E‑Commerce Lead – Chile, Peru & Uruguay

About the role

As an E‑Commerce Lead you will drive the digital commercial performance for the Chile, Peru and Uruguay markets. You will plan and execute daily product exposure on the website and mobile app, ensuring offers are relevant, visible and aligned with the commercial plan.

Key responsibilities

• Secure and confirm content from the Content team for .com and app according to the QCP and marketing calendar.
• Manage day‑to‑day product exposure, ranking, deals and tactical activities, making ad‑hoc adjustments to maximise performance.
• Lead manual execution tasks such as collection curation, product bag selection, ranking adjustments and category maintenance across CL, PE and UY.
• Use data‑driven insights to optimise navigation, exposure tactics, deals and ranking for continuous commercial improvement.
• Optimise internal search (add synonyms, keywords, landing pages) based on local customer behaviour.
• Set up product‑exposure strategies using Synerise and support Martech and AI‑personalisation initiatives.
• Coordinate operational implementation and validation of campaign pages, PLPs, URLs and navigation structures with internal stakeholders.
• Oversee weekly commercial execution, local adaptation and major campaign roll‑outs (Cyber, Black Friday, seasonal events).
• Support SEO, app, CRO and other digital projects from a business‑execution perspective and maintain planning documentation.
• Translate performance signals into actionable recommendations for ranking, exposure and campaign optimisation.

Required profile

• Proven experience in e‑commerce or digital merchandising, preferably managing multiple markets.
• Strong analytical mindset with the ability to turn data into actionable commercial insights.
• Excellent stakeholder management skills and ability to coordinate cross‑functional teams.
• Familiarity with seasonal commercial planning and campaign execution in LATAM.

Required skills

• Synerise
• SEO
• Martech platforms
• AI‑personalisation tools
• Data analysis