Senior Marketing Data Analyst

JoomPro is the first and only end-to-end cross-border B2B marketplace. It serves small, medium, and large businesses, handling orders of any size from start to finish. Today, JoomPro operates in Brazil and Mexico, with plans to expand into other markets. JoomPro is one of the key businesses of Joom Group, an international tech-centric group of e-commerce companies founded in 2016 in Latvia. Joom Group also includes other businesses: Joom, a global cross-border marketplace; JoomPulse, an AI analytics tool for Mercado Livre and Shopee sellers; and Onfy, a pharmaceutical marketplace in Germany. Joom Group has offices in Latvia, China, Portugal, Germany, and Brazil, with its headquarters in Lisbon, Portugal. We work as one international team, sharing knowledge and collaborating across countries, businesses and products.

We're now looking for an experienced Marketing Data Analyst to strengthen our analytics team and help turn data into insights that drive smarter decisions, improve channel performance, and optimize marketing investments.

About the team

Our analytics team helps the business make smarter, data-driven decisions. We focus on:

• Optimizing marketing budgets and channel mix to maximize efficiency;
• Building dashboards and analytical tools for decision-making, ROI forecasting, and strategic planning;
• Designing and analyzing experiments to measure channel performance and incrementality;
• Developing alerting systems to monitor spend, efficiency changes, and data quality;
• Supporting budget planning, marketing strategy, and market research.

Why join us:

• Real business impact: your work directly influences ROI and growth;
• Access to advanced AI tools (Cursor, ChatGPT, Gemini, Claude);
• Strong analyst community & cross-team knowledge sharing;
• Matrix structure: your manager is also an analyst;
• Transparent growth path & structured onboarding;
• Open culture with minimal bureaucracy.

Responsibilities:

• Evaluate the effectiveness of existing and new advertising channels (Google Ads, Meta Ads, etc);
• Set up and improve monitoring of statistical anomalies in metrics;
• Improve long-term payback prediction models;
• Generate hypotheses to improve acquisition/product feeds/ad creatives/channel split/targets, etc.;
• Conduct data studies to uncover insights;
• Write ETL pipelines (data marts, alerts, and predictions);
• Ask "why" and "what problem are we solving".

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