Data Engineer I

Do you have the technical skill to build BI solutions that process billions of rows a day using AWS technologies? Do you want to create next-generation tools for intuitive data access? Do you wake up in the middle of the night with new ideas that will benefit your customers? Are you persistent in bringing your ideas to fruition?

First things first, you know SQL and data modelling like the back of your hand. You also need to know Big Data and MPP systems. You have a history of coming up with innovative solutions to complex technical problems. You are a quick and willing learner of new technologies and have examples to prove your aptitude. You are not tool-centric; you determine what technology works best for the problem at hand and apply it accordingly. You can explain complex concepts to your non-technical customers in simple terms.

Key job responsibilities

• Work with SDE teams and business stakeholders to understand data requirements and design data ingress flow for team
• Lead the design, model, and implementation of large, evolving, structured, semi-structured and unstructured datasets
• Evaluate and implement efficient distributed storage and query techniques
• Interact and integrate with internal and external teams and systems to extract, transform, and load data from a wide variety of sources
• Implement robust and maintainable code with clear and maintained documentation
• Implement test automation on code implemented through unit testing and integration testing
• Work in a tech stack which is a mix of NAWS services and legacy ETL tools within Amazon

About the team
Data Insights, Metrics & Reporting team (DIMR) is the central data engineering team in Amazon Warehousing & Distribution org which is responsible for 4 things mainly -

• Building and maintaining data engineering and reporting infrastructure using NAWS to support internal/external data use-cases.
• Building data ingestions pipelines from any kind of upstream data sources which include (but not limited to) real time event streaming services, data lakes, manual file uploads, etc.
• Building mechanisms to vend data to internal team members or external sellers with right data handling techniques in place.
• Build robust data mart to support diverse use-cases powered by GenAI tool.