Senior Marketing Analytics Engineer
Wellhub
| Company | Wellhub |
| Category | Engineering |
| Location | Brazil (Remote) |
| Remote | Remote |
| Employment | Not stated |
| Level | Senior |
| Salary | Not stated by the employer |
| Posted | 1 Jul 2026 |
| Last verified | 3 Aug 2026 |
| Source | Employer ATS (greenhouse) |
Description
Your wellbeing, our mission. Join a company shaping a healthier world.
GET TO KNOW US
At Wellhub we're revolutionizing workplace wellness. Our platform connects employees worldwide to the best partners for fitness, mindfulness, therapy, nutrition, and sleep—all in one simple subscription. Headquartered in NYC with team members in Europe, North America and South America, we’re on a mission to make every company a wellness company.
We believe work should be fulfilling, inspiring, and balanced. Here, you’ll find a team that values wellbeing, collaboration, and different perspectives, where passion and creativity push boundaries to create real impact. Your contributions will help shape a healthier, more balanced world for you and millions of people globally.
Join us in redefining the future of wellbeing!
THE OPPORTUNITY
We are hiring a Senior Marketing Analytics Engineer to our Marketing team in Brazil!
The Marketing Analytics Engineer (AI) is an embedded analyst position, a hybrid role combining data engineering, marketing data analysis, and applied AI skills. This individual supports the Marketing Analytics team by building and maintaining the data infrastructure that enables business teams to analyze campaigns and overall marketing performance, while also expanding that infrastructure to power AI-driven analytics tools.
Responsibilities span data modeling, pipeline management, data architecture, predictive analysis, and the design of machine-readable business context that makes AI agents accurate and trustworthy. This role collaborates closely with teams across the company (including Global Analytics, Product Development, Martech, and Sales Operations) to ensure data-driven insights effectively drive business growth and strategic initiatives.
YOUR IMPACT
Data Engineering & Architecture — Own the foundational marketing data layer. You will design, build, and maintain the data pipelines that transform raw marketing sources into optimized dimensional models (utilizing Medallion architecture), delivering clean, report-ready tables for downstream business intelligence and analytics;
Predictive Analysis — Develop and apply statistical and predictive models to forecast key marketing funnel metrics, identify areas for improvement, and inform strategic decision-making;
Marketing Attribution — Support the design and implementation of marketing attribution models to accurately measure channel and campaign contribution;
Data Quality & Governance — Ensure data reliability through monitoring, validation, cleansing, and adherence to data governance policies;
Documentation & Versioning — Maintain thorough documentation of the marketing data ecosystem and version data projects in GitHub;
Semantic Layer for AI — Maintain and extend BI data models (LookML and/or dbt preferred) enriched with metadata, business context, and structured definitions that make them consumable by AI agents and natural language analytics interfaces;
Reusable AI Skills & Workflows — Build reusable AI skills, tools, and workflows that allow analysts and stakeholders to interact with governed data through natural language interfaces;
AI Quality Assurance — Run structured test cycles to validate AI agent output accuracy, identify regressions, and maintain quality standards as models and data evolve;
Data Standards for Responsible AI — Contribute to metadata standards and responsible AI guidelines that govern how marketing data is surfaced through automated and AI-assisted analytics.
WHO YOU ARE
3–5 years in analytics engineering, data engineering, business intelligence, or a closely adjacent technical role;
Experience working in cross-functional settings with both technical and non-technical stakeholders;
Fluent English;
Proficiency in Python and SQL for production-grade data work;
Experience with data warehousing con