Senior Full-Stack Growth Engineer - Marketing & Analytics

New

TruelogicSoftware engineering

Location: BrazilFull-TimeSenior

Salary not disclosed

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Job Details

Have a proven track record as a full-stack software engineer building modern web applications, scalable backend APIs, and working with relational databases.
Demonstrate advanced SQL proficiency and hands-on analytical scripting experience in Python, R, or equivalent modern data tools.
Have a practical foundation in experimental design, A/B testing methodologies, statistical inference, and causal reasoning.
Have hands-on experience implementing product analytics instrumentation, event pipelines, and data-quality audits.
Have experience architecting third-party integrations using webhooks, APIs, and event-driven patterns.
Be able to translate data findings and technical tradeoffs into actionable business and product insights.
Have practical experience with AI-assisted software development and analytical tools, with sound judgment about validation and security.
Preferred: familiarity with PostHog, CRM platforms, marketing automation, Conversion APIs (CAPI), or server-side tracking.
Preferred: familiarity with CAC, LTV, churn and retention curves, and ROAS.
Preferred: understanding of incrementality testing, conversion lift studies, and attribution models.
Preferred: consulting, client-facing delivery, or software development in regulated environments such as healthcare or fintech.

Build and maintain production full-stack features, backend services, web applications, and internal growth tools.
Design, deploy, and evaluate experiments across acquisition, onboarding, conversion, and retention.
Apply statistical inference and causal reasoning to evaluate A/B tests and communicate uncertainty to stakeholders.
Implement event tracking, instrumentation, and product analytics using platforms such as PostHog.
Integrate advertising platforms, CRMs, databases, and third-party tools through APIs, webhooks, and event-driven pipelines.
Evolve database schemas and analytical data models for product workflows, attribution, and measurement.
Connect acquisition, product usage, CRM, and revenue data to analyze customer journeys and business outcomes.
Maintain data quality, automated testing, observability, user privacy, and data security across integrations.
Collaborate with marketing, product, and design partners to turn growth objectives into engineering roadmaps.
Use AI-assisted engineering and analytical workflows with validation and data governance.

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