Data Engineer and Data Scientist

Designed an AI inference pipeline using Snowflake and Apache Airflow for real-time facial biometric verification. The solution processed 512-dimensional embeddings at approximately 50 ms per image and achieved 86–90% precision for fraud-risk workflows.

Built a Snowflake-backed fraud detection platform with FastAPI microservices for multi-criteria risk scoring. Implemented RBAC, audit logging, data-quality validation, ETL orchestration, monitoring, and alerting to support regulatory compliance and reduce manual review effort.

Architected Snowflake ELT pipelines with dbt for data modeling, transformation, analytics-ready datasets, and machine-learning feature stores for multiple clients. Developed production Apache Airflow DAGs for automated ingestion from REST APIs, SQL databases, and flat files.

Integrated Claude and OpenAI APIs into Snowflake-orchestrated workflows to build RAG-based document retrieval and classification applications. Also automated ETL workloads with Azure Data Factory, BigQuery, and Prefect, and delivered LSTM/Prophet anomaly detection on IoT data that reduced equipment downtime by 30%.

Built serverless ETL pipelines on AWS using Lambda, S3, EC2, and Python to support reliable cloud data delivery. Achieved 97% customer satisfaction through SLA adherence and dependable pipeline operations.

Enforced data governance across cloud pipelines through IAM-based access control, column-level encryption, and observability dashboards. Monitored pipeline health and strengthened security and operational reliability.

Built Python microservices and SQL data models supporting automated ETL workflows. Developed integrations that enabled data processing and automated service operations.

Deployed serverless AWS Lambda functions for machine-learning inference and cloud service integrations. Supported scalable cloud-based data workflows through Python and AWS services.

Optimized database architecture and stored procedures to improve the reliability and performance of operational reporting systems.

Established data-quality standards that reduced inconsistencies across reporting data and improved confidence in operational information.

Performed database optimization work for institutional artificial-intelligence projects. Improved data structures and supported quality-oriented data practices.

Ensured adherence to quality standards across project data workflows, contributing to more reliable institutional AI initiatives.

GPA: 4.0/5.0. Member of the AI Research Group. Certifications include Python for Data Scientists, Machine Learning & Advanced Analytics, CCNA Enterprise Networking, and Introduction to IoT.