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Business Intelligence Engineer, Prime Video APAC and ANZ

Amazon

Business Intelligence Engineer, Prime Video APAC and ANZ

Amazon

Mumbai, MH, IND

·

On-site

·

Full-time

·

1mo ago

Benefits & Perks

Healthcare

401(k)

Equity

Paid Time Off

Parental Leave

Mental Health

Healthcare

401k

Equity

Parental Leave

Mental Health

Required Skills

SQL

Python

Tableau

Quicksight

Data Modeling

ETL

R

SAS

Matlab

Are you interested in shaping the future of entertainment? Prime Video's technology, marketing, and operations teams are creating the best-in-class digital video experience. You’ll get to work on projects that are fast-paced, challenging, and varied. You’ll also be able to experiment with new possibilities, take risks, and collaborate with remarkable people.

Prime Video APAC and ANZ team is seeking a Business Intelligence Engineer to drive customer insights for the PV APAC and ANZ analytics team. As a Business Intelligence Engineer, you will generate insights that support Prime Video India's content, programming, marketing, and marketplace lines of business.

The successful candidate will be a self-starter who is comfortable with ambiguity and large data sets. They will have strong attention to detail and an ability to work in a fast-paced and entrepreneurial environment. They will have strong SQL skills, experience building visual dashboards, and the ability to clearly communicate information to business stakeholders.

  • Key job responsibilities
  • Strong interest and ability to take complex datasets and boil them down to actionable data visualizations.
  • Own the design, development, and maintenance of ongoing performance dashboards, metrics, reports, analyses, etc. to drive key business decisions.
  • Build scalable, efficient, and automated data processes to inform business decision making.
  • Proactively develop new metrics and studies to quantify customer behavior and reduce customer churn.

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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About 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+

Employees

Seattle

Headquarters

Reviews

2.9

10 reviews

Work Life Balance

2.8

Compensation

3.7

Culture

2.5

Career

2.3

Management

2.1

35%

Recommend to a Friend

Pros

Good pay and compensation

Strong benefits package

Flexible scheduling options

Cons

Poor management and leadership

Limited growth and promotion opportunities

High stress and demanding work environment

Salary Ranges

2 data points

L2

L3

L4

L5

L6

L2 · Data Analyst L2

0 reports

$108,330

total / year

Base

$43,332

Stock

$54,165

Bonus

$10,833

$75,831

$140,829

Interview Experience

10 interviews

Difficulty

3.7

/ 5

Duration

21-35 weeks

Offer Rate

20%

Experience

Positive 10%

Neutral 10%

Negative 80%

Interview Process

1

Application Review

2

Recruiter Screen

3

Online Assessment

4

Technical Phone Screen

5

Onsite/Virtual Loop

6

Team Matching

7

Offer

Common Questions

Coding/Algorithm

System Design

Behavioral/STAR

Leadership Principles

Technical Knowledge