• Defined and led the implementation of a secure multi-cloud ingestion architecture bridging Azure clinical systems with AWS analytics, establishing a HIPAA-compliant platform serving 1M+ residents with 99.9% reliability
• Standardized Terraform-based infrastructure patterns across networking, IAM, and data services to eliminate configuration drift, enforce security baselines, and enable repeatable, auditable multi-environment deployments
• Designed a Snowflake-centric ELT architecture leveraging AWS Glue and Snowpipe to consolidate 20M+ fragmented health records, reducing query latency by 40% and enabling near real-time statewide analytics
• Built CI/CD pipelines using GitHub Actions and Terraform to replace manual deployments, enabling zero-downtime schema evolution while improving release reliability and deployment velocity
• Implemented platform-wide security controls using IAM, VPC isolation, KMS encryption, and CloudWatch observability to enforce least-privilege access, reducing audit exceptions by 35% and improving incident MTTR by 25%
• Partnered with analytics leadership to define a GenAI readiness roadmap by establishing governed, metadata-rich data foundations supporting future RAG and LLM analytics in HIPAA-regulated environments

• Architected and governed 25+ production-grade pipelines integrating 12+ disparate healthcare data domains, processing 3–5TB monthly to enable scalable enterprise analytics across claims, member, and provider ecosystems
• Built Delta Lake–based ELT frameworks to address schema drift and incremental processing challenges, enabling reliable analytics consumption across 7+ downstream claims, member, and provider reporting teams
• Orchestrated batch workflows using Airflow with idempotent DAGs and automated backfills to eliminate manual reruns, reducing operational intervention by 40% and improving data consistency across recurring workloads
• Re-engineered Spark workloads through partition strategy redesign, caching optimization, and resource allocation tuning, reducing processing latency by 35% and accelerating new data source onboarding by 25%

• Built a serverless ingestion pipeline using AWS SES, Lambda, S3, and Python-based validation logic to replace manual registration processing, reducing 200K+ annual submissions from hours to under five minutes while improving data quality controls
• Designed and maintained a 40+ table MySQL data warehouse using structured ETL workflows and optimized SQL transformations, centralizing academic and administrative datasets for 500+ stakeholders and improving reporting accuracy and consistency
• Automated registration and payment workflows using Lambda, API Gateway, and Python-driven REST integrations to eliminate 70% of manual effort, enabling reliable execution of 250+ conference sessions during a five-day event
• Scaled event-driven ingestion and analytics pipelines for a national academic conference, mentoring 10+ graduate students on AWS serverless and data engineering best practices while supporting reliable operations and approximately $120K in annual revenue

• Engineered large-scale batch and streaming pipelines using Spark, Hive, and Airflow to process healthcare provider and claims datasets, improving data availability and reliability for enterprise analytics teams
• Led the migration of enterprise analytics workloads from on-prem Hadoop to AWS-managed services, redesigning processing architectures to improve platform reliability and increase operational efficiency by 15% through elastic infrastructure adoption
• Automated repetitive DataOps tasks using Python-based frameworks, generating $250K+ in annual operational savings and earning a High Impact Award for improving delivery timelines and platform stability
• Implemented early-stage GenAI automation to enhance pipeline observability and reduce manual intervention, improving reliability across distributed data processing workflows

Coursework: Distributed Systems, Advanced Cloud Computing Architectures, Database & Query Optimization