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• Developed custom Java-based scripts for automated ETL (Extract, Transform, Load) processes, integrating disparate financial data sources into a unified investment data warehouse. Utilized Java’s concurrent package for multithreading to parallelize data processing tasks, improving the performance of batch operations on large financial datasets by 60%.• Utilized AWS CloudFormation to provision and manage infrastructure as code, ensuring consistent and repeatable deployments across development, staging, and production environments. Implemented serverless architectures using AWS Lambda and API Gateway to handle dynamic workloads, reducing operational costs by 25%.• Designed and maintained microservices-based architectures with Spring Boot, deploying them in AWS using Lambda and API Gateway, ensuring system reliability and operational stability.• Integrated machine learning models using Weka and Apache Spark MLlib to predict financial market outcomes, enhancing decision support capabilities within the investment platform. Deployed Java-based microservices using Docker containers, orchestrating them with Kubernetes to ensure high availability and fault tolerance in a cloud-native environment.• Configured SLF4J and Log4j frameworks for granular application monitoring, enabling efficient debugging and error tracking in production systems. Refactored legacy Java codebases to adhere to industry coding standards, and introduced type safety with Java's generics, improving code maintainability and readability across the development team.• Developed search solutions using AWS, Elasticsearch, and OpenSearch, ensuring high performance and scalability for enterprise-level financial applications at MFS Investment Management.• Developed custom Apache Airflow hooks and operators to interact with Apache Kafka APIs, enabling advanced control over real-time financial data streaming, cluster management, and job execution.

• Developed real-time data ingestion solutions using Java, Spring Boot, and AWS Kinesis, creating robust Java applications to process and transform streaming financial data with minimal latency. Integrated AWS DynamoDB for low-latency time-series data management in financial systems.• Designed and implemented search solutions using AWS Elasticsearch/OpenSearch, optimizing search queries and improving search relevance and performance for large-scale financial datasets. Collaborated with data teams to integrate and index datasets efficiently.• Utilized AWS S3 and Glue for large-scale data storage, ETL operations, and real-time financial data processing. Deployed AWS Lambda functions in Java to manage high-throughput data streams, enhancing real-time reporting with asynchronous processing.• Developed and deployed microservices using Spring Boot and Docker containers, ensuring scalability and reliability. Utilized Kubernetes to orchestrate deployments and manage containerized financial applications in a highly available cloud-native environment.• Created SQL scripts and cron jobs to automate time-series data aggregation and reporting, ensuring accurate and timely financial metrics. Troubleshot SQL Server performance issues, improving query execution times and data integrity.• Automated CI/CD pipelines using Jenkins and Git, enabling seamless deployment of Java applications across environments. Employed Git branching strategies like feature branching and pull requests to manage parallel development.

• Developed and maintained high-performance search solutions using AWS, Elasticsearch, and OpenSearch, ensuring optimal search relevance, scalability, and system reliability for financial applications at Goldman Sachs.• Optimized search queries to improve search relevance and performance, collaborating with data teams to integrate and index large datasets from various sources.• Designed and managed the deployment of API systems using Spring Boot (Java), integrating both REST and SOAP web services for efficient data exchange across financial services.• Implemented security and compliance measures for search data, leveraging AWS IAM and VPC for secure infrastructure deployment and ensuring regulatory compliance with financial data standards.• Hands-on experience with Docker, Kubernetes, and Cloud Foundry for containerized deployment, providing scalable and resilient environments to handle high traffic and large datasets.• Utilized Jenkins for building robust CI/CD pipelines, integrated with Git for version control, and deployed applications on AWS EC2 and RDS, ensuring smooth and automated release cycles using IntelliJ for development.• Developed Spring Boot applications for microservices architecture, ensuring system stability, reliability, and optimized performance in a large corporate environment.• Monitored and enhanced search infrastructure for availability and scalability using AWS CloudWatch and Elasticsearch cluster monitoring tools, ensuring real-time insights and performance tracking under heavy loads.