Social Media&Marketing Specialist
Pubblicato il 13-08-2026 - Internet / Comunicazione online / E-commerce in Milano
We are looking for a Digital Analytics Specialist to join our Marketing team in Milan and play a key strategic role in data interpretation and advanced analytics.
This role will work at the intersection of Marketing, Tech and UX, with the mission to ensure data accuracy, unlock actionable insights, and support data-driven decision making across channels, brand partners.
As a key analytical partner, you will manage tracking systems, improve the quality of data flows, and collaborate on advanced analysis, modeling, and visualization to optimize marketing performance and strategic outcomes. You will also translate on-site user behavior data into concrete hypotheses on user experience, partnering with the UX/Product team to prioritize improvements to our digital touchpoints.
Ensure the quality, consistency, and reliability of all collected data, and guarantee they're correctly implemented and visualized across platforms (GA4, dashboards, BI tools) and ready for reporting needs.
Configure and maintain custom GA4 reports to analyze website performance and user interactions (e.scrolls,
videos, wish lists, product rankings) and extract strategic insights.
Analyze on-site behavioral data — funnel drop-offs, rage clicks, scroll depth, session recordings and heatmaps to identify friction points across the user journey, and translate findings into clear, actionable UI/UX recommendations for the Product/UX team.
Build and maintain customer journey maps across key touchpoints, highlighting drop-off and conversion points to help prioritize experimentation and optimization efforts.
Partner with Brand Marketing and Channels teams to set up advanced analysis frameworks and test alternative attribution models.
Collaborate with Tech team to develop and manage customized dashboards, integrating GA4 events and performance metrics, to deliver deep insights into user journeys and conversion funnels.
Research, propose, and contribute to the implementation of predictive models, including cus