Professional Support Technologies

Key Features

• ETL: gathered data from multiple sources and systems into a centralized data lake, ready to be consumed by different departments.

• DevOps: designed and automated infrastructure pipelines for downstream data integration on AWS.

• ML: trained models on historical and daily-updated data; deployed models for real-time and batch predictions.

• BI: built dashboards for different departments to visualize data and model predictions.

• Monitoring: set up monitoring and alerting for the entire infrastructure and applications.

• Security: ensured data and applications met security and compliance requirements.

• Automated the full model lifecycle — training, testing, and deployment — using CI/CD pipelines.

Results

• KPIs generated and published to AWS QuickSight, providing real-time visibility across sales, marketing, and operations.

• Designed and implemented a robust ETL pipeline to extract, transform, and load data from multiple sources including CRM, ERP, IoT systems, and APIs.

• Consolidated all structured and unstructured data into an AWS S3-based data lake.

• Developed and trained machine learning models using historical and daily-updated data for accurate forecasting and anomaly detection.

• Deployed models using AWS SageMaker, enabling real-time predictions and batch processing.

• Created interactive dashboards and reports using QuickSight, providing insights for sales, marketing, and operations teams.