Chandini Saisri Uppuganti

• Supported the development of an internal security platform for automated threat modeling and risk assessment workflows at Adobe. • Built backend automation to enhance security analysis with contextual data from enterprise sources. • Implemented AI-assisted SME agents to improve threat identification and recommended security controls. • Collaborated with security engineers on the design and documentation of production features.

• Developed a scalable and secure LLM-RAG application to enhance response accuracy. • Ensured high reliability and robust data privacy while focusing on enterprise-level security. • Contributed to the end-to-end development of a cloud-ready and modular solution adaptable to business needs.

Migrated large-scale data pipelines from on-prem to cloud platforms by developing automated Python scripts for data extraction, cleaning, transformation, and validation from multiple sources including SAP, Teradata, Oracle, and Snowflake. Converted legacy ETL workflows into scalable cloud-native pipelines, including rewriting complex Teradata SQL queries for Google BigQuery to optimize performance and cost-efficiency. Orchestrated and monitored workflows using Apache Airflow, ensuring reliable, scheduled execution of data pipelines with built-in retry logic, dependency management, and alerting. Implemented real-time data ingestion by integrating Apache Kafka to stream API data and load it into AWS Redshift using Kafka topics and Redshift staging mechanisms for low-latency analytics.

Designed, built, and maintained scalable ETL workflows & data pipelines using a 5-zone architecture on Azure Data Lake and Warehouse, enabling high-performance over structured/unstructured data and reducing processing latency by 30%. Gathered and Transformed data from diverse sources (REST/SOAP APIs, SAP ECC, Oracle, Snowflake); applied data quality checks (deduplication, nulls, formats), stored results in Parquet, csv, and designed Star & Snowflake Schema data models to optimize analytical querying in downstream systems. Stored historical data using SCD types, and performed joins and lookups; loaded the transformed data into Azure Synapse and developed complex SQL queries in the Application zone to support business reporting and analytics. Implemented a handshake mechanism with the application team to ensure data consistency, and automated end-to-end ETL pipelines using Informatica IICS, incorporating real-time alerts and regression testing to boost pipeline efficiency by 25% and reliability by 40%.

CMPE 272 - Enterprise Software Platforms CMPE 255 - Data Mining CMPE 256 - Advanced Data Mining CMPE 257 - Applied Machine Learning CMPE 258 - Deep Learning CMPE 202 - Systems Software Engineering CMPE 297 - Special Topics in AI CMPE 260 - Reinforcement Learning

Gained a robust foundation in key areas of the field. This includes in-depth knowledge of Database Management Systems (DBMS), C, Java and Python programming, Digital Logic Design, Data Structures and Algorithms (DSA), Machine Learning, and Data Warehousing. Through a combination of theoretical coursework and practical projects, I developed a strong skill set in designing efficient algorithms, managing and analyzing data, and applying machine learning techniques to solve complex problems.