Senior Solution Architect
Leading data and machine learning solutions for AWS customers, from design to deployment, focusing on scalability and operational efficiency.
Launched on:
See it live →
Overview
As a Senior Solution Architect at AWS, I worked directly with customers to implement strategic data and machine learning initiatives, focusing on cloud database, analytics, and machine learning solutions that are scalable and efficient.
Role Highlights
- Data Management Platforms: Built and maintained data management platforms using AWS services such as Amazon Elastic MapReduce (EMR), Amazon Redshift, Amazon Kinesis, and Amazon S3.
- Machine Learning Solutions: Worked with Amazon Machine Learning services (including SageMaker) to build scalable machine learning models and analytics solutions.
- Data Lake to Data Mesh: Led enterprise customer migrations from monolithic data lakes to distributed data mesh architectures.
- Service Team Collaboration: Collaborated with AWS service teams (e.g., S3, Glue, Rekognition) to improve services, resolve issues, and implement customer-requested features.
- DataOps Leadership: Led the creation of the DataOps domain in AWS’s Data Lab, coordinating an international team to develop thought leadership content and implement DataOps architectures.
Publishing and Contributions
- AWS Blog Post: Published a guide on Landing Data from Databases to a Data Lake at Scale using AWS Glue, along with open-source code.
- AWS Glue Service Contributions: Helped develop and test AWS Glue’s integration with Apache Hudi 0.12.0, and contributed to the marketplace product Apache Hudi Connector 0.10.1 for AWS Glue.
Results
- Accelerated client migrations to data mesh architectures, improving data processing speeds and scalability.
- Improved data pipeline efficiency by 40%, reducing data delivery times and operational costs.
- Increased machine learning model accuracy for several clients, resulting in better business intelligence and decision-making.
Challenges
- Large-scale Data Management: Managing data at scale across multiple AWS services while ensuring operational efficiency.
- Complex Client Requirements: Meeting diverse customer needs for scalable, high-performance data solutions.
Conclusion
My work at AWS allowed me to accelerate customer success in architecture, development, and deployment of cloud data solutions. Moving forward, I continue to help clients leverage AWS services for better data management and machine learning implementation.