Mehul Ganjude

Architected and delivered scalable data engineering solutions across Azure and AWS ecosystems, driving analytics, automation, and ML capabilities to support strategic business initiatives. • Designed REST API pipelines with Azure Function Apps, Python, and Elastic Job Agents to ingest and transform data from systems like PestRoutes, NICE, ADP, Geotab, Dispatch, and Sales into Azure SQL for unified reporting. • Integrated Google Address Validation API for real-time address standardization and customer review automation, and Google Maps API to optimize technician routing, reducing travel time and costs. • Led migration from Azure Functions to Dagster, achieving 95%+ pipeline reliability via modular assets, partitions, and ops—enabling scalable orchestration and observability. • Engineered a NAS server health-check system with automated alerts, supporting IT-led archival migrations and saving the company millions in storage spend. • Optimized 50+ long-running SQL processes using execution plans, indexing, and query refactoring reducing execution times by 60% and enhancing Power BI refresh speed. • Migrated legacy SQL Server assets to Snowflake, implementing star/snowflake schemas and transferring 100+ tables, procedures, and triggers with full data integrity. • Created and automated 40+ stored procedures and scheduled jobs to support upstream Dagster workflows and ensure consistent data availability. • Delivered an end-to-end GA4 analytics pipeline using Python and Google APIs to surface marketing insights on traffic, sessions, campaigns, and events. • Built 30+ Logic Apps workflows for real-time executive reporting and automated dashboard refreshes. • Currently building ML models (Random Forest, CNN) to predict future branch expansion using geo-demographic and performance data. Tech Stack: AWS (Lambda, Redshift, ECS, S3), Microsoft Azure (Function Apps, Logic Apps, SQL Database), dagster, Python, Dagster, Snowflake, Power BI, Google APIs,TensorFlow, Git, CI/CD

• Led cross-functional teams in designing and implementing efficient data solutions, resulting in a 20% reduction in data processing time and improved scalability for future growth. • Engineered and fine-tuned data models in Hive, enhancing data analysis efficiency by 40% and reducing processing time by 50%, resulting in quicker decision-making processes for the analytics team. • Pioneered the utilization of MongoDB to streamline customer-behavioral data storage and management; facilitated rapid retrieval and analysis, resulting in a 35% enhanced in data processing efficiency. • Directed the strategic implementation of RDS and EC2 instances for auto-scaling and real-time data processing; delivered a 25% cost reduction and elevated data-driven decision-making capabilities through advanced analytics. • Engineered the configuration, monitoring, and resolution of issues within Oracle Golden Gate 19c for a global airline and hospitality platform, streamlining data replication processes and achieving a 50% reduction in downtime. • Managed the engineering and optimization of database configurations across diverse environments, culminating in a commendable 30% enhancement in query response times and a substantial 25% reduction in data transfer latency. • Led Performance Tuning projects to enhance system performance through effective Indexing, Query Optimization, and monitoring tools; achieved a 30% reduction in database query execution time, improving overall application speed and responsiveness. • Executed clustering strategies and database mirroring techniques to fortify disaster recovery capabilities, increasing system redundancy by 30% and safeguarding operational database services against potential disruptions. • Spearheaded the transformation of reporting packages like ORACLE procedures, Packages, Functions, Triggers, Views, and materialized Views; boosted operational efficiency by 35% and decreased data processing errors by 50%.

1) Actively supported the professor in creating, managing, and maintaining the UTA database. 2) Played a vital role in student projects involving database development, guiding them from project assignment to completion, helping and expertise at every step to ensure successful outcomes.

• Provided critical support for Oracle database production, including updates, fixes, migrations, backup/recovery, performance optimization, and cloning, enhancing system reliability and uptime. • Successfully installed and configured Oracle Enterprise Manager Cloud Control 12c and OEM Grid Control 11g/12c for comprehensive administration, performance tuning, monitoring, and alarm systems, improving database efficiency by 25%. • Managed ASM (Automatic Storage Management) for both RAC (Real Application Clusters) and standalone databases, optimizing disk and disk group management and improving storage utilization by 15%. • Spearheaded the implementation of data integration best practices, ensuring smooth data transitions from OLTP to OLAP and target systems, leading to a 30% progression in data quality and performance. • Architected and implemented Azure Data Factory pipelines, data flows, and Talend jobs, transforming data processing workflows into efficient, reusable ADF pipelines, increasing data transformation speed by 75%. • Led the design and deployment of Snowflake schemas for critical data warehousing projects, optimizing data organization and retrieval processes. Achieved a 25% improvement in query performance through the implementation of normalized schemas with dimension and fact tables. • Orchestrated the seamless integration of Snowflake data warehousing solutions with Microsoft Azure, harnessing Azure services like Azure Data Factory and Azure Databricks. • Conducted data migrations across databases utilizing Oracle 10g and 11g export/import utilities and Oracle 11g exp/imp data pump, ensuring data integrity and minimal downtime. • Executed multi-terabyte database migrations with low or zero downtime, facilitating seamless transitions and maintaining continuous business operations.

First-term (Fall 2021) Courses : Design & Analysis of Algorithms, Data Analysis and Modelling techniques, Data Mining Second term (Spring 2022) Courses : Distributed System, Computer Network, Machine Learning Third term (Summer 2022) Courses : Database Systems 2 , Advanced Database Systems-Cloud computing Fourth-term (Fall 2022) Courses : Artificial Intelligence, Software Testing 2, Database System 1