Data Engineer MIDAS Digital Acceleration
@ Amazon

Chennai, Tamil Nadu, India
$150,000
On Site
Full Time
Posted 22 days ago

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Job Details

Overview

Amazon's Digital Acceleration (DA) org seeks a Data Engineer to design and deliver advanced analytics that directly influence product decisions. Join the MIDAS team to develop foundational analytical datasets across orders, subscriptions, discovery, promotions, pricing, and royalties.

Key Job Responsibilities

  • Develop data products, infrastructure, and ETL pipelines using AWS services (Redshift, Kinesis, EMR, Lambda, etc.)
  • Improve existing solutions to enhance scale, quality, efficiency, and data compliance
  • Collaborate with Software Developers, BI Engineers, MLEs, Scientists, and Product Managers
  • Drive operational excellence through automation and robust data mechanisms

About The MIDAS Team

The MIDAS team within Amazon's Digital Analytics engineering builds analytics and data engineering solutions used by over 100 business and technology teams. With more than 20,000 monthly active users, the platform supports metadata discovery, data lineage, customer segmentation, compliance automation, and advanced data quality monitoring.

Basic Qualifications

  • Bachelor's degree
  • 3+ years of data engineering experience
  • Experience with data modeling, warehousing, and ETL pipelines
  • Proficiency in one modern scripting or programming language (Python, Java, Scala, NodeJS)

Preferred Qualifications

  • 5+ years of data engineering experience
  • Experience with AWS technologies (Redshift, S3, AWS Glue, EMR, Kinesis, Lambda)
  • Experience with non-relational databases and data stores
  • Knowledge of engineering and operational excellence methodologies

Key Skills/Competency

  • Data Engineering
  • ETL
  • Data Modeling
  • AWS
  • Big Data
  • Database Architecture
  • Automation
  • Operational Excellence
  • Python
  • Analytics
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How to Get Hired at Amazon

🎯 Tips for Getting Hired

  • Customize resume: Tailor your skills to match data engineering needs.
  • Highlight AWS expertise: Emphasize proficiency with AWS tools.
  • Prepare for technical screens: Practice ETL and data modeling challenges.
  • Research Amazon culture: Understand values and recent projects.

📝 Interview Preparation Advice

Technical Preparation

Review AWS service documentation.
Practice ETL pipeline challenges.
Brush up on data modeling concepts.
Familiarize with big data tools.

Behavioral Questions

Describe a data pipeline success story.
Explain handling project setbacks.
Discuss team collaboration examples.
Share a time managing complex tasks.

Frequently Asked Questions