Data Engineer , Customer Behavior Analytics
Amazon
Iasi, ROU
5 zile în urmă

Job summary

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).

BASIC QUALIFICATIONS

  • 3+ years of experience as a Data Engineer or in a similar role
  • Experience with data modeling, data warehousing, and building ETL pipelines
  • Experience in SQL
  • Experience in one or more data vizualisation and access tools (e.g. AWS QuickSight, Kibana)
  • Experience with Java, Python, Scala or C++ · Bachelor's degree in computer science, engineering, mathematics, or a related technical discipline
  • Industry experience in software development, data engineering, business intelligence, data science, or related field with a track record of manipulating, processing, and extracting value from large datasets
  • Experience using big data technologies (Hadoop, Hive, HBase, Spark, EMR, etc.)
  • Knowledge of data management fundamentals and data storage principles
  • Knowledge of distributed systems as it pertains to data storage and computing
  • PREFERRED QUALIFICATIONS

    PREFERRED QUALIFICATIONS

  • 5+ years of experience as a Data Engineer, BI Engineer, Business / Financial Analyst or Systems Analyst in a company with large, complex data sources.
  • Experience building / operating highly available, distributed systems of data extraction, ingestion, and processing of large data sets
  • Experience working with AWS big data technologies (EMR, Redshift, S3)
  • Demonstrated strength in data modeling, ETL development, and data warehousing
  • Proven success in communicating with users, other technical teams, and senior management to collect requirements, describe data modeling decisions and data engineering strategy
  • Experience providing technical leadership and mentoring other engineers for best practices on data engineering
  • Background non-relational databases, Machine Learning and Data Mining is a plus
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