招聘
Amazon Lab126 is an inventive research and development company that designs and engineers high-profile consumer electronics. Lab126 began in 2004 as a subsidiary of Amazon.com, Inc., originally creating the best-selling Kindle family of products. Since then, we have produced devices like Fire tablets, Fire TV, Amazon Echo and Amazon Show. The Amazon Devices group delivers delightfully unique Amazon experiences, giving customers instant access to everything, digital or physical.
We are seeking an exemplary Data Engineer with broad technical skills to develop, own, and support the underlying data infrastructure and data pipelines that fuels our business analytics and research. The ideal candidate demonstrates thought leadership in data engineering practices and possesses a strategic vision for building scalable products and solutions. We look for candidates who are excellent communicators and have demonstrated leadership experience.
This role has great exposure to a broad scope that can really help shape the future of Global Operations products. The successful candidate will have a proven track record of leading and implementing strategies that align with organizational goals and demonstrate the ability to influence and guide technical decisions at a strategic level. You will have excellent problem solving abilities, and the ability to synthesize data into crisp and clear recommendations for our customers. You will have broad skills in data infrastructure management, database design, be comfortable dealing with ambiguity, large and complex data sets, develop complex data pipelines, and experience with CI/CD.
- Key job responsibilities
- Design, implement, and support a platform providing ad-hoc access to large datasets
- Interface with other technology teams to extract, transform, and load data from a wide variety of data sources using SQL
- Build robust and scalable data integration (ETL) pipelines using SQL, Python and AWS services such as Data Pipelines, Glue
- Implement data structures using best practices in data modeling, ETL/ELT processes, and SQL/Redshift
- Interface with business customers, gathering requirements and delivering complete reporting solutions
- Build and deliver high quality datasets to support business analyst and customer reporting needs
- Continually improve ongoing reporting and analysis processes, automating or simplifying self-service support for customers
Basic Qualifications
- Bachelor's degree
- 3+ years of data engineering experience
- Experience with data modeling, warehousing and building ETL pipelines
- 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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