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Manage day-to-day zone operations: monitor dashboards and communications, onboard and support riders/vendors/agents, handle order exceptions and escalations, coordinate resources, perform data checks and system follow-ups, and support improvement initiatives with zonal leadership.

Manage day-to-day zone operations: monitor dashboards, respond to requests, onboard and train riders/vendors/agents, handle order exceptions and escalations, coordinate resources, and support implementation of operational improvements under zonal leadership. Fast-paced, shift-based role.

Manage day-to-day zone operations: respond to requests, monitor dashboards, onboard and support riders/vendors/agents, resolve order exceptions and escalations, coordinate resources, and assist the Assistant Zonal Manager with improvements.

Manage day-to-day zone operations: respond to requests, monitor dashboards, onboard and support riders/vendors/agents, resolve order exceptions and escalations, coordinate resources, and assist Assistant Zonal Manager with improvements.

Lead design and implementation of scalable data pipelines, warehouses, and data models. Ensure data quality, observability, governance, and self-service analytics. Partner with cross-functional teams, support ML/forecasting initiatives, mentor engineers, and optimize infrastructure for performance, scalability, and cost.

Lead architecture and technical direction across multiple engineering teams. Design and build scalable backend systems and infrastructure, improve reliability, performance, observability, and developer workflows. Drive cross-functional initiatives across logistics, fulfillment, payments, and platform infrastructure. Mentor engineers, set engineering standards, identify technical risks, and collaborate with Product and Leadership on long-term technical strategy.

Design, build, and maintain APIs and backend services for customer, vendor, and rider apps. Improve performance, debug production issues, collaborate with product and frontend teams, and address technical debt by proposing standards, tooling, and process improvements.

Design, build, deploy, and maintain production machine learning models and pipelines for forecasting, ETA prediction, recommendations, fraud detection, and operational optimization. Collaborate with Data, Product, and Engineering teams to operationalize, monitor, and improve ML systems and tooling.

Design, build, and maintain scalable data pipelines and warehouses to support analytics, reporting, forecasting, and ML workflows. Improve data quality, reliability, observability, performance, and governance while partnering with Product, Operations, Finance, and Engineering to meet business needs.

Lead design, development, and deployment of ML models for forecasting, optimization, personalization, and risk detection. Build data pipelines, experimentation frameworks, pricing and incentive algorithms, and metrics tracking. Partner with Product, Engineering, and Operations, maintain data quality, mentor junior scientists, and translate insights for non-technical stakeholders.