DESCRIPTION
Carpool BI LATAM team is creating the underlying software, apps and infrastructure to develop robust data products aiming to process data, benchmarking and enabling other BI solutions for Retail purposes.
This role is expected to primarily support the development of the core software modules needed to execute BI and data engineering tasks and could be flexible to support work across the different BI squads. We are looking to expand beyond simple heuristics and data products to more robust, learning-based approaches, but our first priority this year is delivering a product that works for benchmarking purposes. This role will be expected to be in the Mexico office, developing and testing features, observing and supporting them as they deploy to prod, and identifying new opportunities to build scalable framework, tools in which our team can invest for delivering better BI and data products for Amazon Retail.
This role is expected to primarily support the development of the core software modules needed to execute BI and data engineering tasks and could be flexible to support work across the different BI squads. We are looking to expand beyond simple heuristics and data products to more robust, learning-based approaches, but our first priority this year is delivering a product that works for benchmarking purposes. This role will be expected to be in the Mexico office, developing and testing features, observing and supporting them as they deploy to prod, and identifying new opportunities to build scalable framework, tools in which our team can invest for delivering better BI and data products for Amazon Retail.
BASIC QUALIFICATIONS
- Experience in automating, deploying, and supporting infrastructure
- Experience programming with at least one modern language such as Python, Ruby, Golang, Java, C++, C#, Rust
PREFERRED QUALIFICATIONS
- Knowledge of and proficiency in the use of Python scripting language
- Bachelor's degree or above in computer science, computer engineering, or related field
- Knowledge of best practices for software deployment
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