Data Engineer, ALX
Amazon
DESCRIPTION
What does the future of business intelligence look like with the emergence of gen AI? What are the tools and processes needed to evolve this space long-term? What do we need to build today to get there?
Welcome to the Amazon Leadership Experience (ALX) organization. We are a business intelligence team and our purpose is to use data to influence the way Amazon grows and scales our talent and compensation strategies for over a million employees worldwide. The business intelligence world is experiencing a fundamental shift from traditional reporting to predictive and prescriptive analytics. For ALX, this means building a foundation that can support both current needs and future innovations.
If you are looking for a role and a team that can make large scale impact, this is the place to be. We are an interdisciplinary team that develops evidence-based products and services that power the growth and development of Amazon’s talent across all of our businesses and locations around the world. As a Data Engineer on our team, you'll work with emerging technologies and complex data environments. You'll be responsible for enhancing our existing data architecture to further standardize metrics and definitions, developing end-to-end data engineering solutions for complex analytical problems, and collaborating with other data and software engineers to drive operational excellence standards.
This role requires you to live at the intersection of data, software, and science. We leverage a comprehensive suite of AWS technologies, with key tools including S3, Redshift, DynamoDB, Lambda, API's, Glue, MWAA, DataZone, and SageMaker. You will drive the development process from design to release.
Key job responsibilities
-Using industry best practices in building CI/CD pipelines for automated deployment and testing.
-Applying Infrastructure as Code (IaC) tools like Terraform, CloudFormation, or CDK to manage and provision cloud infrastructure.
-Using scripting for automation and tool development, which is scalable, reusable, and maintainable.
-Creating scalable data models for effective data processing, storage, retrieval, and archiving.
-Leveraging code repositories (e.g., Git, CodeCommit, etc.) for effective version control.
-Managing data ingestion from heterogeneous data sources, with automated data quality checks.