Lead Data Analyst, Growth & Experimentation

Accountabilities: Own the Growth experimentation program, establishing rigorous standards for test design, statistical power, sample sizes, significance, and experiment readouts. Identify underpowered tests, false positives, tracking issues, and attribution problems before they lead to incorrect business decisions. Extend the use of anytime-valid statistical monitoring across Growth experiments, ensuring early decisions are based on reliable statistical boundaries rather than preliminary trends. Develop and improve the self-service experimentation layer, including automated Growth metrics in Lightdash, Python-based statistical significance tooling, and existing AI-powered workflows for pre-test power calculations and live-test health checks. Build and maintain a trusted acquisition-to-conversion funnel model covering visitors, leads, and customers across brands and channels, with clear CAC, LTV, and conversion-rate metrics. Improve funnel instrumentation and data consistency across paid, organic, and partner acquisition channels, addressing attribution complexity, seasonality, and channel mix. Partner directly with the Director of CRO, performance marketing teams, and Growth Product Managers to translate analytical findings into recommendations and measurable business decisions. Challenge assumptions and communicate statistical uncertainty clearly, helping stakeholders adjust priorities when the evidence supports a different direction. Establish the standards, processes, and playbook needed to scale the Growth analytics function beyond a single analyst. Provide a buy-versus-build recommendation for the experimentation technology stack, evaluating external statistical tools against further development of existing Python and AI-powered capabilities. Drive measurable improvements in conversion and experimentation efficiency, reducing test re-runs caused by tracking or attribution issues and enabling routine experiments to become self-service. Requirements: Significant experience in data analytics, experimentation, growth analytics, or a closely related field, with a strong background in designing and evaluating controlled experiments. Advanced statistical knowledge, including hypothesis testing, statistical power, sample-size calculations, significance, novelty effects, interaction effects, and the appropriate use of experimentation. Strong understanding of anytime-valid statistics or sequential testing methodologies, with the ability to apply rigorous monitoring standards to live experiments. Expert-level SQL skills and practical Python proficiency, particularly for automating statistical calculations, analytics workflows, and experiment reporting. Experience working with dbt and Lightdash, including building and maintaining analytical models and self-service metrics. Familiarity with experiment instrumentation, including Segment events and Flagsmith randomization, and the ability to identify tracking and attribution issues before experiments launch. Strong understanding of acquisition and conversion funnels, including CAC, LTV, conversion rates, channel economics, attribution challenges, seasonality, and channel mix. Experience analyzing performance across multiple acquisition channels, including paid, organic, partner, SEO, and lifecycle marketing initiatives. An AI-native approach to analytics, with daily use of AI tools to support SQL, dbt, analytical validation, and workflow automation. Experience extending AI-powered routines or agent-based workflows is particularly valuable. Ability to work independently with imperfect or evolving data, build reliable models, and automate processes without relying on a dedicated data engineering team for every task. Strong communication and stakeholder-management skills, with the ability to turn complex analysis into clear recommendations and influence decisions without direct authority. A proactive, consultative mindset: comfortable identifying opportunities, challenging assumptions, and shaping analytical priorities rather than simply responding to requests. Comfortable working as an individual contributor in a remote environment, collaborating with teams and stakeholders operating on US-facing schedules. Benefits: Annual base salary of $75,000–$100,000 USD . Fully remote position, open to candidates in Brazil. Flexible paid time off, with performance and outcomes prioritized over hours logged. Access to existing AI-powered experimentation tools and workflows, with the opportunity to extend their capabilities. High-impact individual contributor role with ownership of Growth experimentation and analytics practices. Opportunity to work directly with Growth leadership, performance marketing, and product stakeholders.