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Business Intelligence Engineer
2 months ago
We are seeking a highly skilled Business Intelligence Engineer to join our JP Retail Science team at Amazon. As a key member of our team, you will be responsible for designing and building robust data pipelines, architectures, and platforms to support the delivery of advanced analytics and machine learning models for our eCommerce partners.
Key Responsibilities- Work closely with frontline consultants to distill technical problems from business requirements.
- Define requirements and design for scalable deployment of solutions through paid services, leveraging tools on AWS and CDK.
- Identify appropriate metrics and feature sets for problems, and build/maintain automated pipelines for their extraction.
- Work closely with our scientists to design scalable model architectures.
- Utilize Amazon systems and tools to effectively work with terabytes of data.
- Independently apply models developed by science team to vendor use cases and draw business conclusions from results.
- Actively contribute to and maintain internal team BI infrastructure.
- Prepare materials for vendor-facing presentations and distill vendor problems into technical requirements.
You will be part of the JP Retail Science team, consisting of scientists and business intelligence engineers collaborating with Product Managers and Software Developers worldwide. Our current projects touch on areas such as causal inference, representation learning, anomaly detection, NLP, and forecasting. As part of working on Paid Services, you will be exposed to all other projects the team works on.
Requirements- 2+ years of experience building scalable ETL pipelines and analytical solutions.
- Experience programming to extract, transform, and clean large (multi-TB) data sets.
- Experience with data modeling, warehousing, and building ETL pipelines.
- Experience using SQL to pull data from a database or data warehouse and scripting experience (Python) to process data for modeling.
- Experience in Statistical Analysis packages such as R, SAS, and Matlab.
- Experience working directly with business stakeholders to translate between data and business needs.
- Experience with AWS solutions such as EC2, DynamoDB, S3, and Redshift.
- Experience with CDK.
