Bal Reddy

• Designed and implemented microservices and event-driven architectures using Java (Spring Boot) and Python (FastAPI), supporting ultra-high transactional throughput exceeding millions of events per second.
• Developed scalable, distributed systems with sub-millisecond latency for real-time transaction processing, optimized for horizontal scaling in cloud-native environments.
• Created multithreaded applications in Java and C++ to streamline logistics data pipelines, resulting in significant cost savings and improved delivery efficiency.
• Led the architectural modernization of legacy systems, reducing latency by 20% and increasing overall system reliability and maintainability.
• Deployed containerized services using Kubernetes, Amazon ECS, and App Mesh, with infrastructure managed through Terraform and CI/CD pipelines powered by Jenkins.
• Built serverless applications utilizing AWS Lambda and API Gateway, resulting in modular, low-maintenance, and cost-efficient solutions.
• Designed and maintained large-scale ETL pipelines using AWS Glue and Amazon Redshift, enabling advanced analytics capabilities such as demand forecasting and inventory optimization.
• Implemented real-time data streaming pipelines with Apache Kafka and Apache Spark, capable of processing billions of records daily, significantly enhancing supply chain responsiveness.
• Integrated AWS Glue, Redshift, and Athena to enable near real-time analytics over curated datasets, improving decision-making speed and accuracy.
• Leveraged Amazon S3 and Athena for scalable data lake architectures, reducing data processing costs by over 50%.
• Tuned SQL (PostgreSQL) and NoSQL (DynamoDB) databases to support high-throughput, low-latency data ingestion and querying.
• Automated infrastructure provisioning and deployment using Infrastructure-as-Code (Terraform) and continuous integration via Jenkins, ensuring rapid and reliable software releases.
• Advocated for Test-Driven Development (TDD) and established CI/CD best practices, peer code reviews, and standardized development workflows across engineering teams.
• Effectively communicated complex architectural decisions to both technical and non-technical stakeholders, ensuring alignment between engineering initiatives and business objectives.

•Led the end-to-end development of a microservices-based inventory and routing platform leveraging Java (Spring Boot), deployed on AWS ECS with PostgreSQL as the primary relational datastore.

• Architected resilient, event-driven systems utilizing Apache Kafka and AWS EventBridge to ensure high availability, fault tolerance, and uninterrupted service delivery.
• Designed and exposed RESTful APIs to facilitate seamless inter-service communication and enable robust integration with external stakeholders.
• Implemented a real-time shipment tracking solution using AWS Lambda, SQS, and DynamoDB, enabling stateful event processing and real-time operational visibility.
• Orchestrated cloud-native, scalable infrastructure on AWS, integrating services such as Lambda, ECS, DynamoDB, S3, and Redshift Spectrum to optimize performance and cost-efficiency.
• Enhanced DynamoDB read throughput by implementing advanced partitioning strategies and caching layers, including Amazon DAX, to support low-latency data access at scale.
• Applied Test-Driven Development (TDD) methodologies to maintain high code reliability, ensure robust test coverage, and support long-term scalability of microservices.
• Built and managed CI/CD pipelines using Jenkins and AWS CodePipeline, automating testing, code quality enforcement, and deployment workflows across multiple environments.
• Utilized Git for source control and actively contributed to Agile ceremonies including daily standups, sprint planning, and peer code reviews to foster collaboration and velocity.
• Developed large-scale data pipelines using Apache Spark and Hadoop, enabling both near real-time data processing and high-volume batch analytics.
• Optimized analytical workloads by integrating Redshift Spectrum to query compressed and partitioned data in Amazon S3, resulting in a 3× performance improvement and 50% cost reduction.
• Engineered high-performance NoSQL data models optimized for low-latency and high-throughput operations while ensuring strong consistency under concurrent access.
• Improved overall system throughput by leveraging caching solutions (Redis/Memcached), fine-tuning indexing strategies, thread pool configurations, and implementing intelligent load balancing.
• Performed rigorous AWS infrastructure tuning based on cloud performance optimization best practices, ensuring reliable low-latency delivery across real-time applications.