Jyosna Reddy

About Jyosna Reddy

Results-driven Data Engineer with 5+ years of experience in designing, building, and optimizing scalable data pipelines, ETL processes, and real-time analytics solutions for financial services and IT consulting domains. Expertise in Azure Synapse, Apache Spark, Kafka, and Airflow, delivering high-performance data workflows to drive business intelligence. Skilled in SQL, Python, and R, optimizing structured and unstructured datasets for advanced analytics and predictive modeling. Experienced in data quality assurance, compliance, and governance, ensuring adherence to GDPR, SOX, and financial regulations. Adept at Tableau-based reporting for actionable insights, enhancing executive decision-making. Proficient in containerized deployments, DevOps automation, and CI/CD pipelines, ensuring seamless data integration. Passionate about leveraging big data technologies to enhance operational efficiency, reduce costs, and support business innovation. Collaborative team player with a strong ability to align data initiatives with organizational goals, ensuring business success.

Experience

1. Data Engineer

   Capital One

   Jun 2024 — Present · US

   Developed and deployed Azure-based data pipelines using Azure Synapse, Data Factory, and Data Lake Storage, optimizing financial data processing by 45%, improving regulatory compliance, and reducing operational bottlenecks for fraud detection systems. Implemented high-throughput real-time streaming using Apache Kafka, minimizing latency in transaction monitoring and improving risk analysis accuracy by 30%, enhancing fraud prevention and security compliance strategies. Designed scalable ETL workflows using Azure Databricks and Apache Spark, reducing data processing costs by 35% while increasing the speed of financial reports and analytics dashboards used by executive teams. Integrated Tableau dashboards with Azure Synapse, enabling interactive financial data visualizations that improved risk assessment insights and enhanced financial forecasting accuracy across various business units. Automated anomaly detection with PySpark and Airflow, ensuring data integrity and consistency by identifying irregularities in financial transactions, reducing fraudulent activities and operational risks significantly. Optimized SQL-based queries in Azure Synapse, reducing query execution time by 50%, accelerating financial reporting workflows, and enabling data scientists to conduct complex analyses more efficiently. Implemented Azure RBAC and security policies, ensuring strict access control and encryption, aligning with SOX, GDPR, and PCI DSS regulations to protect sensitive financial and customer data. Developed predictive modeling pipelines using machine learning techniques, improving credit risk analysis accuracy and reducing loan default rates by 20%, supporting data-driven decision-making for loan approvals. Orchestrated Kubernetes-based containerized ETL workflows, ensuring seamless scaling, high availability, fault tolerance, minimizing data pipeline downtime, and increasing operational efficiency.

Education

• University At Buffalo

  Master of Science, Data Science

• JNTUH College of Engineering Hyderabad

  Bachelor's degree, Bachelor of Technology: Electronics and Communication Engineering