Suggested rewrite: Led a cross-functional initiative that improved [business outcome] by [measurable result], demonstrating experience relevant to this role...
Senior Full-Stack Growth Engineer – Marketing & Analytics
- Remote from
- Brazil
- Salary
- Undisclosed
- Department
- Software Engineering
- Employment
- Full Time
- Experience
- Senior
- Published
- Apply before
- 8 Dec 2026
- Listing views
- 19
- Application actions
- 1
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About this role.
About Truelogic
At Truelogic we are a leading provider of nearshore staff augmentation services headquartered in New York. For over two decades, we’ve been delivering top-tier technology solutions to companies of all sizes, from innovative startups to industry leaders, helping them achieve their digital transformation goals.
Our team of 600+ highly skilled tech professionals, based in Latin America, drives digital disruption by partnering with U.S. companies on their most impactful projects. Whether collaborating with Fortune 500 giants or scaling startups, we deliver results that make a difference.
By applying for this position, you’re taking the first step in joining a dynamic team that values your expertise and aspirations. We aim to align your skills with opportunities that foster exceptional career growth and success while contributing to transformative projects that shape the future.
Our Client
A premier digital product and technology consulting firm specializing in modern software engineering, cloud-native architecture, and strategic digital transformation across high-impact sectors including healthcare, financial services, and education.
Job Summary
The Senior Full-Stack Growth Engineer plays a pivotal role in designing, building, and evolving the technical infrastructure that powers customer acquisition, experimentation, and marketing performance. This unique position bridges end-to-end full-stack software development with rigorous quantitative analysis to drive measurable business impact. The ideal candidate will architect production features and internal tools, build resilient API and CRM integrations, instrument deep product analytics, and design statistically sound experiments across the entire user lifecycle. Collaborating closely with cross-functional product, design, and growth teams, this role turns critical business hypotheses into high-impact, shippable solutions supported by clean data and causal measurement.
Responsibilities
Architect, build, and maintain production-ready full-stack features, backend services, web applications, and internal growth tools.
Design, deploy, and evaluate experiments across customer acquisition, onboarding flows, conversion funnels, and retention loops.
Apply statistical inference and causal reasoning to evaluate A/B test results, identify confounding factors, and communicate uncertainty clearly to stakeholders.
Implement robust event tracking, instrumentation, and product analytics using modern platforms (e.g., PostHog).
Integrate advertising platforms, CRMs, databases, and third-party tools via REST/GraphQL APIs, webhooks, and event-driven data pipelines.
Evolve database schemas and analytical data models to support new product workflows, attribution, and reliable measurement.
Connect acquisition, product usage, CRM, and revenue data streams to analyze full-funnel customer journeys and business outcomes.
Maintain high standards for data quality, automated testing, observability, user privacy, and data security across all integrations.
Collaborate with marketing, product, and design partners to translate strategic growth objectives into testable engineering roadmaps.
Leverage modern AI-assisted engineering and analytical workflows responsibly, ensuring rigorous validation and data governance.
Qualifications and Job Requirements
Proven track record as a Full-Stack Software Engineer building modern web applications, scalable backend APIs, and working with relational databases.
Advanced SQL proficiency alongside hands-on scripting/analytical experience in Python, R, or equivalent modern data tools.
Practical foundation in experimental design, A/B testing methodologies, statistical inference, and causal reasoning.
Hands-on experience implementing product analytics instrumentation, event pipelines, and auditing data quality.
Solid experience architecting third-party integrations using webhooks, APIs, and event-driven patterns.
Strong ability to translate complex data findings and technical tradeoffs into actionable business and product insights.
Practical experience with AI-assisted software development and analytical tooling, backed by sound engineering judgment regarding validation and security.
Preferred / Nice to Have:
Familiarity with PostHog, CRM platforms, marketing automation engines, Conversion APIs (CAPI), or server-side tracking.
Familiarity with growth metrics such as CAC, LTV, churn/retention curves, and ROAS.
Understanding of incrementality testing, conversion lift studies, and attribution model nuances.
Background in consulting, client-facing delivery, or building software in regulated environments (e.g., healthcare, fintech).
What We Offer
100% Remote Work: Enjoy the freedom to work from the location that helps you thrive. All it takes is a laptop and a reliable internet connection.
Highly Competitive USD Pay: Earn an excellent, market-leading compensation in USD, that goes beyond typical market offerings.
Paid Time Off: We value your well-being. Our paid time off policies ensure you have the chance to unwind and recharge when needed.
Work with Autonomy: Enjoy the freedom to manage your time as long as the work gets done. Focus on results, not the clock.
Work with Top American Companies: Grow your expertise working on innovative, high-impact projects with Industry-Leading U.S. Companies.
Why You’ll Like Working Here
A Culture That Values You: We prioritize well-being and work-life balance, offering engagement activities and fostering dynamic teams to ensure you thrive both personally and professionally.
Diverse, Global Network: Connect with over 600 professionals in 25+ countries, expand your network, and collaborate with a multicultural team from Latin America.
Team Up with Skilled Professionals: Join forces with senior talent. All of our team members are seasoned experts, ensuring you’re working with the best in your field.
Apply now!
Annual salary information is not provided for this position. Explore salary ranges for similar roles in our Salary Directory ›
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Sample interview questions
I would define the primary success metric and guardrail metrics, establish the eligible population and randomization unit, calculate a target sample size, and document decision criteria before launch. I would validate event instrumentation, monitor sample-ratio mismatch and data quality during the test, then report effect sizes and confidence intervals alongside practical business impact.
I would define consistent identifiers and event schemas, ingest source data through authenticated APIs or webhooks, and store normalized events in an analytical model. I would add deduplication, retry handling, schema validation, lineage documentation, and reconciliation checks so acquisition, conversion, and revenue metrics remain trustworthy.
I first look for possible confounders, selection bias, seasonality, and changes in tracking or eligibility. When possible, I use randomized experiments; otherwise, I apply suitable quasi-experimental approaches and communicate assumptions and uncertainty rather than presenting observational relationships as causal facts.
I would maintain an event taxonomy, version event schemas, test critical events automatically, and monitor event volumes and property completeness. I would also assign ownership, document metric definitions, audit tracking after releases, and alert on material deviations from expected data patterns.
I would clarify the decision the team needs to make and propose a minimal instrumentation plan before release, including a primary metric and essential guardrails. If speed is critical, I would ship behind a feature flag, document limitations, and schedule follow-up work to strengthen measurement rather than relying on unreliable results.
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