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Schedule Interview NowMy name is Sujay M. and I have over 1 years of experience in the tech industry. I specialize in the following technologies: Microsoft Azure, PostgreSQL, Snowflake, Microsoft SQL Server, Apache Kafka, etc.. Some of the notable projects I’ve worked on include: Fiber Broadband Networking Switches and Routers Virtualization, Predictive Reallocation of Underutilized Routers, ETL historical network utilization data into Snowflake with Airflow, Performance optimization of Snowflake Data Ingestion Pipeline, Network Bandwidth Utilization Dashboards, etc.. I am based in Cumming, United States. I've successfully completed 14 projects while developing at Softaims.
I specialize in architecting and developing scalable, distributed systems that handle high demands and complex information flows. My focus is on building fault-tolerant infrastructure using modern cloud practices and modular patterns. I excel at diagnosing and resolving intricate concurrency and scaling issues across large platforms.
Collaboration is central to my success; I enjoy working with fellow technical experts and product managers to define clear technical roadmaps. This structured approach allows the team at Softaims to consistently deliver high-availability solutions that can easily adapt to exponential growth.
I maintain a proactive approach to security and performance, treating them as integral components of the design process, not as afterthoughts. My ultimate goal is to build the foundational technology that powers client success and innovation.
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Snowflake
Eliminated multi-million-dollar Alcatel-Lucent switches and routers from AT&T wire centers by virtualizing networking software using Azure Kubernetes and Reserved VMs. This initiative reduced capital and maintenance costs, improved resource efficiency, enhanced scalability, and delivered substantial long-term financial benefits to AT&T. Tech Stack: Golang, Docker, Microsoft Azure Kubernetes, Azure Reserved and Spot Instance VMs, Virtual Network Functions (VNF)
Developed a predictive recommendation algorithm that automated the reallocation of underutilized network routers, optimizing resource management and reducing overutilization. This solution cut down the planning time from over a week to real-time decision-making, ensured accurate bandwidth forecasting, and prevented service disruptions by maintaining customer bandwidth agreements without requiring manual intervention from onsite planners.
Designed and implemented Apache Airflow DAGs to automate hourly preprocessing and ingestion of 100+ GB of network utilization data from Azure Blob Storage into Snowflake. Optimized data pipelines to reduce load times by over 85%, leveraging parallel processing with restructured 1 GB file segments for enhanced efficiency.
Developed an Azure Function to monitor Blob Storage for new files, triggering Airflow pipelines. Designed a pipeline to split 100GB network data files into sub-1GB segments, compress them to <400MB using gzip, and streamline data handling. Optimized Snowflake ingestion via COPY INTO with parallel processing, reducing data upload time from 8+ hours to under 1 hour.
Developed Grafana dashboards to visualize near-real-time network bandwidth utilization using Snowflake. Configured alerts to notify stakeholders on key performance indicators, enabling proactive network management. Enhanced data visualization through customized dashboard features, improving stakeholder engagement and decision-making.