採用
必須スキル
Python
Java
SQL
AWS
Spark
Scala
Have you ever ordered a product on Amazon and when that box with the smile arrived you wondered how it got to you so fast?
Have you wondered where it came from and how much it cost Amazon to deliver it to you?
If so, Amazon Logistics (AMZL), Last Mile team is for you. We manage the delivery of tens of millions of products every week to Amazon’s customers, achieving on-time delivery in a cost-effective manner to deliver a smile for our customers.
Amazon Logistics is looking for a customer focused, analytically and technically skilled Data Engineer to design, build, and maintain software solutions and advanced data infrastructure for AMZL leadership and BI teams in Japan. This position will focus on developing scalable solutions using AWS services to support our rapidly expanding logistics operations.
The successful candidate will be able to effectively manipulate large datasets, optimize systems for low latency and high accuracy, and implement quality checks and monitoring systems. This individual will collaborate with business, engineers, and science teams to understand business requirements and ensure solutions drives effective business impact. This role requires an individual with software development and data warehouse skills. The successful candidate will be a self-starter comfortable with ambiguity, with strong attention to detail, and enjoy working with large scale of data.
- Key job responsibilities
- Own the design, development, and maintenance of software solutions and data sets.
- Manipulate/mine data from database tables (Redshift, Apache Spark SQL)
- Conduct deep dive investigations into solution run errors and data issues.
- Identify and adopt best practices in developing software solutions, data pipelines, and data sources: test design, build, validation, and documentation.
- Continually track and improve software solutions and data pipeline healthiness.
- Work with in-house scientists, global supply chain, transportation and logistics teams, and software teams to identify new features and projects.
- Identify ways to optimize complex processes through AWS.
- This is an individual contributor role that will partner with internal stakeholders across multiple teams, gathering requirements and delivering complete solutions
Basic Qualifications
- 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
- Experience with one or more query language (e.g., SQL, PL/SQL, DDL, MDX, HiveQL, SparkSQL, Scala)
Preferred Qualifications
- Experience with AWS technologies like Redshift, S3, AWS Glue, EMR, Kinesis, Fire Hose, Lambda, and IAM roles and permissions
- Experience with big data technologies such as: Hadoop, Hive, Spark, EMR
- Speak, write, and read fluently in Japanese
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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Amazonについて

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+
従業員数
Seattle
本社所在地
$1.5T
企業価値
レビュー
2.9
10件のレビュー
ワークライフバランス
2.8
報酬
3.7
企業文化
2.5
キャリア
2.3
経営陣
2.1
35%
友人に勧める
良い点
Good pay and compensation
Strong benefits package
Flexible scheduling options
改善点
Poor management and leadership
Limited growth and promotion opportunities
High stress and demanding work environment
給与レンジ
4件のデータ
L2
L3
L4
L5
L6
L2 · Data Analyst L2
0件のレポート
$108,330
年収総額
基本給
$43,332
ストック
$54,165
ボーナス
$10,833
$75,831
$140,829
面接体験
10件の面接
難易度
3.7
/ 5
期間
21-35週間
内定率
20%
体験
ポジティブ 10%
普通 10%
ネガティブ 80%
面接プロセス
1
Application Review
2
Recruiter Screen
3
Online Assessment
4
Technical Phone Screen
5
Onsite/Virtual Loop
6
Team Matching
7
Offer
よくある質問
Coding/Algorithm
System Design
Behavioral/STAR
Leadership Principles
Technical Knowledge
ニュース&話題
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