Data Engineer , Retail Business Service

- 1+ years of data engineering experience
- Experience with SQL
- 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)
- Experience with big data technologies such as: Hadoop, Hive, Spark, EMR
- Experience with any ETL tool like, Informatica, ODI, SSIS, BODI, Datastage, etc.

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Job summary
Retail Business Services (RBS) supports Amazon’s Retail business growth WW through three core tasks. These are (a) Selection, where RBS sources, creates and enrich ASINs to drive GMS growth; (b) Defect Elimination: where RBS resolves inbound supply chain defects and develops root cause fixes to improve free cash flow and (c) supports operational process for WW Retail teams.
Our team of high caliber software developers, applied scientists, data engineers, product managers and Business Intelligence Engineers use rigorous ML and deep learning approaches to ensure that we identify & fix the right catalog defect to ensure the good shopping experience for our customers.
As a Data Engineer, you will own and deliver data engineering initiatives and build end-to-end analytical solutions that are highly available, scalable, stable, secure, and cost-effective. You strive for simplicity, demonstrate creativity and sound judgement. You deliver data solutions that are customer focused, easy to consume and create business impact. You are passionate about working with huge datasets and have experience with the organization and curation of data for analytics. You have a strategic and long term view on architecting advanced data eco systems. You are experienced in building efficient and scalable data services and have the ability to integrate data systems with AWS tools and services to support a variety of customer use cases/applications.
In this role, you have the opportunity to:


Implement data ingestion routines both real time and batch using best practices in data modeling, ETL/ELT processes by leveraging AWS technologies and big data tools.

Design, implement and operate large-scale, high-volume, high-performance data for analysis and data science.

Gather business and functional requirements and translate these requirements into robust, scalable, operable solutions with a flexible and adaptable data architecture.

Help continually improve ongoing reporting and analysis processes, automating or simplifying self-service modeling and production support for customers.