Senior Java/Python Backend Engineer with React Frontend
Resume:
Sai Ayyapusetty
**************@*****.*** 301-***-**** www.linkedin.com/in/ayyapusetty-sai
Professional Summary:
With 7+ years of IT experience involving all phases of Software Development Life Cycle (SDLC) including planning, analysis, design, implementation, testing and maintenance of multi-Tier distributed, secure enterprise applications using Java and Python.
Proficient in various Spring modules, including Spring Core, Spring ORM, Spring Boot, and Spring Security, and experienced in developing microservices using Spring Boot with RESTful APIs.
Proven expertise building microservice-based backend architectures that are scalable, fault-tolerant, and aligned with modern RESTful design principles.
Skilled in PostgreSQL schema design, query optimization, and integrating data-intensive financial systems.
Strong command of frontend development with ReactJS, implementing reusable UI components, hooks, and state management to deliver responsive, high-performance interfaces.
Experienced in CI/CD pipelines with Jenkins, GitLab, Docker, and AWS CodeDeploy for automated build, test, and cloud deployments.
Experienced in big data processing using Apache Spark, developing batch and real-time data pipelines with Spark SQL and Structured Streaming to process large-scale financial datasets efficiently.
Solid understanding of software design patterns, unit testing (JUnit/Mockito), and Agile collaboration with UX, QA, and DevOps teams to ensure performance, quality, and maintainability.
Technical Skills:
Programming Languages
Java, Python, Node JS, C
Web Technologies
React JS, AngularJS, HTML, DHTML, JavaScript, jQuery, CSS, AJAX, DOJO, XML, Web Services (REST, WSDL).
Frameworks
Hibernate, JPA, Spring (IOC, AOP, MVC, Boot, ORM, Dependency Injection)
Database Environments
MongoDB, Cassandra, CockroachDB,Oracle Sql,, MySQL
Web/Application Servers
Web Logic, Apache Tomcat, JBOSS
Webservice Technologies
REST, GraphQL
Cloud Technologies
AWS, Azure
Operating Systems
Windows (All Variants) UNIX
Messaging Services
Rabbit MQ, Active MQ, Apache Kafka
Version Control Tools
GitLab, GIT, Bit Bucket, CVS, SVN
Professional Experience:
Client: Freddie Mac, Mclean VA. Sep 2024 – Present
Role: Sr Full Stack Developer
Project Summary:
Working on financial applications designed for risk analytics, portfolio management, and capital forecasting. These applications process large-scale financial data, execute credit risk models, and provide real-time scenario analysis for investment decisions and regulatory compliance. The system integrates various financial services, pricing models, and data workflows to deliver data-driven insights for business strategies.
Responsibilities:
Developed and maintained Java-based applications supporting financial risk models, capital forecasting, and loan valuation calculations across distributed systems
Designed and optimized RESTful APIs using Java and Spring Boot for secure, high-throughput data exchange between risk analytics services and external financial modelling platforms
Implemented event-driven and streaming architectures using Kafka on AWS MSK for real-time data transformations, aggregations, windowed operations, and enrichment of financial datasets
Built observability and monitoring dashboards using Splunk, custom Python log parsers, CloudWatch metrics, and JVM diagnostics to track service availability, API latency, error trends, and memory utilization
Designed UI prototypes and application skeletons using AngularJS, HTML5, CSS, and DHTML, and developed responsive web interfaces for risk and loan valuation workflows.
Developed Apache Spark batch processing jobs using Spark SQL and DataFrames to process and transform large mortgage datasets stored in Amazon S3.
Enforced authentication and authorization across microservices using Spring Security, JWT, and OAuth2, securing internal analyst access and system-to-system integrations via REST and JDBC
Designed and implemented Oracle PL/SQL stored procedures, functions, and triggers to support risk calculations, capital forecasting logic, and large-scale batch processing
Built Python-based ETL utilities to cleanse, transform, and validate large mortgage datasets before ingestion into AWS RDS, Aurora, and MongoDB, improving pipeline reliability and performance.
Designed and optimized ETL pipelines to move structured and semi-structured data from relational databases into Amazon S3 and Glacier for large-scale processing and long-term archival.
Integrated Angular Router for navigation across modules, secured routes with guards, and optimized state management using Angular services.
Integrated Kafka producers and consumers with Spring Boot microservices and downstream systems including S3, RDS, and DynamoDB to support batch and streaming ingestion use cases
Leveraged AWS Lambda for data transformation, aggregation, and scheduled processing tasks, reducing manual batch workloads and improving turnaround times by approximately 40%
Troubleshot production performance issues by correlating logs and metrics across Kafka, application services, and databases, significantly improving triage and root-cause analysis efficiency
Wrote and tuned complex SQL and PL/SQL queries to extract, validate, and reconcile large datasets used in credit risk models and capital forecasting dashboards
Managed Jenkins-based CI/CD pipelines, automating builds, unit tests, Docker image creation, vulnerability scans, and deployments to AWS environments
Implemented Spark batch jobs to transform, cleanse, and aggregate structured and semi-structured datasets before loading into AWS S3 and RDS.
Implemented fault-tolerant Spark pipelines with checkpointing and structured streaming to ensure reliability of mortgage processing workflows.
Optimized Docker images using multi-stage builds and lightweight base images, reducing image size and accelerating deployments to EC2 and EKS.
Built scalable Spark ETL pipelines to cleanse, enrich, and aggregate financial datasets before loading them into AWS RDS and MongoDB.
Enhanced API reliability by implementing centralized exception handling, interceptors, and validation layers within Spring MVC and Spring Boot services
Deployed microservices on AWS EC2 and Kubernetes clusters, integrating CloudWatch alarms and health checks to monitor system performance and availability.
Configured API Gateway and VPC networking to securely expose services, manage traffic routing, and control access between internal and external systems
Implemented Spark Structured Streaming pipelines integrated with Kafka to process real-time mortgage transaction data and perform windowed aggregations.
Implemented infrastructure using AWS CloudFormation and Terraform to provision EC2 instances, VPCs, security groups, MSK clusters, and database resources consistently across environment.
Developed unit and component test cases using Jasmine and Karma to validate AngularJS controllers, UI workflows, form validations, and business rules
Automated financial report generation using Python, scheduling jobs to aggregate risk metrics and deliver reports to business stakeholders
Collaborated closely with product owners, risk analysts, and QA teams to gather requirements, validate functional and non-functional behaviour, and resolve issues across development, QA, and production environments
Environment: Java 8/11, Spring (Core, Security, JDBC), Angular 14, RESTful APIs, MongoDB, AWS, Spark, Jenkins, GitLab, MongoDB Compass, Oracle PL/SQL, Apache Tomcat, Apache HTTP Server
Client: E-Trade, CA May 2022 – Aug 2024
Role: Sr Full Stack Developer
Project Summary:
The goal of the project was to improve the functionality and user experience of E Trade application system. The scope encompassed both back and frontend enhancements, including optimizing database queries, improving frontend responsiveness, and implementing server less computing for scalability.
Responsibilities:
Designed and implemented event-driven microservices using Java and SpringBoot, enabling seamless communication between loan-valuation and risk-analytics services. Designed and implemented event-driven microservices using Java and SpringBoot, enabling seamless communication between loan-valuation and risk-analytics services
Developed and enhanced SQL stored procedures and functions to handle portfolio management and trading data in CockroachDB
Used GraphQL to aggregate and orchestrate data from multiple Spring Boot microservices, reducing over-fetching and minimizing client-side API calls
Built responsive, user-friendly web interfaces using HTML5, JavaScript, jQuery, AJAX, and DataTables for dynamic data presentation
Implemented Azure App Services and Azure Functions for serverless workloads, supporting event-driven financial operations
Implemented Splunk dashboards to monitor end-to-end performance of trading and portfolio-management microservices, tracking API latency, order-processing times, and system throughput during peak trading hours
Implemented asynchronous programming patterns using Promises and async/await to efficiently manage concurrent API calls and real-time data updates
Supported integration of Hadoop-processed data with Kafka, Cassandra, and CockroachDB for hybrid batch and streaming use cases
Tuned Kafka consumer performance by optimizing poll intervals, batch sizes, commit strategies, and concurrency to reduce lag and improve throughput
Integrated CockroachDB for distributed transactional workloads, improving horizontal scalability and fault tolerance
Designed and implemented robust server-side applications using Spring MVC, ensuring high performance and responsiveness to frontend requests
Built interactive dashboards and data visualizations using React with D3.js and Recharts, enabling analysts to derive insights from complex financial data
Configured Azure API Management to securely expose REST and GraphQL endpoints, enforcing throttling, authentication, and access control
Developed Spark batch and streaming applications to process high-volume trade transactions and market data feeds with low latency
Integrated Hibernate and JPA for ORM-based data access with CockroachDB, managing complex entity relationships and transactional consistency
Leveraged Python with Pandas and NumPy to preprocess market and trade data for real-time analytics dashboards built in React
Used Java 8 features such as Lambda expressions, Stream API, and functional interfaces to improve performance of bulk data operations
Integrated Kafka pipelines with CockroachDB for real-time ingestion of streaming data, ensuring scalability and fault tolerance
Worked extensively with GraphQL schemas, resolvers, and queries to support UI-driven data requirements for trading and portfolio dashboards
Containerized Django and React applications using Docker and managed deployments through Kubernetes for consistency across environments
Deployed Spark jobs on AWS EMR clusters and configured resource allocation, executor memory, and parallelism settings to ensure efficient cluster utilization
Developed Spark Structured Streaming applications integrated with Kafka to process real-time market data streams with low latency.
Created custom reusable React component libraries and leveraged jQuery plugins for drag-and-drop, widgets, menus, and form handling
Implemented automated testing frameworks including JUnit and Mockito to ensure application quality and reliability within CI/CD pipelines
Designed and implemented real-time streaming applications using Kafka Streams, enabling complex data processing and analytics
Configured RabbitMQ message brokers for inter-service communication between Spring Boot microservices and Node.js event processors, improving decoupling and reliability
Deployed applications through Jenkins-based CI environments to support continuous integration and development testing
Worked with Kubernetes to define deployments, services, and scaling strategies for containerized applications in cloud environments
Configured Git integration with Jenkins CI pipelines and automated containerization workflows using Ansible and Kubernetes
Automated builds and deployments using Azure DevOps pipelines, integrating unit tests, code quality checks, and container image publishing
Implemented Python-based monitoring utilities to track Azure Functions and Kafka pipelines, generating alerts and performance metrics
Performed capacity planning and load testing on Kafka clusters to handle peak financial transaction volumes
Environment: Java,GraphQL, Spring Boot, Microservices, ReactJS, REST, Hibernate, Kafka, RXJS, HTML5, CSS3, JavaScript, SQL, Cassandra, DOM, Docker, Jenkins, Azure, EJB, IntelliJ, oracle, Agile and Windows, swagger
Pixel Dream, India – (Client – ICANOTES) June 2018 – July 2021
Role: Full stack developer
Responsibilities:
Worked on Angular and Java to develop end-to-end modules for the loyalty and rewards platform, implementing RESTful APIs on the backend and dynamic dashboards on the frontend for seamless user interaction.
Utilized Angular Router for navigation and route-level authorization between user, campaign, and admin modules.
Optimized JavaScript performance by implementing lazy loading, code splitting, and minimizing DOM manipulation for faster rendering of trading dashboards
Created Python data-ingestion modules to feed healthcare claim events into Cassandra and PostgreSQL, streamlining large-batch processing.
Developed the application using Spring MVC, Spring Web flow and added asynchronous features using AJAX.
Developed Spring Data Cassandra repositories for seamless integration with Spring Boot microservices, reducing query latency.
Created Python data-ingestion modules to feed healthcare claim events into Cassandra and PostgreSQL, streamlining large-batch processing.
Developed Angular components, services, and modules to manage campaign creation, reward tracking, and user interactions efficiently
Optimized SQL/PLSQL execution plans to reduce query latency and improve batch job performance.
Worked on the JAVA Collections API for handling the data objects between the business layers and the front end.
Developed the presentation layer written using JSP, HTML, CSS and client-side validations were done using JavaScript, and jQuery and Angular JS.
Utilized AWS EC2 and S3 to host services and manage object storage, achieving cost-efficient cloud integration.
Deployed Angular builds through Jenkins pipelines and hosted static assets on AWS S3 for scalable and reliable access.
Implemented extensive JavaScript for creating the Global templates that can be used across the JSP Pages. Produced client-side validation through JavaScript and Ajax for asynchronous communication.
Applied J2EE design patterns such as MVC, Singleton, and Data Access Objects in the architecture and used JDBC as DAO class to persist the data on to Oracle Database.
Implemented time-series data storage in Cassandra for audit logs and event tracking, supporting efficient queries on billions of records.
Collaborated in CI/CD pipelines to automate AngularJS build and deployment processes through Jenkins and AWS S3 hosting.
Performed unit testing using JUnit, ensuring robustness and minimizing production defects.
Developed Python scripts for asynchronous job scheduling to support daily batch reconciliations and real-time claims reporting
Enhanced system performance by tuning long-running PL/SQL queries and indexing strategies.
Environment: Java, JSP, Java Script, AJAX, AngularJS, PL/SQL, Maven, Servlet, EJB, Agile, MVC, JSON, JDBC, Oracle 8i, JUnit, CSS, Web logic server, Eclipse.
Education:
Master’s degree – University of Maryland Baltimore County
Bachelor's degree – GITAM University
Contact this candidate