Data Engineer

Who you are

• Experience in data engineering
• Experience with data modeling, warehousing and building ETL pipelines
• Experience with AWS technologies like Redshift, S3, AWS Glue, EMR, Kinesis, FireHose, Lambda, and IAM roles and permissions
• Experience with non-relational databases / data stores (object storage, document or key-value stores, graph databases, column-family databases)

What the job involves

• Are you passionate about building data solutions that power decisions at massive scale?
• As a Data Engineer II at Amazon, you will design, build, and optimize data architectures that turn raw information into reliable, high-quality datasets used by analysts, scientists, and engineering teams
• You will work across the full data lifecycle — from ingesting and transforming semi-structured data streams to creating logical data models that drive physical design and enable self-service analytics
• This role offers the opportunity to solve complex data challenges, improve how teams discover and trust their data, and directly influence business outcomes through the systems you create
• Key job responsibilities
• Design and implement ETL workflows and data pipelines that extract, transform, and load data at scale, ensuring stability, performance, and efficient resource usage across distributed systems and cloud infrastructure
• Create and maintain logical and physical data models that accurately represent business requirements, enforce data integrity, and support downstream analytics and reporting needs
• Write clean, well-documented code using SQL and scripting languages to automate data processes, optimize query performance, and build tools for tracking data lineage and improving data quality
• Collaborate with analysts, scientists, and software engineers to understand data requirements, balance competing priorities, and deliver usable datasets that provide clear business value
• Participate in code reviews, design discussions, and operational excellence initiatives, contributing to continuous improvement of team data solutions while mentoring peers on system architecture and best practices
• You will start your day reviewing pipeline health dashboards and resolving any data quality issues before they reach downstream consumers. From there, you might pair with a scientist to refine a data model, write and optimize SQL queries for a new reporting dataset, or evaluate a generative AI tool to accelerate a repetitive transformation task. Afternoons often include design reviews with your team, where you propose improvements to existing architectures or troubleshoot production incidents at their root cause