Data Engineer I, Intl. Seller Growth

Global technology and e-commerce company that leads in online retail, cloud computing, and artificial intelligence.
Data
Entry-Level Software Engineer
In-Person
5,000+ Employees
1+ year of experience
Enterprise SaaS · AI

Description For Data Engineer I, Intl. Seller Growth

Join Amazon's International Seller Services (ISS) Central Analytics Data Engineering team in a role that combines technical leadership with hands-on data engineering. As a Data Engineer I, you'll be part of a team supporting Applied Scientists, Data Scientists, and Economists in turning machine learning and AI research into impactful products. The role involves working with massive datasets, building end-to-end analytical solutions that are highly available, scalable, and secure. You'll be responsible for designing and implementing large-scale data structures, creating efficient ETL/ELT processes, and working with AWS technologies. This position offers the opportunity to work with cutting-edge big data technologies while solving complex problems in international seller growth. The ideal candidate will combine technical expertise with strategic thinking to architect advanced data ecosystems that drive business value. Working at Amazon means being part of a global technology leader while contributing to solutions that impact millions of sellers worldwide.

Last updated a day ago

Responsibilities For Data Engineer I, Intl. Seller Growth

  • Design, implement and operate large-scale, high-volume, high-performance data structures for analytics and data science
  • Implement data ingestion routines using best practices in data modeling, ETL/ELT processes
  • Gather business and functional requirements and translate these into robust, scalable solutions
  • Collaborate with engineers to help adopt best practices in data system creation
  • Identify opportunities in existing data solutions for improvements

Requirements For Data Engineer I, Intl. Seller Growth

Python
  • 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)
  • Knowledge of writing and optimizing SQL queries in a business environment with large-scale, complex datasets

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