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We are looking for a highly skilled and experienced AWS Data Engineer to design, build, and optimize our next-generation data platform. This is a critical role that will allow you to leverage your deep technical expertise to build robust data assets that drive key business decisions.
Job Responsibility:
Architecting Data Pipelines: Design, build, and maintain highly scalable and efficient data pipelines using cutting-edge AWS services
Driving Code Excellence: Perform significant Code Refactoring on a legacy codebase—cleaning, modernizing, and improving readability and long-term maintainability
Mastering Optimization: Apply your deep understanding of Spark SQL and PySpark to continuously optimize code for performance and cost efficiency
Embracing Quality: Champion Test-Driven Development (TDD) and meticulous Unit Testing to ensure functionality, catch bugs early, and maintain a high bar for code quality
Solving the Toughest Challenges: Dive into complex systems to debug and resolve difficult bugs related to performance, concurrency, or intricate logic flaws
Collaborating on Cloud Architecture: Leverage and explain the benefits of key AWS services like S3, EC2, Lambda, Redshift, and CloudFormation to build robust solutions
Requirements:
7+ years of hands-on experience in Data Engineering, with a proven track record of creating high-impact data pipelines and assets
4+ years of strong, hands-on programming experience with PySpark, Python, and Boto3, including expertise in Python frameworks and libraries, adhering to best practices
Deep knowledge of Code Versioning practices, including Git Repositories and experience with artifact management tools like JFrog Artifactory
Solid practical knowledge of AWS Architecture and its core data services
A true commitment to clean code, testability, and engineering best practices