Business Intelligence Engineer II
Confirmed on the employer's own hiring board on Sep 28, 2026. First seen by Alion on Sep 16, 2026.
Amazon's vision is to be the earths most customer centric company . The ambition of the Amazon payments business is to transform payments in India . The payments data team provides data and insights to the partner verticals so as to enable them to take decisions. The role requires team-work and building a business perspective. The individual will have to interact extensively with other members within the team, across business lines work in partnership with Pay Finance, Data Engineering and Central Customer Analytic teams. There will be a steep learning curve, adding a fair amount of business skills to the individual.
Key job responsibilities
1) Ability to do exploratory analysis: Fetch data from systems and analyze trends
2) Identify consumer segments that need to be actioned to drive adoption of Amazon Pay
3) Developing India specific customer segmentation models to improve the efficiency of marketing and financing product roadmap inputs.
4) Establishing mechanisms for cross functional teams to consume customer insights to improve engagement along the customer life cycle
5) Work with cross functional teams
About the team
The ambition of the Amazon payments business is to transform payments in India . The payments data team provides data and insights to the partner verticals so as to enable them to take decisions.
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
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