Gopikrishna Nallappareddi

• Architected real-time data processing pipeline using PySpark Structured Streaming and Delta Lake, achieving under 2 minutes data latency.
• Spearheaded migration from legacy Hadoop infrastructure to cloud-native Databricks Lakehouse, decreasing costs by 42% and enhancing job reliability to 99.7%.
• Led cross-functional team of 8 engineers in deploying ML-powered anomaly detection on 15TB of transaction data, uncovering $3.2M in potential fraud during first quarter.

• Refactored PySpark code and implemented dynamic partition pruning, reducing daily processing time by 68% and saving over 230 compute hours monthly.
• Deployed a metadata-driven framework for data quality validation, automatically detecting schema drift and integrity issues across 200 datasets.
• Collaborated with data scientists to productionize ML models, decreasing deployment time from weeks to two days while achieving 99.5% prediction accuracy.

• Developed reusable PySpark components for data transformation, standardizing code quality across six project teams.
• Resolved performance bottlenecks in Spark SQL queries, enhancing job completion times by 45%.
• Contributed to internal PySpark training program, onboarding 12 junior developers and reducing ramp-up time by 40%.

AML Cards & Optimization is a compliance project handling all credit card transactions, both retail and consumer. The main goal is to detect fraudulent transactions and generate alerts for such transactions over a data set of about 400 GB per month for the USA and Canada alone. The project is divided into two parts.

• Segmentation (12-month historical data is provided to analysts.)
• Transaction monitoring (alerts are generated on 12 months, and recurring feed data). This is a rule-based alert generation model.
• Assisted in Agile adoption within the Big Data & Analytics team.
• Led daily stand-ups and sprint planning for the development and analytics team.
• Coordinated across multiple teams to deliver data-driven insights and solutions.
• Facilitated Agile retrospectives to improve collaboration and remove blockers for development teams.

• Developed Big Data Solutions that enabled the business and technology teams to make data-driven decisions..
• Led Agile adoption within the development team to enhance sprint efficiency.
• Implemented daily workflow automation for extraction, processing, and analysis of large data sets.

Citi QA UAT Support - This application handles all the trading procedures and takes the data from various source systems. After that, it processes and stores the data in different databases. These systems transmit data to the next level.

• Handling all the trade-related issues while trades are moving from EDTS/COMET (FO) to ZTS (MO).
• Sending daily checkouts mail to business users for all the tools that are being used in the trading life cycle.
• Automation program developed for morning checkouts.
• Server maintenance.

Citi Equities DMOS Application Support.

• Involved in the requirement study.
• Developing components for the Middle Tier as per various business rules.
• Designing of User interface.
• Testing of Modules and Code review.
• Performance Monitoring (CPU, Memory, Paging, Network Latency, HTTP Response)

The Defence Research and Development Organisation (DRDO) is an agency under the Department of Defence Research and Development in the Ministry of Defence of the Government of India, charged with military research and development, headquartered in Delhi, India.

• Programming in PL/SQL in both Linux and Windows platforms.
• Software Configuration activities.

Contributed in maintenance of hardware in Loop Simulation activities. Prepare the documents as per requirements.