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职位Amazon

Data Engineer II, AP Analytics

Amazon

Data Engineer II, AP Analytics

Amazon

Hyderabad, TS, IND

·

On-site

·

Full-time

·

2w ago

Do you want to be in the forefront of engineering big data solutions that takes data models to the next generation? Do you have a solid analytical thinking, metrics driven decision making and want to solve problems with solutions that will meet the growing worldwide need?

We are looking for top notch Data Engineers to build real time data analytical platforms using big data tools and AWS technologies like Spark, EMR, SNS, SQS, Lambda, Kinesis Firehose, DynamoDB Streams.

The ideal candidate relishes working with large volumes of data, enjoys the challenge of highly complex technical contexts, and, above all else, is passionate about data and analytics. He/she is an expert with data modeling, ETL design and business intelligence tools and passionately partners with the business to identify strategic opportunities where improvements in data infrastructure creates out-sized business impact. He/she is a self-starter, comfortable with ambiguity, able to think big (while paying careful attention to detail), and enjoys working in a fast-paced and global team. It's a big ask, and we're excited to talk to those up to the challenge!

4+ years of experience performing quantitative analysis, preferably for an Internet with large, complex data sources.
Hands-on experience on Big data technologies and frameworks. Hive, Spark, Hadoop, SQL on Big Data, Redshift
Experience in near real time analytics
Experience with scripting languages i.e. Python, Perl, etc.
Experience with ETL, Data Modeling, and working with large-scale datasets. Extremely proficient in writing performant SQL working with large data volumes
Ability to manage competing priorities simultaneously and drive projects to completion.
Bachelor's degree or higher in a quantitative/technical field (e.g. Computer Science, Statistics, Engineering).
Experience with large scale data processing, data structure optimization and scalability of algorithms a plus

Basic Qualifications

  • 4+ years of data engineering experience
  • 4+ years of SQL experience
  • 4+ years of developing and operating large-scale data structures for business intelligence analytics using ETL/ELT processes experience
  • Knowledge of batch and streaming data architectures like Kafka, Kinesis, Flink, Storm, Beam

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)
  • Experience providing technical leadership and mentoring other engineers for best practices on data engineering

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

Amazon

Public

Amazon.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