Do you want to be in the forefront of engineering big data analytical solutions that takes Amazon's global transportation models to the next generation? Do you have a solid analytical thinking, metrics driven decision making and want to solve problems with solutions that will meet the growing worldwide need? We are looking for strong senior Business Intelligence Engineers to be part of ShipTech Analytics team to build data and analytics solutions powering Amazon's global transportation network. We perform data modeling, build real time analytical platforms using big data tools and AWS technologies like Hadoop, Spark, EMR, SNS, SQS, Lambda, Kinesis Firehose, DynamoDB Streams. As a BIE, you'll work on building analytical solutions that empower operations teams worldwide. The ideal candidate relishes working with large volumes of data, enjoys the challenge of highly complex technical contexts, and, above all else, is passionate about data and analytics. The candidate is an expert with data modeling, ETL design and business intelligence tools and passionately partners with the business to identify strategic opportunities where improvements in data infrastructure creates out-sized business impact and is a self-starter, comfortable with ambiguity, able to think big, and enjoys working in a fast-paced and global team. The candidate will also build metrics for the transportation network, while contributing to innovative AI-powered solutions delivering automated insights across the transportation network. Key job responsibilities 1. Design and build scalable ETL and metrics supporting Amazon's global transportation network. 2. Build data reporting and data tools that streamline the complete business lifecycle 3. Partner closely with stakeholders across operations and analytics teams to understand requirements, design solutions, and deliver metrics and insights that enable faster, more informed decision-making 4. Own data quality and implement enhancements for datasets that enable operational excellence and improve customer experience 5. Collaborate with cross-functional teams to standardize analytics capabilities and build AI-powered automation 6. Leverage AWS cloud technologies to transform raw data into actionable metrics 7. Drive continuous improvement through code reviews, design discussions, and operational best practices 8. Partner with senior engineers and principal engineers to solve problems at scale to improve existing data services, building new ones, that enhance customer experience