AWS Data Engineer

To accurately describe the key responsibilities for the AWS Data Engineer role, here's a breakdown:

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  Develop and Manage AWS Data Infrastructure: Utilize AWS services like LakeFormation, S3, Athena, Redshift, and Lambda to design, implement, and maintain data solutions, ensuring data accessibility, security, and integrity.
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  Data Integration and ETL: Leverage ETL tools and Python for data extraction, transformation, and loading processes, ensuring smooth integration of data across various platforms.
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  AI/ML Platform Integration: Work with Sagemaker to integrate AI and machine learning capabilities into the data platform, enabling predictive analytics and data-driven decision-making.
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  Configuration and Optimization: Be hands-on with Redshift for data storage and analytics, configuring environments to optimize performance and scalability for large datasets.
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  Automation and Scripting: Use tools like Docker and CloudFormation to automate infrastructure deployment and manage cloud resources efficiently.
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  Data Security and Privacy: Ensure adherence to data privacy regulations and handle confidential data, particularly in healthcare settings, with a focus on HIPAA compliance and secure data zones.
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  Collaboration and Documentation: Collaborate with cross-functional teams to understand data needs and document all data-related processes, architectures, and policies for future scalability and compliance.

MPI does not discriminate on the basis of race, color, religion, sex, sexual orientation, gender identity or expression, national origin, age, disability, veteran status, marital status, or based on an individual's status in any group or class protected by applicable federal, state or local law. MPI encourages applications from minorities, women, the disabled, protected veterans and all other qualified applicants.