Role: AWS Solutions Architect
Location: Maryland
Duration: Long term
Candidate should be local to MD or VA
Job Description:
A. Data Architecture Design: Develop and maintain a robust data architecture strategy for the AWS
Cloud-based Lake House, encompassing data modeling, data storage, data processing, and data
integration across various AWS services, such as S3, Glue, Athena, and Redshift.
B. Cloud Data Governance: Define and enforce data governance principles, standards, and best practices
to ensure data quality, security, and compliance with relevant regulations.
C. Data Integration: Design and implement efficient data integration processes, enabling seamless data flow between diverse data sources and the data lake infrastructure. D. Performance Optimization: Collaborate with data engineers and technical teams to optimize the performance and scalability of the data lake architecture, ensuring high availability and low latency
for data access and processing.
E. Metadata Management: Implement metadata management solutions to enhance data discoverability,
lineage, and cataloging within the data lake house.
F. Data Security: Develop and enforce security measures to protect sensitive data, adhering to industry
best practices and AWS security standards.
G. Collaboration and Leadership: Work closely with cross-functional teams, including data analysts, data
scientists, and data engineers, to understand their data requirements and provide technical guidance on data-related initiatives. H. Data Architecture Documentation: Maintain comprehensive documentation of the data architecture, including architectural diagrams, design specifications, and data flow processes.
Cloud-based Lake House, encompassing data modeling, data storage, data processing, and data
integration across various AWS services, such as S3, Glue, Athena, and Redshift.
B. Cloud Data Governance: Define and enforce data governance principles, standards, and best practices
to ensure data quality, security, and compliance with relevant regulations.
C. Data Integration: Design and implement efficient data integration processes, enabling seamless data flow between diverse data sources and the data lake infrastructure. D. Performance Optimization: Collaborate with data engineers and technical teams to optimize the performance and scalability of the data lake architecture, ensuring high availability and low latency
for data access and processing.
E. Metadata Management: Implement metadata management solutions to enhance data discoverability,
lineage, and cataloging within the data lake house.
F. Data Security: Develop and enforce security measures to protect sensitive data, adhering to industry
best practices and AWS security standards.
G. Collaboration and Leadership: Work closely with cross-functional teams, including data analysts, data
scientists, and data engineers, to understand their data requirements and provide technical guidance on data-related initiatives. H. Data Architecture Documentation: Maintain comprehensive documentation of the data architecture, including architectural diagrams, design specifications, and data flow processes.
—
– –
