United States
Software Engineer with 3+ years of experience designing and building scalable data and analytics systems on AWS. Proven expertise in developing cloud-native data pipelines using Python, SQL, Snowflake, Redshift, Airflow, and DBT to support batch and near-real-time analytics. Strong foundation in data modeling, workflow orchestration, CI/CD, and containerized deployments. Experienced in building reliable, testable, and cost-efficient analytics platforms enabling self-service BI and data-driven decision-making
• Designed scalable ELT pipelines using Snowflake, AWS Glue, and Python to solve large-volume healthcare ingestion challenges resulting in 40% faster batch processing. • Implemented DBT models for analytics-ready fact and dimension tables to solve inconsistent reporting logic resulting in standardized self-service BI datasets. • Developed Apache Airflow workflows to solve unreliable pipeline scheduling resulting in automated retries, monitoring, and SLA compliance. • Optimized Snowflake queries and storage strategies to solve high compute costs resulting in a 25% reduction in warehouse spend. • Integrated Kafka streaming pipelines into analytics layers to solve delayed event visibility resulting in near-real-time dashboards. • Applied data quality checks and CI/CD workflows to solve production data defects resulting in improved pipeline reliability.
• Engineered AWS-based ETL pipelines to solve high-latency insurance data processing resulting in 30% faster analytics queries. • Modeled dimensional data warehouses in Snowflake and Redshift to solve inefficient reporting schemas resulting in improved BI performance. • Automated Kafka ingestion and Spark processing to solve delayed claims analytics resulting in near-real-time insights. • Implemented Airflow orchestration to solve manual pipeline coordination resulting in consistent daily analytics delivery. • Enforced data validation and testing rules to solve data quality issues resulting in trusted downstream analytics. • Supported CI/CD pipelines using GitHub Actions to solve manual deployments resulting in improved engineering velocity