Top Data & Analytics Jobs

Own wholesale performance reporting and analysis across retail partners, products, and regions. Build datasets, data models, dashboards, and automated reporting while validating data quality. Analyze sell-through, inventory, pricing, promotions, and commercial trends to identify growth opportunities. Partner with stakeholders across Wholesale, Digital Commerce, and Marketing to frame analytical questions, communicate findings, and provide actionable recommendations.

Leads WHOOP’s data governance function, defining strategy, operating models, ownership, data contracts, event taxonomy, cataloging, access policies, quality standards, and privacy compliance. Builds and coaches a senior team while partnering across engineering, analytics, product, legal, GRC, and InfoSec. Establishes governance frameworks, automated quality monitoring, access controls, compliance practices, and metrics, while embedding standards into technical workflows and advancing AI-powered governance.

Conduct healthcare product analysis, build dashboards and recurring reports, monitor KPIs, support A/B testing, validate data, and identify trends affecting member engagement and health outcomes. Partner with product, engineering, design, data science, and marketing teams to deliver actionable insights, improve data quality, and support healthcare product strategy. The role also contributes to documentation, segmentation analysis, and repeatable analytics processes.

Own commercial analytics for direct-to-consumer and marketplace channels, analyzing pricing, promotions, conversion funnels, merchandising, product mix, customer behavior, and channel economics. Build scalable reporting tools, support forecasting and planning, and translate transactional and behavioral data into recommendations for digital commerce leadership. Partner with Marketing Analytics and Analytics Engineering to connect acquisition trends with commercial outcomes and ensure trusted data.

Leads commercial analytics across digital commerce, apparel and accessories, and wholesale. Manages analysts, owns bookings forecasting, develops customer and channel economics frameworks, and builds scenario-planning and measurement models. Partners with Finance and senior stakeholders to evaluate growth opportunities, diagnose performance, quantify tradeoffs, and guide investment decisions. Requires strong SQL, analytical modeling, people leadership, and cross-functional business judgment.

Own commercial analytics for WHOOP’s Wholesale business, analyzing partner, product, pricing, promotion, inventory, and geographic performance. Build scalable reporting tools, support forecasting and market sizing, identify growth opportunities, and translate transactional and retailer data into recommendations. Partner with Wholesale, Sales, Marketing, and Analytics Engineering teams to improve channel performance, data quality, and go-to-market strategy.

The Senior Business Analyst partners with Platform Engineering to measure the business impact of infrastructure, identity, authentication, and security initiatives. Responsibilities include analyzing conversion, engagement, retention, and purchase funnels; designing experiments; assessing security and member-experience tradeoffs; analyzing gross margin, COGS, and cloud hosting costs; building dashboards and reports; and presenting actionable insights to stakeholders and senior leadership.

Lead operational analytics for WHOOP’s global operations, identifying inefficiencies across shipping, fulfillment, inventory, costs, and service levels. Own metrics, forecasting, spend tracking, variance analysis, and cost-reduction initiatives while partnering with Operations, Finance, Membership Services, vendors, and Data Engineering. Build trusted data assets, pipelines, semantic-layer definitions, dashboards, and performance reviews. Manage and mentor analysts while influencing strategic prioritization and resource allocation through data-driven insights.

Design, build, and operate WHOOP’s data platform infrastructure, including lakehouse, warehouse, and streaming systems. Improve performance, reliability, cost efficiency, observability, and developer experience across AWS-based data systems. Develop internal tools, APIs, automation, CI/CD, and infrastructure-as-code practices. Partner with data, ML, analytics, and software engineering teams to support scalable workflows, governance, and production excellence while using AI-assisted development tools effectively.