Computer Science Graduate from Stevens Institute of Technology. I work on back-end technologies building smart solutions to problems using solutions that are efficient and scalable.
Developed ETL/ELT pipelines using spark to aggregate data and perform cross-sectional analysis working within data volumes exceeding 1 Billion items daily.
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Leveraged scientific testing methodologies to optimize Cassandra cluster performance, fine-tuning JVM configurations and Linux system resources to consistently meet stringent SLA requirements, including minimizing long-tail latencies for high throughput applications.
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Architected and implemented a versatile aggregation framework capable of handling both real time and batch data data patterns, enabling dynamic feature variable testing and generic data serving through client configured metadata, significantly reducing developer involvement for new output requirements.
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Improved Kafka Consumption rate by a factor of 10 utilizing asynchronous processing and reducing network calls to the broker.
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Reduced existing Apache Spark container resource requirements by 75% by better utilizing data partitioning and memory usage.
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Modernized data pipeline infrastructure using AWS, Kubernetes, and Terraform saving the firm $20MM/year.
Lead the planning, development of a React Web Application, Node API, supporting database, and CI/CD pipelines allowing clients to configure multiple plugins and manage security resulting in team OKR goals being completed.
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Developed automated tooling using Python to create containerized test environments, set up local developer environments, and fill datastores based on mock production data for consistent setup and reproducibility.
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Created an automated release bot that integrated with github and AWS lambda to provide instant cloud environment deployments and more quickly reflect developer progress.
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Developed a node microservice using Typescript that served as an engineering template for future microservices by explicitly thinking about the engineering requirements around Test Driven Development (TDD), observability, and code standards.
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Optimized cloud infrastructure monitoring costs by ensuring critical application data is surfaced while reducing excess data noise adding to overages.
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Effectively onboarded multiple engineers onto ongoing Django and Node projects using a combination of Pair Programming and effective Engineering process documentation.
Developed a Content Management System (CMS) for a variety of required documents related to NLU model training, so that product owners can create customizable experiences for end users. The CMS data can be easily moved between deployed and local environments across multiple tenants and sub-applications.
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Worked to develop and manage an end to end model training, evaluation, and packaging data pipeline that can be run as a Jenkins job or AWS Batch to support multiple parallel processes with a variety of parameters.
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Developed a metrics API that supports concurrent evaluations of running reports accessible via a unique identifier, so that product owners can effectively measure the effectiveness of models without the involvement of knowledge engineers.
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Aided in the development of multiple other REST API's, ensuring they complied by design standards.
Worked primarily on the front-end displaying 6800+ stock options in an elegant, yet effective UI/UX for clients through a variety of formats and features. (e.g. Advanced Search Tool, Custom Portfolio, Industry, Region, Sector). In the back-end I worked on database management using PHP, C++, and JS.
