Food-service distributor scaling logistics and sales operations
MegaG Alimentos is a Brazilian food-service wholesaler with 1,000+ employees and a distribution operation in São Paulo, serving pizzerias, restaurants, hotels, and industrial kitchens across a 2,000+ SKU portfolio. Hiring velocity is accelerating with 22 roles posted in the last 30 days — 13 in logistics alone — while simultaneously building internal tools (sales dashboards, margin calculators, route optimization) to address stated pain points in sales efficiency and operational bottlenecks. The tech stack reveals a heavy focus on business intelligence (Power BI, Tableau, Qlik, Looker) paired with ERP and WMS infrastructure, suggesting active investment in visibility and process control.
MegaG Alimentos operates a food-service distribution business founded in 2005, based in Vargem Grande Paulista, São Paulo. The company supplies branded and private-label products to pizzerias, hamburger chains, steakhouses, restaurants, hotels, and industrial kitchens across Brazil. Operations center on a modern distribution facility with informatized systems and a trained workforce. The product mix spans over 2,000 items from established food brands plus proprietary lines. Stated core values are innovation, transparency, commitment, and excellence.
MegaG uses Active Directory, Oracle Database, ERP, WMS, and TMS for operational infrastructure. Analytics tools include Power BI, Tableau, QlikView, and Looker Studio. Sales and process support run on AppSheet, with Python and PHP for custom applications.
Active initiatives include sales dashboards, route optimization, fleet sizing, margin calculator tools, preventive maintenance, and a sales support application — all aimed at improving sales efficiency and operational visibility.
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MegaG Alimentos's technology stack, projects, and hiring signals are inferred from public hiring and company data — career pages, public listings, and company web presence — then clustered and de-duplicated. Figures are estimates that refresh over time. Read our full methodology →
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