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职位Amazon

Data Engineer - FinTech, Fintech

Amazon

Data Engineer - FinTech, Fintech

Amazon

Hyderabad, TS, IND

·

On-site

·

Full-time

·

3d ago

We are seeking a highly skilled Data Engineer to join our Fin Tech ADA team, responsible for building and optimizing scalable data pipelines and platforms that power analytics, automation, and decision-making across Finance and Accounting domains. The ideal candidate will have strong expertise in AWS cloud technologies including Redshift, S3, AWS Glue, EMR, Kinesis, Firehose, Lambda, and IAM, along with hands-on experience designing secure, efficient, and resilient data architectures.

You will work with large-scale structured and unstructured datasets, leveraging both relational and non-relational data stores (object storage, key-value/document databases, graph, and column-family stores) to deliver reliable, high-performance data solutions. This role requires strong problem-solving skills, attention to detail, and the ability to collaborate with cross-functional teams to translate business needs into technical data solutions.

Key job responsibilities
Scope -
Fintech is seeking a Data Engineer to be part of Accounting and Data Analytics team. Our team builds and maintains data platform for sourcing, merging and transforming financial datasets to extract business insights, improve controllership and support financial month-end close periods. As a contributor to a crucial project, you will focus on building scalable data pipelines, optimizations of existing pipelines and operation excellence.

  • Qualifications-
  • 5+ yrs experience as Data Engineer or in a similar role
  • Experience with data modeling, data warehousing, and building ETL pipelines
  • Bachelor’s degree in Computer Science, Engineering, Mathematics, or a related field.
  • Extensive experience working with AWS with a strong understanding of Redshift, EMR, Athena, Aurora, DynamoDB, Kinesis, Lambda, S3, EC2, etc.
  • Experience with coding languages like Python/Java/Scala
  • Experience in maintaining data warehouse systems and working on large scale data transformation using EMR, Hadoop, Hive, or other Big Data technologies
  • Experience mentoring and managing other Data Engineers, ensuring data engineering best practices are being followed
  • Experience with hardware provisioning, forecasting hardware usage, and managing to a budget.
  • Exposure to large databases, BI applications, data quality and performance tuning

Basic Qualifications

  • 3+ years of data engineering experience
  • Experience with data modeling, warehousing and building ETL pipelines
  • Experience with SQL

Preferred Qualifications

  • 5+ years of data engineering experience
  • 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

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