Delivered automated ETL orchestration via Airflow for large-scale dataset ingestion, transformation, and fraud risk reporting, reducing manual review overhead through monitoring, alerting, and standardized data-quality checks.

Designed AI inference pipeline on Snowflake and Airflow for real-time facial biometric verification, enabling high-volume fraud risk analysis. Built fraud detection platform with FastAPI microservices, RBAC, audit logging, and data quality validation.

Enabled analytics-ready datasets, dashboards, and advanced analysis for clients. Built RAG-based document retrieval and anomaly detection workflows, reducing equipment downtime by 30%.

Architected ELT pipelines on Snowflake with dbt, developed Airflow DAGs, integrated Claude and OpenAI APIs, and automated ETL on Azure Data Factory and BigQuery with Prefect.

Delivered trustworthy datasets for reporting and analysis, improved transparency into data quality and pipeline health for stakeholders.

Built serverless ETL pipelines on AWS (Lambda, S3, EC2) with Python, achieving 97% customer satisfaction. Enforced data governance with IAM, encryption, and observability dashboards.