Job Summary: As an AWS Data Engineer with expertise in AWS Glue and RedShift, you will play a pivotal role in our data infrastructure, ensuring efficient data integration, transformation, and storage. You will collaborate with cross-functional teams to design, implement, and maintain data pipelines that enable data-driven decision-making for our organization and clients. 50/50 hybrid flexibility.

Key Responsibilities:

• Data Pipeline Development: Design, develop, and maintain robust, scalable, and efficient data pipelines using AWS Glue, AWS Data Pipeline, or other relevant tools.

• ETL Processes: Build and optimize ETL (Extract, Transform, Load) processes to ingest, cleanse, transform, and store data from various sources into Amazon Redshift.

• Data Modeling: Create and maintain data models within Amazon Redshift, ensuring data accuracy, performance, and scalability.

• Performance Optimization: Monitor and optimize AWS Glue jobs and Amazon Redshift clusters for performance, scalability, and cost efficiency.

• Data Integration: Integrate data from diverse sources, including databases, APIs, and data lakes, into a unified data platform.

• Data Quality: Implement data quality checks and data validation processes to ensure the accuracy and integrity of data.

• Security: Implement and maintain data security measures, including access controls, encryption, and compliance with industry standards.

• Documentation: Maintain thorough documentation of data pipelines, ETL processes, and data models.

• Troubleshooting: Identify and resolve data-related issues, including data discrepancies, performance bottlenecks, and system failures.

• Collaboration: Collaborate with data analysts, data scientists, and other stakeholders to understand data requirements and deliver data solutions that meet their needs.

• AWS Expertise: Stay current with AWS technologies and best practices, applying them to enhance data engineering capabilities.