Data Engineer II, ShipTech Analytics

Overview

ShipTech Analytics (STA) is on a mission to revolutionize Amazon's global transportation network through data-driven innovation and artificial intelligence. Our vision is to be the central nervous system of Amazon's transportation operations, providing real-time insights, diagnostive analytics, and AI-powered decision-making capabilities that drive operational excellence across the network.

As a Data Engineer, you'll be building and scaling mission-critical data products that power worldwide operations business decisions. In this role, you'll drive technical excellence while building data infrastructure and products. You'll be working on scalable solutions that process petabytes of data daily, serving diverse user personas from front-line operators to corporate leadership. We expect you to bring strong technical acumen to solve difficult problems at scale, while maintaining high operational standards and driving innovation in both traditional analytics and AI/ML domains.

Responsibilities

• Design and implement scalable, secure data pipelines and infrastructure using AWS technologies and big data tools
• Build and maintain high-performance ETL processes that handle large-scale, complex datasets
• Architect end-to-end analytical solutions that are highly available, stable, and cost-effective
• Transform raw data into actionable insights through effective data modeling and integration
• Implement best practices in data system creation, data integrity, and documentation
• Proactively identify opportunities for process improvement and automation
• Partner with business stakeholders to gather requirements and translate them into technical solutions

Basic Qualifications

• 3+ years of data engineering experience
• 4+ years of SQL experience
• Experience with data modeling, warehousing and building ETL pipelines

Preferred Qualifications

• Experience with AWS technologies like Redshift, S3, AWS Glue, EMR, Kinesis, FireHose, Lambda, and IAM roles and permissions
• Experience with non-relational databases / data stores (object storage, document or key-value stores, graph databases, column-family databases)

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