytics work. The role requires advanced SQL and Python, Databricks and PySpark expertise, visualization experience, and SaaS product analytics experience.