Maniteja Bonagani
Data Engineer @Citi
Signup · Get unlimited contacts
WORK HISTORY
Data Engineer @Citi
Led the design and optimization of real-time data pipelines leveraging AWS cloud services. Developed real-time fraud detection systems, integrating transactional data from multiple sources to enhance risk assessment and compliance reporting. Additionally, contributed to healthcare analytics projects, enabling predictive insights for patient outcomes using AWS big data technologies and machine learning models.Key Achievements:• Architected and maintained AWS Glue-based ETL workflows processing 50M+ records daily, ensuring efficient real-time data ingestion, transformation, and storage for financial risk analytics.• Designed and deployed real-time streaming pipelines using AWS Kinesis and AWS Lambda, reducing fraud detection latency by 35% through immediate transaction anomaly detection.• Implemented Amazon Redshift for high-performance querying of financial datasets, improving query execution time by 50% with optimized distribution keys and sorting strategies.• Developed serverless data processing workflows using AWS Lambda and Step Functions, reducing operational overhead and improving scalability.• Built a real-time data lake architecture on Amazon S3, enabling schema-on-read analytics for structured and semi-structured data.• Integrated machine learning fraud detection models into production pipelines using AWS SageMaker, improving real-time risk assessment accuracy by 25%.• Automated compliance reporting for GDPR, HIPAA, and PCI-DSS using AWS Glue Data Catalog and AWS Lake Formation, ensuring 99.9% data accuracy and audit readiness.• Led the migration of legacy ETL pipelines to AWS Glue and Amazon Redshift, reducing batch processing time by 40% and cutting cloud costs by 30%.• Designed near real-time dashboards in Amazon QuickSight for financial analysts, enabling faster tracking of trade volumes, fraud alerts, and market trends.
EDUCATION
University of New Haven
Master of Science - MS, Data Science
KAKATIYA INSTITUTE OF TECHNOLOGY AND SCIENCE
Bachelor's Degree, Information Technology
ABOUT MANITEJA BONAGANI
With a Master’s degree in Data Science from the University of New Haven and a strong foundation in Information Technology, I have always been driven by the power of data to solve real-world challenges. My academic journey allowed me to explore machine learning, big data processing, and cloud computing, leading to multiple research projects focused on predictive analytics and data-driven decision-making. Passionate about leveraging AI and data science, I have developed solutions that optimize efficiency and drive actionable insights across industries.Professionally, I have over five years of experience in designing and optimizing large-scale data architectures across finance, healthcare, and environmental analytics. At Metiz Technologies, I led the development of real-time fraud detection systems, streamlining financial risk analytics using AWS. During my tenure at Infosys, I built scalable data solutions for healthcare clients, enhancing clinical decision-making with Azure-based analytics. My work at INCOIS involved real-time marine data processing, utilizing cloud and big data technologies to improve maritime safety. Through these roles, I have gained expertise in data engineering, cloud platforms (AWS & Azure), and big data frameworks like Apache Spark, Hadoop, and Snowflake.Looking ahead, my goal is to build next-generation AI-driven data solutions that enhance operational efficiency, drive innovation, and support critical business decisions. I am particularly interested in advancing real-time data processing and predictive analytics for finance and healthcare, ensuring seamless, data-driven transformations. I am always eager to collaborate, innovate, and contribute to impactful projects that push the boundaries of data engineering and AI. Let’s connect and explore the possibilities!
This profile is compiled from publicly available professional sources. Unifers is not affiliated with or endorsed by LinkedIn. Request removal of this profile.