Senior GCP Data Engineer at The Texas Farm Bureau Group

Architected and deployed scalable multi-cloud data platforms on GCP and Azure processing multi-terabyte datasets for enterprise underwriting and risk assessment, supporting 1M+ policy evaluations annually. Engineered real-time streaming pipelines using Kafka and GCP Pub/Sub delivering sub-second underwriting insights, cutting decision latency by 60%. Optimized BigQuery and Snowflake workloads via partitioning, clustering, and materialized views, achieving 35% faster queries and $50K annual cost savings. Developed ML pipelines with MLflow and BigQuery ML to improve underwriting risk scoring by 35%, reducing claim losses by 20%. Containerized 50+ data workloads with Docker and deployed on Kubernetes, automating infrastructure with Terraform to reduce deployment time by 40% and eliminate drift. Implemented CI/CD with Jenkins and GitHub Actions; designed REST APIs and event-driven microservices; orchestrated workflows with Airflow/Cloud Composer; ensured SOC2 compliance.

Senior Data Engineer at M&T Bank

Built enterprise-scale multi-cloud data platforms across AWS and GCP to support personalized banking solutions for 10M+ customers. Developed distributed Spark and PySpark pipelines processing 3TB+ daily transaction data with 99.95% data quality. Implemented real-time ingestion pipelines using Kafka, AWS Kinesis, and GCP Pub/Sub handling 500K events/second with sub-100ms latency. Designed and optimized Snowflake, BigQuery, and Redshift warehouses reducing query runtimes by 35% through partitioning and clustering. Engineered ML pipelines with Spark MLlib and Databricks for customer segmentation and churn prediction achieving 92% model accuracy. Built 100+ REST APIs and event-driven microservices using FastAPI; containerized pipelines with Docker and Kubernetes reducing infrastructure costs by 30%. Implemented CI/CD pipelines with Jenkins, GitHub Actions, and Azure DevOps automating 150+ data workflows. Delivered Power BI and Tableau dashboards; automated data acquisition reducing manual

Data Engineer at State of Texas

Designed hybrid cloud architectures across AWS and Azure supporting Texas Public Services Portal serving 5M+ citizens. Built end-to-end ETL pipelines using AWS Glue, Azure Data Factory, and PySpark processing 2TB+ daily government datasets. Implemented Delta Lake on Azure Databricks enabling auditable, versioned datasets for compliance and regulatory reporting. Developed ML pipelines using Azure ML and AWS SageMaker for predictive analytics improving service delivery efficiency by 25%. Designed REST APIs and event-driven microservices for secure data services with OAuth2/JWT. Containerized ETL workloads using Docker and AKS achieving 99.8% availability. Automated infrastructure provisioning using Terraform and CloudFormation reducing manual deployment by 60%. Integrated analytics with Power BI dashboards providing real-time executive reporting.