Gustavo Souza

At Argo Crew since January 2023, my journey as a Software Engineer and Data Engineer has been marked by pioneering innovations and relentless pursuit of efficiency in data engineering, especially within the real estate sector. I led a transformative project that drastically reduced the data ingestion timeline for millions of real estate records from a staggering 2-5 days to a mere 8-24 hours. This achievement was not just a testament to my ability to optimize codebases, but it also highlighted my strategic approach to leveraging Datadog for insightful performance metrics and refining Kafka's ingestion strategies to supercharge indexing and search capabilities. In tandem, I engineered a strategic overhaul of time buffering and record quantity management, fine-tuning Elasticsearch's mass ingestion API. This initiative substantially lessened the computing resource demand on Amazon Elastic Kubernetes Service (EKS), showcasing a blend of innovation with environmental consciousness. My role has continually involved pushing the boundaries of technology adoption, integrating advanced tools like TypeScript, Elasticsearch, Kafka, Datadog, and AWS. This not only addressed a significant scalability challenge but also set new benchmarks for data processing performance, ensuring the robustness and continuity of critical data systems. This phase of my career is a prelude to my aspirations of becoming a Customer Engineer, where I aim to apply my expertise in technology and data engineering to guide enterprise customers in maximizing their investments, especially in platforms like FlutterFlow. My ability to rapidly acquire new skills and set performance standards speaks to my commitment to professional growth and excellence.

• Engineered and maintained a sophisticated front-end and back-end architecture for Sorcero’s data ingestion platform using Python 3 and React, successfully processing both structured and unstructured datasets, which led to an increase in data processing efficiency, supported advanced NLP enrichment tasks and productivity by data scientist providing.
• Spearheaded the development of robust data ingestion pipelines on Google Cloud Platform (GCP) utilizing PySpark for NLP, LLMs RAG pipelines, significantly enhancing the platform's capability to handle large-scale biomedical literature with improved accuracy in data extraction by 95%.
• Played a pivotal role in troubleshooting and remediating complex issues within web applications and data pipelines, reducing downtime by 99% and enhancing system reliability for critical life sciences research and analysis.
• Innovatively designed and implemented advanced cloud-based solutions for structured and unstructured data management, leading to a 327% improvement in the scalability and performance of NLP, LLMs RAG pipelines compared to the previous major version, thus accelerating the delivery of actionable insights for biomedical research.