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职位Amazon

Data Engineer, Seller Partner Trust and Store Integrity Science

Amazon

Data Engineer, Seller Partner Trust and Store Integrity Science

Amazon

Seattle, WA, USA

·

On-site

·

Full-time

·

2w ago

Do you want to join an innovative team of scientists and engineers who use machine learning and artificial intelligence to help Amazon provide the best customer experience by preventing e Commerce fraud? Are you excited by the prospect of building scalable data infrastructure and pipelines that process terabytes of data, enabling state-of-the-art algorithms to solve real world problems? Do you like to own end-to-end data systems and directly impact the team's ability to deliver insights and models that drive company profitability? Do you enjoy collaborating in a diverse team environment?

If yes, then you may be a great fit to join the Amazon Selling Partner Trust & Store Integrity Science Team. We are looking for a talented data engineer who is passionate about building robust data platforms and pipelines that empower scientists to develop advanced machine learning systems, helping manage the safety of millions of transactions every day and scaling up our operations with automation.

Key job responsibilities

  • DATA INFRASTRUCTURE & PIPELINE DEVELOPMENT

  • Design, build, and maintain scalable data pipelines that support multiple ML model training and inference workflows

  • Develop and optimize ETL processes to ingest, transform, and prepare terabytes of data from diverse sources for model consumption

  • Implement robust data quality checks and monitoring systems to ensure data integrity across all pipelines

  • ML OPERATIONS SUPPORT

  • Build and maintain infrastructure for model training pipelines, including feature engineering, data versioning, and experiment tracking

  • Design and implement scalable inference pipelines that serve predictions for millions of transactions with low latency and high reliability

  • Collaborate with scientists to productionize ML models, translating research code into production-ready systems

  • SYSTEM PERFORMANCE & RELIABILITY

  • Optimize data processing workflows for cost efficiency and performance, managing compute and storage resources effectively

  • Implement monitoring, alerting, and logging systems to ensure pipeline reliability and quick issue resolution

  • Maintain comprehensive documentation of data schemas, pipeline architectures, and operational procedures

  • CROSS-FUNCTIONAL COLLABORATION

  • Partner closely with scientists to understand data requirements and translate them into technical solutions

  • Work with stakeholders to define data SLAs and ensure systems meet business needs

  • Provide technical guidance on data architecture decisions and best practices

Basic Qualifications

  • 3+ years of data engineering experience
  • 1+ years of developing and operating large-scale data structures for business intelligence analytics using ETL/ELT processes experience
  • 1+ years of developing and operating large-scale data structures for business intelligence analytics using OLAP technologies experience
  • 1+ years of developing and operating large-scale data structures for business intelligence analytics using data modeling experience
  • 1+ years of developing and operating large-scale data structures for business intelligence analytics using SQL experience
  • 1+ years of developing and operating large-scale data structures for business intelligence analytics using Oracle experience
  • Experience with data modeling, warehousing and building ETL pipelines

Preferred Qualifications

  • 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)

Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.

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.

The base salary range for this position is listed below. Your Amazon package will include sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location. Amazon also offers comprehensive benefits including health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage), 401(k) matching, paid time off, and parental leave. Learn more about our benefits at https://amazon.jobs/en/benefits.

USA, WA, Seattle - 132,100.00 - 178,800.00 USD annually

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关于Amazon

Amazon

Amazon

Public

Amazon.com, Inc. is an American multinational technology company engaged in e-commerce, cloud computing, online advertising, digital streaming, and artificial intelligence.

10,001+

员工数

Seattle

总部位置

$1.5T

企业估值

评价

2.9

10条评价

工作生活平衡

2.8

薪酬

3.7

企业文化

2.5

职业发展

2.3

管理层

2.1

35%

推荐给朋友

优点

Good pay and compensation

Strong benefits package

Flexible scheduling options

缺点

Poor management and leadership

Limited growth and promotion opportunities

High stress and demanding work environment

薪资范围

4个数据点

L2

L3

L4

L5

L6

L2 · Data Analyst L2

0份报告

$108,330

年薪总额

基本工资

$43,332

股票

$54,165

奖金

$10,833

$75,831

$140,829

面试经验

10次面试

难度

3.7

/ 5

时长

21-35周

录用率

20%

体验

正面 10%

中性 10%

负面 80%

面试流程

1

Application Review

2

Recruiter Screen

3

Online Assessment

4

Technical Phone Screen

5

Onsite/Virtual Loop

6

Team Matching

7

Offer

常见问题

Coding/Algorithm

System Design

Behavioral/STAR

Leadership Principles

Technical Knowledge