Lakshmi Avula

Built a real-time market data streaming platform with Apache Kafka and Flink, reducing data ingestion latency by 60% and enabling real-time analytics for risk management and trading. Optimized low-latency financial transaction pipelines with Apache Ignite, supporting pricing engines and compliance with 99.99% uptime. Enhanced large-scale data processing speed threefold using Python tools and developed advanced SQL models cutting execution times by 45%. Modernized data infrastructure using AWS services to reduce operational costs by 30% while maintaining millisecond performance. Created an anomaly detection framework that improved trade surveillance and fraud detection by 50%, ensuring compliance with key regulatory standards.

Engineered high-performance data pipelines processing terabytes of telecom data daily using Apache Spark and Hadoop, enabling real-time network analytics and predictive insights. Collaborated with network and business teams to optimize ETL pipelines, reducing analytics latency by 40%. Built real-time streaming pipelines with Apache Kafka handling millions of events per second with sub-100ms latency for fraud detection and service personalization. Improved batch processing using Hive with partitioning and bucketing strategies that halved query times. Developed optimized SQL queries improving report generation by 45% and automated data validation scripts increasing data integrity by 35%. Integrated large datasets through Azure Data Factory and Synapse Analytics to speed ETL processing by 55%, supporting real-time BI dashboards for executives.

Engineered and maintained high-performance data pipelines handling TBs of telecom and customer usage data daily using Apache Spark and Hadoop, enabling real-time network analytics and predictive insights for service optimization. Partnered with network engineers, product analysts, and business teams to define technical requirements, optimize ETL pipelines, and design scalable data models, improving data accessibility and reducing analytics latency by 40%. Built real-time data streaming pipelines with Apache Kafka, processing millions of subscriber events per second with sub-100ms latency to support fraud detection, network traffic monitoring, and personalized service offerings. Enhanced batch processing using Apache Hive, implementing partitioning, bucketing, and indexing strategies that cut query execution times by 50%, supporting efficient reporting for compliance and operational teams. Wrote optimized SQL queries (CTEs, Window Functions, Dynamic SQL) for telecom-specific KPIs like d