I am a Senior Data Engineer and Data Architect with deep experience designing and optimizing large-scale data platforms and real-time analytics systems. My background spans finance, SaaS, and healthcare, where I have built resilient pipelines, governed data at scale, and improved operational reliability for mission-critical workloads.
At JP Morgan Chase & Co, I led the design and delivery of a financial data platform on AWS, building high-throughput pipelines, real-time streaming workflows, and governance controls that supported faster fraud detection and regulatory reporting. I worked extensively with Python, Java, SQL, Spark, Databricks, EMR, Glue, Kafka, and Airflow to process millions of transactions daily with strong reliability.
In my SaaS experience at BlueCore Innovations, I owned ETL and event-driven data services for marketing automation analytics. I helped modernize legacy workflows, improve reporting speed, strengthen data quality, and support better campaign targeting through scalable modeling and orchestration solutions.
Earlier in my career at NextTech Solutions, I built HIPAA-compliant ETL pipelines and secure data-processing systems for healthcare analytics. I focused on integrating claims, medical, and patient outcome data while maintaining compliance, access controls, and dependable reporting for stakeholders.
Across these roles, I have consistently delivered measurable impact by reducing processing time, improving uptime, lowering operational overhead, and enabling teams to make faster decisions with trusted data. I enjoy solving complex architecture problems and turning business requirements into scalable technical systems.
I hold a Master’s degree in Data Science and a Bachelor’s degree in Information Technology from the University of Utah. I am now looking to apply my cloud, big data, and architecture expertise to build high-impact data solutions in a challenging engineering environment.
Lead the design and delivery of a financial data platform, creating large-scale data pipelines, real-time streaming, ETL/ELT workflows, observability, and metadata governance on AWS. Built production systems using Python, Java, SQL, Spark, and related tools to process high-volume financial transactions, improve fraud detection, support regulatory reporting, and reduce downtime. Architected Spark and PySpark pipelines on Databricks, EMR, and Glue; designed Delta Lake architecture on AWS S3; built 300+ Airflow DAGs; developed Kafka and Spark Structured Streaming pipelines; and established coding standards and architectural practices.
Owned the design, build, and optimization of ETL pipelines, event-driven data services, and Snowflake modeling layers for a SaaS marketing automation platform. Delivered scalable solutions with Python, SQL, dbt, Airflow, Kafka, Spark, Snowflake, Docker, Terraform, and AWS. Orchestrated 50+ ETL/ELT pipelines, replaced legacy SSIS workloads, optimized reporting performance, managed Kafka clusters, built dbt transformations, automated CI/CD pipelines, and implemented secure access controls and data governance.
Engineered HIPAA-compliant ETL pipelines and a secure data-processing framework for a healthcare insurance analytics platform. Built production systems using Python, SQL, Flask APIs, Docker, Airflow, PostgreSQL, AWS, and Great Expectations. Developed ETL workflows for 10M+ healthcare records monthly, designed optimized PostgreSQL schemas, built secure APIs, containerized ETL operations, automated reporting and validation, improved data durability, and created healthcare analytics dashboards.
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