Top Analytics Jobs in Vancouver

Own Jetson’s marketing data lake end to end, including source integrations, dbt medallion models, data quality, attribution, experimentation infrastructure, LTV and ROI models, Metabase dashboards, and analytics for marketing and growth teams. The role combines hands-on data engineering with measurement and strategic analysis across paid, organic, direct mail, CTV, partnerships, and offline channels.

Manage and grow strategic partnerships across US & Canada for a Google Cloud-focused AI and data consultancy. Drive co-sell initiatives, GTM planning, partner enablement, joint marketing, event execution, partner onboarding, and pipeline generation while tracking partner performance and reporting impact.

Design and build a unified analytics instrumentation platform using Go, Kubernetes, ClickHouse, and NATS. Improve data emission, transit, and quality for usage and billing, provide incident response and root cause analysis, collaborate with product and data teams to drive adoption, make tradeoffs for reliability and performance, and mentor engineers through reviews and documentation.

Build scalable GCP-based data pipelines and datasets for insurance fraud analytics. Develop and evaluate machine learning, NLP, and Generative AI models using claims and unstructured SIU data. Apply explainable AI techniques, conduct exploratory analysis, automate data workflows, document architectures, and support privacy, fairness, and compliance requirements while collaborating with investigators and business analysts.

Lead design and implementation of full‑stack authentication and account security features for Stripe Organizations. Build APIs, frontend UX/UI, and analytics to secure and simplify login, SSO, MFA, RBAC, and account workflows. Mentor engineers, drive technical decisions, collaborate with Security and partner teams, and create reusable auth building blocks used across Stripe products.

Lead change strategy and stakeholder engagement for technology and AI-enabled transformations. Design and execute adoption programs, define adoption metrics, monitor readiness and risks, and apply human-centered change frameworks. Support AI adoption, workforce impacts, and delivery excellence while partnering with clients, analytics, and product teams to link behavior change to business outcomes.

Own the vision, roadmap, and delivery of AI and analytics capabilities for an enterprise EHSQ SaaS platform. Lead customer and market discovery, prioritize backlogs, define product metrics, and partner with engineering, data science, architecture, design, sales, marketing, and services teams. Drive AI feature launches, adoption measurement, governance, enablement, pricing, and executive communications while ensuring trustworthy, explainable, and compliant product experiences.

The Analytics Manager will lead analytics for the Payments and Identity lifecycle, manage a team of analysts, develop KPIs, and ensure data quality while collaborating with cross-functional teams.

Senior Customer Analytics Manager leads strategic analytics across the customer lifecycle—acquisition, onboarding, engagement, retention, monetization, and expansion. Partners with cross-functional teams to translate business questions into rigorous analyses, experimentation, forecasting, and predictive models; defines KPIs; builds dashboards and decision tools; mentors analysts; and drives scalable, AI-enabled analytics that inform executive decisions and improve customer value.

Leads MAU analytics and data science, managing decision scientists and data scientists while setting quality standards. Owns MAU driver modeling, forecasting, causal analysis, experimentation, and exploratory work supporting growth strategy. Establishes consistent analytical practices, partners with revenue, product, engineering, and data engineering leaders, and embeds the team in roadmap planning. Promotes AI-native workflows through coding agents, automation, and analytical tools while coaching team members into strategic advisors.

Deploys, integrates, and scales enterprise generative AI solutions in customer cloud environments. Responsibilities include architecting GCP and Vertex AI infrastructure, deploying and optimizing LLMs, building RAG data and vector search pipelines, managing GPU/TPU workloads on GKE, automating infrastructure with Terraform, and advising clients on AI safety, prompt engineering, hallucinations, and inference costs. The role also collaborates with research and platform teams and requires travel to client sites.

Manage and grow client relationships for multiple marketing accounts, coordinate campaign execution across paid media, SEO, content and email, report on KPIs, run client check-ins and strategy calls, identify upsell opportunities, and collaborate with internal teams to ensure retention and performance.

Senior SDET who contributes across frontend and backend systems by building and maintaining features with a strong testing mindset. Develop and maintain automated tests, improve test coverage and CI/CD, investigate bugs, support regulatory and compliance testing strategies, and balance automation with exploratory and risk-based testing.

The Customer Success Manager will educate clients on creative strategy, build relationships, and help maximize their use of Motion's platform through engagement and tailored support.

Leads the enterprise workforce planning function, building forecasting, headcount, capacity, and scenario models. Partners with Finance, HR, Talent Acquisition, and business leaders on workforce costs, budgets, recruitment strategy, and planning governance. Owns workforce planning data infrastructure, pipelines, dashboards, and reporting, while redesigning manual processes into scalable systems. Influences senior stakeholders, delivers executive-ready analysis, and translates workforce data into strategic recommendations.

Design and build end-to-end data solutions and pipelines using modern cloud platforms; ingest, transform, and make data available for BI and downstream consumers. Partner with business stakeholders, implement CI/CD, testing, observability, and documentation, triage performance issues, and support delivery across client engagements.

The GTM Analytics Lead will drive data-driven decisions across marketing, sales, and finance, optimizing performance and ensuring sustainable revenue growth through analysis and real-time insights.

Lead end-to-end process mapping and analysis using BPMN and process/task-mining tools to identify inefficiencies, create As-Is/To-Be documentation, define BRDs, model future-state scenarios, and drive cross-functional implementation with executive reporting.

As a Senior Full Stack Software Engineer, you will own product features, design scalable systems, mentor teammates, and ensure coding excellence while collaborating with cross-functional teams.

Lead people-analytics strategy for Talent Acquisition and Talent Development. Drive end-to-end analysis of complex workforce problems, deliver evidence-based recommendations to senior leaders, ensure data governance and quality, coach junior analysts, build metrics frameworks, and conduct benchmarking and external research to inform workforce strategy.

Design and maintain reusable data models and transformation workflows, establish semantic layers and data governance, improve data quality and lineage, partner with analytics/data science/business teams, and mentor junior engineers to enable trusted, scalable analytics and reporting across the organization.

The Account Executive will develop and implement sales strategies, manage relationships with vendors, and drive growth through effective prospecting and client engagement.

As a Python and Kubernetes Software Engineer, you will develop data analytics solutions using open source tools, collaborating with a distributed team.

As an Engineering Manager, you'll lead a remote team, manage MLOps projects, ensure high performance, and mentor engineers while driving product strategies.

Leads 1Password’s analytics engineering function, owning data architecture, roadmap, modeling, semantic layers, BI tooling, pipelines, governance, testing, documentation, and data quality. Manages and develops analytics engineers, establishes technical standards, reviews complex dbt and SQL work, optimizes warehouse performance, and partners with senior leaders across Finance, Marketing, Product, Analytics, and Data Engineering. Builds trusted data foundations supporting company-wide decisions, AI initiatives, and generative BI.