The JP Economics and Decision Science team is a central science team working across a variety of topics in the Retail business and beyond. We work closely with business leaders to drive change at Amazon. We focus on solving long-term, ambiguous and challenging problems, while providing advisory support to help solve short-term business pain points. Key topics include pricing, product selection, delivery speed, profitability, and customer experience. We tackle these issues by building novel economic/econometric models, machine learning systems, and high-impact experiments which we integrate into business, financial, and system-level decision making.
We are looking for a Data Engineer to help put cutting edge economic and data science models into production. We are looking for a individual who is interested in working with the latest big data technology (Spark, EMR, Glue, SageMaker, and Airflow) and collaborate with Economist and Scientist in creating scalable solutions for our multiple Retail Businesses.
Key job responsibilities
- Partnering with economists and senior team members in building an environment of large high-quality datasets
- Create and support AWS based data platforms that leverage diverse data sources across Amazon, that feed input to and serve output from machine learning / econometric models
- Help us move fasted by building fast prototypes that help us prove out our ideas
- Develop and extend reusable libraries for quickly rolling out new data pipelines
- Create and drive data governance strategies for disparate data sources across the company
- Create and review technical requirement and design documents, working backward from customer needs
We are open to hiring candidates to work out of one of the following locations:
Bangalore, KA, IND
- 3+ years of data engineering experience
- Experience with data modeling, warehousing and building ETL pipelines
- 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)
- Experience with productionalizing ML/AI models.
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