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Akhilesh Reddy

Data Engineer @State Street

Cape Girardeau, MO, US
MOBILE NUMBERS
+91 *********19

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WORK HISTORY

Jan 2025 — Present

Data Engineer @State Street

US

Architected high-performance cloud-based data lake platforms on AWS using Databricks, Amazon S3, and Delta Lake, integrated with Hadoop and HDFS, enabling large-scale data modeling and regulatory analytics for structured products, fixed income, and ESG datasets. Designed and deployed low-latency data ingestion and fraud detection workflows leveraging Apache Kafka, Spark Streaming, and AWS Lambda, reducing false-positive alerts by 15% in structured finance and derivatives transaction monitoring. Managed and optimized distributed NoSQL and relational databases (MySQL, Amazon DynamoDB, MongoDB, Amazon RDS) with robust access control, data lineage, metadata management, and adherence to SOX, CCAR, SEC, and FINRA compliance standards. Engineered and fine-tuned ETL pipelines using Apache Spark, Apache Airflow, and AWS Glue to process 500GB+ of financial, credit, and risk data daily, reducing batch latency by 40% to enable real-time market intelligence and credit risk analytics. Applied statistical modeling and anomaly detection with Python (Pandas, Scikit-learn), AWS Athena, and Amazon SageMaker, improving credit default prediction accuracy and supporting Basel CCAR, and Dodd-Frank compliance. Led data warehouse migrations from SQL Server and Oracle to Amazon RDS and Amazon Redshift, achieving 40% faster query performance and 25% cost reduction, enabling cloud-native scalable analytics. Delivered interactive BI dashboards and automated reporting pipelines using PySpark, Hive, Amazon Athena, and Tableau, providing near real-time insights into ratings performance, asset exposure, and operational risk for executive stakeholders.

EDUCATION

2023 — 2025

Southeast Missouri State University

Master's degree

2018 — 2022

Vidya Jyothi Institute Of Technology

Bachelor of Technology - BTech

ABOUT AKHILESH REDDY

I am a Data Engineer with 4+ years of experience designing scalable data pipelines…

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