Software Development Engineer, Communications and Corporate Responsibility

Amazon.com, Inc.•New York, NY

27d

About The Position

As a Software Development Engineer in the Communications and Corporate Responsibility (CCR) Tech organization, you will work in a collaborative team environment delivering data-driven solutions and analytics capabilities. Our team specializes in building and maintaining data pipelines and analytics platforms that help internal customers access, analyze, and understand Amazon's communications efforts globally. In this role, you will develop and maintain data processing systems and analytics applications. Working alongside experienced engineers, you'll design and implement data ingestion pipelines, transform raw data into actionable insights, and build efficient data storage solutions using technologies like Amazon Redshift, S3 data lakes, and various AWS analytics services. This is an ideal role for engineers with data engineering expertise who want to expand their impact beyond traditional data functions. You'll have opportunities to grow as both a data specialist and a well-rounded software developer. Your daily work will involve writing maintainable, efficient code for data processing and analytics workflows. You'll design and implement ETL pipelines, optimize query performance, and ensure data quality and consistency. Key responsibilities include building and maintaining data lakes using AWS services, developing ETL processes for data ingestion and transformation, optimizing Redshift queries and table designs, implementing data quality checks and monitoring, and creating efficient data models for analytics use cases. You'll be joining a data-focused technical team within a primarily non-technical organization, providing unique opportunities to work with various data sources while building analytics solutions that make immediate impact. We emphasize data quality, performance optimization, and sustainable development practices. Our work spans several core data initiatives supporting Amazon's communications efforts, giving you exposure to different types of data processing and analytics challenges. As part of our team, you'll contribute to systems that help internal customers derive insights from Amazon's communications data across the globe. You'll work with modern data architectures and see the direct impact of your solutions on our customers' ability to make data-driven decisions. We've built our team from scratch since 2022, establishing a culture that emphasizes continuous learning in data engineering and analytics. We believe in providing regular feedback and support to help our engineers develop their skills in modern data technologies and advance their careers. If you're passionate about data engineering and want to work in an environment that supports your growth as a data-focused software developer.

Requirements

3+ years of non-internship professional software development experience
2+ years of non-internship design or architecture (design patterns, reliability and scaling) of new and existing systems experience
Experience programming with at least one software programming language
experience with data warehouse technologies, particularly Amazon Redshift
ETL pipeline development and maintenance
SQL and data modeling
working with large-scale data processing systems
data lake architecture and implementation
Python, Java, or similar languages for data processing

Nice To Haves

3+ years of full software development life cycle, including coding standards, code reviews, source control management, build processes, testing, and operations experience
Bachelor's degree in computer science or equivalent

Responsibilities

writing maintainable, efficient code for data processing and analytics workflows
designing and implementing ETL pipelines
optimizing query performance
ensuring data quality and consistency
building and maintaining data lakes using AWS services
developing ETL processes for data ingestion and transformation
optimizing Redshift queries and table designs
implementing data quality checks and monitoring
creating efficient data models for analytics use cases

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