Data Engineer

Join ShipTech Analytics (STA) at Amazon as a Data Engineer and contribute to the core data and analytics infrastructure that powers our global transportation network. You will be instrumental in developing an intelligent, autonomous, and scalable performance management and decision support system for Amazon Transportation.

In this role, you will create standardized data solutions for operations teams worldwide. Your responsibilities will include developing near-real-time (NRT) streaming and batch processing pipelines to deliver critical insights for decision-making. You will also build self-service analytics tools and automate operational tasks, while contributing to AI-driven solutions for automated insights across the transportation network.

Key Responsibilities

• Design and implement scalable NRT streaming and batch data pipelines for Amazon's global transportation network.
• Develop data products and tools encompassing the full data lifecycle: ingestion, processing, backfill, storage, vending, and operational support.
• Collaborate with operations and analytics stakeholders to understand needs, design solutions, and provide data that facilitates informed decisions.
• Ensure data quality and enhance datasets to improve operational efficiency and customer experience.
• Partner with cross-functional teams to standardize analytics and develop AI-powered automation.
• Utilize AWS cloud technologies to transform raw data into actionable metrics.
• Promote continuous improvement through code reviews, design discussions, best practices, and security compliance.

About the Team

ShipTech Analytics is the data and analytics backbone of Amazon's global transportation network. We build standardized data solutions that streamline analytics and enable faster decision-making for operations teams globally. We collaborate with partners to deliver essential data products and tools, modernizing systems, building NRT architectures, and developing AI-powered automation. Our team fosters opportunities for mentorship, prototyping, and shaping the future of data analytics, with a strong emphasis on cross-geographical collaboration.