招聘

Business Intelligence Engineer , North America Network Sales and Operations planning
Houston, TX, USA
·
On-site
·
Full-time
·
1w ago
At Amazon Supply Chain, we power the world's fastest-growing e-commerce business and embrace the mission of being Earth’s most customer-centric company. As our business continues to expand, the customer fulfillment operations become larger and more complex each year.
The Network Sales and Operations Planning (S&OP) team forecasts all inventory flows across the entire North America Fulfillment network. We manage everything from what Amazon receives from vendors and sellers to what it ships from our Fulfillment Centers (FCs). The network S&OP forecast is the starting point of a weekly planning process spanning first, middle mile, last mile and defining the amount of resources (trucks, labor) that need to be secured to ensure we fulfill our customer orders at the highest speed, best quality, lowest cost and least carbon intensity as possible. We partner closely with Supply Chain Optimization Technology (SCOT) teams in North America and internal tech stakeholders across Transportation, Labor Planning and Finance teams to reconcile bottom-up and top-down inputs to build analytical forecasts which are accurate and provide valuable insight. We also partner with Supply Chain Execution (SCE) teams as well as Science partners to define the optimal allocation under constraints of volumes across the 9 Regions of the US fulfillment network.
We are now seeking a Business Intelligence Engineer to be part of a 2-3 person analytical group within the network S&OP team. This is a Day 1 opportunity to be part of an entrepreneurial and pioneering team building from the ground up. You will research forecasting techniques, deep dive existing data sources to build new models to improve forecasting accuracy. You will also develop data solutions and advanced visualization techniques to offer forecast analysis to senior stakeholders (up to SVP). You will collaborate with business, product management, and engineering teams to drive progress.
Key job responsibilities
- Develop new forecasting models
- Build advanced visualization solutions to allow business and tech stakeholders to deep dive and understand the teams forecasts.
- Collaborate across multiple teams to ensure alignment with business goals and objectives.
Basic Qualifications
- 3+ years of analyzing and interpreting data with Redshift, Oracle, NoSQL etc. experience
- 1+ years of SQL, ETL or Oracle experience
- 1+ years of processing large, multi-dimensional datasets from multiple sources experience
- 1+ years of performing statistical analysis experience
- 1+ years of developing automated reporting 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
- Bachelor's Degree
- Experience analyzing and communicating results to senior leadership
- Experience working directly with business stakeholders to translate between data and business needs
- Experience in scripting for automation (e.g. Python) and advanced SQL skills.
- Experience programming to extract, transform and clean large (multi-TB) data sets
- Experience with theory and practice of design of experiments and statistical analysis of results
- Experience with AWS technologies
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
- Experience with statistical analytics and programming languages such as R, Python, Ruby, etc.
- Experience using Cloud Storage and Computing technologies such as AWS Redshift, S3, Hadoop, etc.
- 3+ years of SQL experience
- Master's degree in statistics, data science, or an equivalent quantitative field
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, NY, New York - 109,500.00 - 185,000.00 USD annually
USA, TX, Houston - 99,500.00 - 160,000.00 USD annually
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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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