United States
DevOps specialist delivering scalable ETL/ELT pipelines (Python, SQL, Spark, Airflow) and cloud-native architectures (AWS/Azure/GCP), with deep experience in CI/CD, Docker, Kubernetes, and Terraform, driving high-availability, low-latency, and production-grade data platforms.
. Designed wireframes and executed rigorous testing of CRM systems using Visio, decreasing tax credit calculation errors by 20% through targeted process refinements. • Adapted responsive web interfaces using React.js, HTML5, and CSS3, optimizing UI performance and cross-browser compatibility. • Streamlined project workflows through Microsoft Azure DevOps, cutting fiscal year project delivery times, enabling better resource allocation and cost management, and saving up to $500K in grant fund allocation. • Created visually aesthetic dashboards in PowerBI to visualize SQL-based analyses, enabling stakeholders and management to track KPIs and make data-driven decisions. • Maintained CI/CD pipelines in Azure DevOps, integrating smoke, sanity, and regression test scripts to ensure seamless deployment cycles with managed and unmanaged solutions. • Acted as the primary liaison for user support, resolving 95% of CRM-related issues under tight deadlines.
• Strategically integrated Adobe Marketo Engage within the EOSS department at ASU to initiate precise, targeted email marketing campaigns and utilize AWS cloud solutions for robust data management, resulting in a significant 10% uplift in student engagement rates with our programs. • Analyzing data validation and cleaning for academic records, leveraging Python and Alteryx for student data gathering, reduced data errors drastically over a period of 3 months. • Developed an ETL pipeline (Azure) for the EOSS department, executing data extraction with MySQL Workbench from Amazon Redshift and Google Sheets. Optimized data for analysis with Alteryx, enhancing student data consolidation from multiple locations in Arizona, contributing to a 15% increase in engagement. • Engineered a robust PostgreSQL data warehouse, orchestrating the collection, integration, and upkeep of student data and facilitating intricate analysis of student retention and success metrics within the ASU ecosystem. • Deployed Tableau dashboards that showcased key performance indicators in student services and highlighted participation and program impact, which enabled data-driven decision-making among the EOSS leadership team.
• Deployed Apache Kafka for real-time analytics at a major cement manufacturer (JINDAL), integrating POS, production, and inventory data to achieve a 12% improvement in inventory turnover and an 8% boost in distributor engagement, enhancing supply chain efficiency and demand forecasting. • Designed a Tableau dashboard that consolidated inventory data across multiple warehouses and informed the respected management on inventory levels in real-time for a retail client, contributing to a 70% faster inventory turnover rate by optimizing stock levels. • Engineered complex MySQL queries and automated ETL workflows using SSIS for a manufacturing client's raw materials planning platform, resulting in a 15% increase in data fidelity and a more efficient inventory tracking system, are instrumental in optimizing their supply chain operations. • Utilized logistic regression in R and predictive analytics with Python to analyze consumer behavior and sales data at Forest Essentials. These insights forecasted key market trends, contributing to revenue growth at 4 locations: Goa, Rajkot, Udaipur, and Jaipur, through targeted marketing and strategic sales initiatives. • Collaborated with cross-functional teams in an 8-week sprint to develop a cloud-based inventory tracking system, employing Agile methodologies to deliver the project on schedule, leading to operational efficiency and a faster delivery time, which helped our company get future work from the same client. • Architected a Python-based ETL data pipeline for a boutique IT consultancy, optimizing the auto-transfer of vast data arrays into AWS cloud storage and achieving a measurable 25% cut in overall data handling duration.