Lead Data Analyst, Growth & Experimentation
New
J
JobgetherGrowth analytics
Fully remote position, open to candidates in Brazil., collaborating with teams and stakeholders operating on US-facing schedulesFull-TimeLead
Salary75,000 - 100,000 USD per year
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Job Details
- Experience
- Significant experience in data analytics, experimentation, growth analytics, or a closely related field
- Required Skills
- PythonSQLdbt
Requirements
- Significant experience in data analytics, experimentation, growth analytics, or a closely related field.
- Strong background designing and evaluating controlled experiments.
- Advanced knowledge of hypothesis testing, statistical power, sample-size calculations, significance, novelty effects, and interaction effects.
- Understanding of anytime-valid statistics or sequential testing methodologies.
- Expert-level SQL skills and practical Python proficiency.
- Experience with dbt and Lightdash, including analytical models and self-service metrics.
- Familiarity with Segment events and Flagsmith randomization, and experiment instrumentation.
- Strong understanding of acquisition and conversion funnels, CAC, LTV, conversion rates, channel economics, attribution, seasonality, and channel mix.
- Experience analyzing paid, organic, partner, SEO, and lifecycle marketing initiatives.
- Daily use of AI tools for SQL, dbt, analytical validation, and workflow automation; experience extending AI-powered or agent-based workflows is valuable.
- Ability to work independently with imperfect or evolving data and automate processes without relying on dedicated data engineering support for every task.
- Strong communication and stakeholder-management skills, including explaining complex analysis and influencing decisions without direct authority.
Responsibilities
- Own the Growth experimentation program and establish standards for test design, statistical power, sample sizes, significance, and experiment readouts.
- Identify underpowered tests, false positives, tracking issues, and attribution problems before they affect business decisions.
- Extend anytime-valid statistical monitoring across Growth experiments.
- Develop the self-service experimentation layer, including automated Growth metrics in Lightdash, Python statistical tooling, and AI-powered workflows.
- Build and maintain an acquisition-to-conversion funnel model covering visitors, leads, and customers across brands and channels.
- Improve funnel instrumentation and data consistency across paid, organic, and partner acquisition channels.
- Partner with the Director of CRO, performance marketing teams, and Growth Product Managers to turn analysis into recommendations.
- Establish processes and a playbook to scale the Growth analytics function.
- Evaluate external statistical tools against further development of existing Python and AI-powered capabilities.
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