Amazon is one of the most popular sites in the US. Our product search engine, one of the most heavily used services in the world, indexes billions of products and serves hundreds of millions of customers world-wide.
Our team leads the data science and analytics efforts for the search page and we own multiple aspects of understanding how we can measure customer satisfaction with our experiences.
This includes building science based insights and novel metrics to define and track customer focused aspects. We manipulate massive amounts of data, terabytes worth a day, coming from multiple different systems with a high degree of complexity in structure, schema and different levels of quality.
We are looking for a data engineer to build and maintain large and complex data pipelines, orchestrated for daily updates with a very high level of senior leadership visibility.
These pipelines bring together data from multiple sources with complex structure. You will be creative with ETL techniques to optimize these jobs for performance and cost.
About the team
The mission of the Search Data Science team is to build a world class shopping experience that delights customers. We focus on the long term and big picture, ensuring that the search page is balancing strategic trade-offs.
We bring to this effort expertise in constrained optimization, causal inference, and marketplace equilibrium effects. We build systems, metrics, and mechanisms to ensure that product decisions are scientifically sound.
We develop models to estimate the downstream dollar value of the quality of the experience. We spend time on evaluating experiments to develop durable learnings.
BASIC QUALIFICATIONS
- 3+ years of data engineering 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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