Golang Developer

Developed a software solution to analyze racial disparities in voting wait times across the US, utilizing geospatial data from millions of phone records, showcasing expertise in advanced geospatial analysis techniques. Architected and optimized scalable ETL pipelines capable of processing over 30 TB of data, leveraging Airflow, Kafka, and AWS, while reducing cloud compute costs by 80% through efficient pipeline design and resource management. Designed and implemented geospatial analysis workflows, including preprocessing, feature engineering, and clustering, using Terraform for infrastructure automation to ensure robust, reproducible pipelines. Integrated advanced query algorithms, such as Chen et al. and cluster-based optimizations, to derive actionable insights and ensure high-accuracy analysis of large-scale datasets. Spearheaded cloud infrastructure improvements by implementing cost-efficient solutions and automation, demonstrating a strong command of modern data engineering tools and practices.

Designed and implemented an arbitrage scanner and automation tools using Go, enabling efficient identification and execution of profitable opportunities by leveraging price differences across multiple asset exchanges. Processed and analyzed large datasets periodically from various exchanges, utilizing robust and efficient data pipelines to ensure high-speed, reliable data ingestion and transformation. Developed AWS Lambda functions to parse and process extensive exchange data, optimizing performance by triggering them via EventBridge for event-driven architecture across various exchanges. Configured and deployed a cloud proxy within the VPC, enabling seamless communication with numerous exchanges while maintaining secure and efficient connectivity. Monitored and managed infrastructure in AWS CloudWatch, overseeing the performance and health of Krak AI’s systems to ensure consistent reliability and uptime. Implemented notification systems using AWS SNS and Lambda to automatically alert the reliability team of service downtimes, reducing response times and improving overall system availability. Rewrote configuration reloading logic at application startup, reducing startup time by 80%, significantly improving the deployment and runtime efficiency of backend services.

Designed and implemented backend solutions and microservices using Golang, significantly enhancing system performance and user experience, while integrating observability tools like Grafana Stack, Elasticsearch, and Prometheus for real-time monitoring and logging. Developed and automated a CI/CD pipeline for seamless deployment of backend features, leveraging Jenkins, which reduced deployment time by 40% and improved overall development efficiency. Built and maintained cloud infrastructure-as-code using Terraform, standardizing cloud services, improving reliability, and streamlining infrastructure management for staging and production environments. Engineered a high-performance video streaming backend leveraging Golang, Jitsi, Redis, Kubernetes, and Google Cloud services, efficiently handling over 300,000 requests per hour, ensuring scalability and reliability under high traffic. Managed DevOps infrastructure across staging and production environments, implementing robust pipelines and ensuring seamless integration, delivery, and operation of scalable backend systems.

Integrated observability with OpenTelemetry using Golang, enabling real-time monitoring and traceability across backend services. Implemented data streaming solutions with Kafka, delivering high-throughput, low-latency pipelines to ensure critical data reached the AI analysis team in near real-time. Designed and maintained communication systems using RabbitMQ for reliable messaging between microservices, ensuring seamless integration and collaboration across backend services. Developed and executed comprehensive test suites for backend features and functionalities, achieving high code quality and minimizing bugs in production. Collaborated with the AI teams to align backend development with analytical goals, ensuring that backend solutions supported advanced machine learning models and analytics.