Amazon has a culture of data-driven decision-making and demands business intelligence that is timely, accurate, and actionable.
The Customer Behavior Analytics team’s role is to answer who did what, where and when it was done, and how key metrics were impacted.
Ultimately, we want to answer why customer behavior changes, and how to influence it.
Our analytics group is responsible for processing big data sets of website and mobile interactions, in order to identify and understand customer behavior.
By understanding traffic flows and customer behavior across Amazon websites, our organization creates business opportunities allowing Amazon strong and sustained growth rates.
Our traffic, customer interaction and attribution datasets power personalization, A / B testing, content optimization, internationalization, marketing spend allocation, product demand, customer conversion, selection reach and application performance (latency, errors, crashes).
Key job responsibilities
As a Data Engineer you gather and understand requirements, build consensus around business logic and data pipeline design with your team, and you work to achieve high quality datasets.
You will be building systems that deal with huge amounts of data in a reliable, accurate and expedient manner. You must have a passion for learning and diving deep into complex problems, and enjoy the challenge of operating reliable systems.
Creating reliable, scalable, and high-performance products requires a sound understanding of the fundamentals of Computer Science and practical experience building large-scale distributed systems.
Successful candidates come from a strong data engineering background. You have experience with structured and unstructured data sources, and are able to analyze and transform the data using various tools.
In addition to SQL, which is a strong requirement, understanding of a high-level programming language is critical.
A day in the life
We are looking for data engineers to join the Client-Side Metrics team and help us build datasets and tools that are used by internal teams to improve the customer experiences of Amazon.
com web and native applications (e.g. reduce customer perceived website latency, eliminate errors and frustration, discover traffic bottlenecks).