招聘
Amazon's Global Procurement Organization manages indirect supply chain for World Wide Operations and Delivery Services. We work with massive amounts of data and leverage them for advanced applications.
We are Data Engineers to build the facilities to help enable the Global Procurement Organization to gain deep insights into all facets of indirectly supply management and apply advanced data science for automation and decision making.
You are someone who is customer focused, relentless and driven to help us build out a brand-new initiative. You will collaborate closely with product managers, developers and leaders across the org. To be successful in this role, you should have broad skills in database design, be comfortable dealing with large and complex data sets, modeling and designing a strong data warehouse and building self-service data platforms for stakeholders to utilize the data we manage.
- Key job responsibilities
- Own and design end-to-end data pipelines and models for complex procurement domains including spend analytics, supplier risk, contract compliance, and catalog management
- Drive technical design reviews and author architecture documents for new data systems and platform components
- Lead scalability and reliability improvements — identify bottlenecks in existing infrastructure, define solutions, and execute with or without a team behind you
- Build frameworks and reusable components that improve engineering productivity and standardize data patterns across GPT data products
- Partner with applied scientists and BIEs to define the data infrastructure needed to power ML models, forecasting engines, and self-service analytics platforms
- Define and enforce data quality standards — including data contracts, freshness SLAs, lineage tracking, and observability instrumentation
- Translate ambiguous business requirements from procurement operations, finance, and leadership into well-scoped technical solutions
- Mentor and develop junior data engineers through code reviews, design feedback, and pair programming
- Operate across multiple concurrent workstreams; manage tradeoffs between speed, correctness, and technical debt
Basic Qualifications
- 3+ years of data engineering experience
- 4+ years of SQL experience
- Experience with data modeling, warehousing and building ETL pipelines
- 2+ years of developing and operating large-scale data structures for business intelligence analytics using data modeling experience
- Experience in at least one modern scripting or programming language, such as Python, Java, Scala, or NodeJS
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)
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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关于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+
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
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