Data Engineer-I

Overview

- 1+ years of data engineering experience - Experience with data modeling, warehousing and building ETL pipelines - Experience with one or more query language (e.g., SQL, PL/SQL, DDL, MDX, HiveQL, SparkSQL, Scala) - Experience with one or more scripting language (e.g., Python, KornShell)
Amazon strives to be Earth's most customer-centric company where people can find and discover virtually anything they want to buy online. By giving customers more of what they want – low prices, vast selection, and convenience – Amazon continues to grow and evolve as a world-class e-commerce platform. Do you have solid analytical thinking, metrics-driven decision making and want to solve problems with solutions that will meet the growing worldwide need? Then SmartCommerce is the team for you. We are looking for top notch Data Engineers to be part of our analytics team. The ideal candidate will be curious, have attention to detail, be energized by challenging entrepreneurial environment, be comfortable thinking big while also diving deep. Are you a smart, hungry, flexible, and world-class data engineer excited by the challenge of launching a new business initiative for Amazon? SmartCommerce team is looking for Data Engineers to be part of a new team being built from the ground up. They will be primarily working on our product SmartBiz. SmartBiz by Amazon is a one-stop shop for Indian sellers to fulfill their online selling needs. Whether a small business, an entrepreneur, or a neighborhood store, a seller can now create their own e-commerce store within minutes and start showcasing and selling their products online. Key job responsibilities 1. Responsible for designing, building and maintaining complex data solutions for Amazon's SmartCommerce businesses 2. Actively participates in the code review process, design discussions, team planning, operational excellence, and constructively identifies problems and proposes solutions 3. Makes appropriate trade-offs, re-use where possible, and is judicious about introducing dependencies 4. Makes efficient use of resources (e.g., system hardware, data storage, query optimization, AWS infrastructure etc.) 5. Asks correct questions when data model and requirements are not well defined and comes up with designs which are scalable, maintainable and efficient 6. Makes enhancements that improve team’s data architecture, making it better and easier to maintain (e.g., data auditing solutions, automating, ad-hoc or manual operation steps) 7. Owns the data quality of important datasets and any new changes/enhancements