Business Intelligence Engineer, Minerva
6 months ago
Come join us as we revolutionize the book industry and deliver an amazing experience to our authors and readers.
Key job responsibilities
As a Business Intelligence Engineer at Amazon, you will connect with world leaders in your field working on similar problems. You will be working with massive-scale data and providing analytic support to scientists, product managers, engineers using these data. You will utilize your deep expertise in business analysis, metrics, reporting, and analytic tooling/languages like SQL, Excel, and others, to translate the data into meaningful insights. You will have ownership of the insights you are building for the business, and will play an integral role in tactical decision-making for critical risk areas.
About the team
Minerva is a cross-functional team of highly experienced scientists, data engineers, and software engineers with a critical business mission making revolutionary leaps forward using massive-scale data with advanced analytics and machine learning and helping democratize the publishing industry. We build science-based systems for marketing and content-discovery for indie authors, fraud/abuse, and content risk. We also use science to optimize manufacturing, fulfillment, and quality processes for our Print On Demand (POD) business.
We are open to hiring candidates to work out of one of the following locations:
Bengaluru, KA, IND
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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