West New York, New Jersey, United States
I studied chemical engineering. I now build AI systems that solve real problems. I built a forecasting tool for a cafe chain after a barista told me they kept running out of bacon. I built a RAG system that reduced document lookup time by 80%. I built an observability framework that helps companies trust their AI agents. I do not just build tools. I solve problems. Open to roles in data engineering, AI engineering, or backend engineering. If you are building something that matters, let us talk. email [email protected] number 9084565420
• Architected a SaaS analytics pipeline on AWS processing 1M+ user events/month using Airflow for orchestration and S3 for data lake storage, enabling real-time product analytics. • Built a RAG document intelligence system ingesting 5,000+ PDF pages, achieving 95% Q&A accuracy using Python and vector search with sub-second query response times. • Designed Snowflake dimensional data models reducing average query time by 40%, improving dashboard performance across all business units. • Consulted for several clients • Established pipeline monitoring and alerting infrastructure on AWS CloudWatch, maintaining 99%+ uptime across all production data workflows.
● Architected a SaaS analytics pipeline on AWS processing 1M+ user events/month using Airflow for orchestration and S3 for data lake storage, enabling real-time product analytics. ● Implemented structured transformations to produce clean, analysis-ready datasets for business stakeholders ● Built a RAG document intelligence system ingesting 5,000+ PDF pages, achieving 95% Q&A accuracy using Python and vector search with sub-second query response times. ● Designed Snowflake dimensional data models reducing average query time by 40%, improving dashboard performance across all business units.
• Built and maintained Python/SQL ETL pipelines connecting freight, finance, and operations data sources to Snowflake, supporting 50+ enterprise clients at 99.9% uptime. • Deployed and managed containerized microservices on AWS (ECS, Lambda, S3) using Pulumi infrastructure-as-code, enabling reproducible and auditable cloud deployments. • Implemented dbt transformation layers for both analytical and operational Snowflake workloads, standardizing data models across engineering and business teams. • Automated recurring data validation and ingestion workflows using AWS Lambda and Python, reducing manual operations by 20% and freeing engineering capacity. • Collaborated across Agile sprint cycles with engineering, finance, and logistics stakeholders to define requirements and deliver data solutions on a two-week release cadence.
Built full-stack web applications using Next.js and React Contributed to team projects emphasizing clean code and maintainable architecture
• Led end-to-end data engineering engagements for startup clients, owning pipeline design, implementation, testing, and documentation with full delivery accountability. • Improved PostgreSQL query performance by 25% through strategic indexing, query plan analysis, and schema optimization across multiple client databases. • Reduced analytical query time by 40% by implementing Kimball-style dimensional data models, enabling faster reporting for business intelligence consumers. • Built reusable Python ETL frameworks with standardized error handling and logging, reducing onboarding time for new client engagements and improving pipeline reliability.
● Improved PostgreSQL query performance by 25% through strategic indexing, query plan analysis, and schema optimization across multiple client databases. ● Reduced analytical query time by 40% by implementing Kimball-style dimensional data models, enabling faster reporting for business intelligence consumers.