Data Engineer, Amazon Customer Service

Global technology company focused on e-commerce, cloud computing, digital streaming, and artificial intelligence.
$118,900 - $205,600
Data
Mid-Level Software Engineer
In-Person
5,000+ Employees
3+ years of experience
Enterprise SaaS

Description For Data Engineer, Amazon Customer Service

Amazon's Global Customer Service Concessions Team is seeking a passionate Data Engineer to join their dynamic team. This role focuses on leveraging data to deliver actionable insights that improve customer experience across different marketplaces. The team systematically identifies and eliminates defects leading to returns and customer issues.

As a Data Engineer, you'll work with cutting-edge big data technologies, developing and maintaining data pipelines for both structured and unstructured data. You'll collaborate with a diverse team of Software Developers, Business Intelligence Engineers, and Scientists in a fast-paced yet welcoming environment. The role offers exposure to statistical and Natural Language modeling through collaboration with scientists on global issue detection models.

The position offers competitive compensation ranging from $118,900 to $205,600 based on location and experience, plus comprehensive benefits including medical, dental, vision coverage, 401(k), and parental leave. You'll be part of a team that values diversity, inclusion, and work-life balance while working on impactful projects that directly affect customer experience.

The ideal candidate should have 3+ years of experience in data engineering, strong knowledge of SQL and Python, and experience with AWS technologies. You'll need excellent communication skills and the ability to work effectively with diverse teams. This role provides an excellent opportunity for career growth within Amazon's established and experienced team structure.

Last updated an hour ago

Responsibilities For Data Engineer, Amazon Customer Service

  • Develop scalable and maintainable data pipelines for structured and unstructured data
  • Partner with Software Developers, Business Intelligence Engineers, Scientists, and Program Managers
  • Oversee existing pipelines and develop new ones for reporting and data warehouse
  • Work across multiple data-sources and understand system communications
  • Prioritize multiple deliverables and communicate progress and blockers effectively

Requirements For Data Engineer, Amazon Customer Service

Python
  • 3+ years of data engineering experience
  • Experience with data modeling, warehousing and building ETL pipelines
  • Experience with one or more query language (SQL, PL/SQL, DDL, MDX, HiveQL, SparkSQL, Scala)
  • Experience in building pipeline in Apache Airflow or similar tools
  • Experience with one or more scripting language (Python, KornShell)
  • Bachelor's Degree

Benefits For Data Engineer, Amazon Customer Service

Medical Insurance
Dental Insurance
Vision Insurance
Parental Leave
401k
  • Medical, Dental, and Vision Coverage
  • Maternity and Parental Leave Options
  • Paid Time Off (PTO)
  • 401(k) Plan

Interested in this job?

Jobs Related To Amazon Data Engineer, Amazon Customer Service

Business Intelligence Engineer II, PAM

Business Intelligence Engineer II role at Amazon focusing on supply chain planning automation and data solutions for global operations.

Business Intelligence Engineer, AMZL EU BAT

Business Intelligence Engineer role at Amazon Logistics focusing on data analysis, visualization, and optimization of last-mile delivery operations in Europe.

Business Intelligence Engineer II, PAM

Business Intelligence Engineer II role at Amazon focusing on data solutions and automation for global Sales and Operations Planning.

Data Engineer II, AET Central Services Technology - Data

Data Engineer role at Amazon working on Foundational People Data Services team, building scalable data solutions using AWS technologies.

Data Engineer II, Ring Data Platform

Data Engineer II position at Ring focused on building and maintaining data infrastructure using AWS services and internal tools to support business analytics and data science initiatives.