At Peraton, I design Snowflake and AWS data pipelines for enterprise analytics and high-volume processing, using dbt, Snowpipe, Streams, Tasks, S3, Glue, Python, PySpark, and Airflow. I process more than 5 TB of data daily and built CDC and SCD Type 2 pipelines across datasets exceeding 100 million records.
I modernized more than 30 Oracle ODI and Informatica workflows into cloud-native architectures, improving processing performance by up to 50%. I also reduced AWS Glue compute costs by approximately $800 per month through Spark tuning, job consolidation, partition optimization, and unnecessary-processing elimination.
I build governed analytical datasets through modular dbt models, dimensional modeling, data-quality validation, reconciliation, lineage, and production observability. My work includes managing 45+ Airflow/MWAA DAGs with dependency handling, retries, SLA monitoring, CloudWatch alerting, and recovery patterns.
Earlier, at Parkview Health and Diligent Soft Tech, I improved ETL accuracy, reporting turnaround, dashboard usability, and data-access security across Oracle BI, ODI, PL/SQL, OBIEE, and Informatica environments. I enjoy partnering with architects, analysts, data scientists, ML engineers, and business stakeholders to turn requirements into dependable data products.
