招聘
Business Intelligence Engineer, Automated Inventory Management, SCOT IN JP, Supply Chain Optimization Technologies (SCOT)

Business Intelligence Engineer, Automated Inventory Management, SCOT IN JP, Supply Chain Optimization Technologies (SCOT)
Bengaluru, KA, IND
·
On-site
·
Full-time
·
1w ago
SCOT Regional Team in India is seeking highly motivated individuals with exceptional data analytics skills and a passion for tackling intricate challenges. In this role, you will utilize your expertise to inform impactful business decisions that enhance customer experience and contribute to long-term free cash flow growth. You will gain a comprehensive understanding of Amazon's systems and supply chain processes through collaboration with diverse teams across product, science, tech, retail categories, finance, and operations.
- Key job responsibilities
- Analyze and synthesize large data streams across multiple systems/inputs.
- Work with Product Managers to understand customer behaviors, spot system defects, and benchmark our ability to serve our customers, improving a wide range of internal products that impact sourcing and inventory health.
- Develop business insights basis data extraction, data analytics, trend deduction & Pattern recognition.
- Present these business insights to senior management/executives.
- Create advanced dashboard to help a large group of teams to consume insights make changes to business process and track progress.
- Build analytical models that can help improve business outcomes at scale enhancing current system abilities.
About the team
Have you ever ordered a product on Amazon and when that box with the smile arrived, wondered how it got to you so fast? Wondered where it came from and how much it cost Amazon? If so, Amazon’s Supply Chain Optimization Technology (SCOT) organization is for you. At SCOT, we solve deep technical problems and build innovative solutions in a fast-paced environment working with smart & passionate team members. (Learn more about SCOT: http://bit.ly/amazon-scot)
Basic Qualifications
- 3+ years of analyzing and interpreting data with Redshift, Oracle, NoSQL etc. experience
- Experience with data visualization using Tableau, Quicksight, or similar tools
- Experience with data modeling, warehousing and building ETL pipelines
- Experience in Statistical Analysis packages such as R, SAS and Matlab
- Experience using SQL to pull data from a database or data warehouse and scripting experience (Python) to process data for modeling
Preferred Qualifications
- Experience with AWS solutions such as EC2, DynamoDB, S3, and Redshift
- Experience in data mining, ETL, etc. and using databases in a business environment with large-scale, complex datasets
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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