招聘
Required Skills
Data Engineering
ETL
Data Modeling
SQL
Python or Java or Scala or NodeJS
Are you excited about the digital media revolution and passionate about designing and delivering advanced analytics that directly influence the product decisions of Amazon's digital businesses. Do you see yourself as a champion of innovating on behalf of the customer by turning data insights into action?
The Amazon Digital Acceleration (DA) org is looking for an analytical and technically skilled data engineer to join our team. In this role, you will play a critical part in developing foundational analytical datasets spanning orders, subscriptions, discovery, promotions, pricing and royalties. Our mission is to enable digital clients to easily innovate with data on behalf of customers and make product and customer decisions faster.
An ideal individual is someone who has deep data engineering skills around ETL, data modeling, database architecture and big data solutions. This individual should have strong business judgement, excellent written and verbal communication skills.
Key job responsibilities
-
Develop data products, infrastructure and data pipelines leveraging AWS services (such as Redshift, Kinesis, EMR, Lambda etc.) and internal BDT tools (Datanet, Cradle, Quick Sight etc.
-
Improve existing solutions/build solutions to improve scale, quality, IMR efficiency, data availability, consistency & compliance.
-
Partner with Software Developers, Business Intelligence Engineers, MLEs, Scientists, and Product Managers to develop scalable and maintainable data pipelines on both structured and unstructured (text based) data.
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Drive operational excellence strongly within the team and build automation and mechanisms to reduce operations
About the team
The MIDAS team operates within Amazon's Digital Analytics (DA) engineering organization, building analytics and data engineering solutions that support cross-digital teams. Our platform delivers a wide range of capabilities, including metadata discovery, data lineage, customer segmentation, compliance automation, AI-driven data access through generative AI and LLMs, and advanced data quality monitoring. Today, more than 100 Amazon business and technology teams rely on MIDAS, with over 20,000 monthly active users leveraging our mission-critical tools to drive data-driven decisions at Amazon scale.
Basic Qualifications
- Bachelor's degree
- 3+ years of data engineering experience
- 3+ years of analyzing and interpreting data with Redshift, Oracle, NoSQL etc. experience
- Experience with data modeling, warehousing and building ETL pipelines
- Experience working on and delivering end to end projects independently
- Experience in at least one modern scripting or programming language, such as Python, Java, Scala, or NodeJS
Preferred Qualifications
- 5+ years of data engineering experience
- Experience with AWS technologies like Redshift, S3, AWS Glue, EMR, Kinesis, Fire Hose, 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)
- Knowledge of Engineering and Operational Excellence using standard methodologies.
Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.
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About Amazon

Amazon
PublicAmazon.com, Inc. is an American multinational technology company engaged in e-commerce, cloud computing, online advertising, digital streaming, and artificial intelligence.
10,001+
Employees
Seattle
Headquarters
Reviews
2.9
10 reviews
Work Life Balance
2.8
Compensation
3.7
Culture
2.5
Career
2.3
Management
2.1
35%
Recommend to a Friend
Pros
Good pay and compensation
Strong benefits package
Flexible scheduling options
Cons
Poor management and leadership
Limited growth and promotion opportunities
High stress and demanding work environment
Salary Ranges
2 data points
L2
L3
L4
L5
L6
L2 · Data Analyst L2
0 reports
$108,330
total / year
Base
$43,332
Stock
$54,165
Bonus
$10,833
$75,831
$140,829
Interview Experience
10 interviews
Difficulty
3.7
/ 5
Duration
21-35 weeks
Offer Rate
20%
Experience
Positive 10%
Neutral 10%
Negative 80%
Interview Process
1
Application Review
2
Recruiter Screen
3
Online Assessment
4
Technical Phone Screen
5
Onsite/Virtual Loop
6
Team Matching
7
Offer
Common Questions
Coding/Algorithm
System Design
Behavioral/STAR
Leadership Principles
Technical Knowledge
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