RAHUL BOBBA

Full Stack Java Developer

FINRA

Boston, USA

01.2021 - Current

Architected and implemented Java-based ETL pipelines to ingest high-volume market data (price, volume, SIP feeds) and external unstructured data sources (Dow Jones, Factiva news, SEC EDGAR filings).
Designed data normalization, enrichment, and correlation layers to transform raw news and filings into issue-level, surveillance-ready datasets consumed by fraud detection engines.
Built scalable ingestion and processing workflows supporting daily batch surveillance execution with robust error handling, retries, and idempotent processing.
Implemented data quality checks and validation frameworks (completeness, freshness, schema validation) to ensure accurate inputs for regulatory surveillance logic.
Developed full-stack Java services and APIs to expose processed datasets to downstream surveillance systems and analytics consumers.
Optimized ETL performance and throughput to handle large data volumes while meeting strict SLAs for daily production surveillance runs.
Provided production support and root-cause analysis for surveillance failures, data delays, and ingestion issues in highly regulated financial systems.
Built enterprise-grade REST/microservices using Java 8/11/17, Spring Boot, JPA, Kafka, and AWS.
Designed multi-threaded and high-volume processing applications ensuring resilient performance.
Implemented secure authentication with OAuth2/JWT and Spring Security.
Built Spark/EMR ETL pipelines integrating S3 → EMR → Aurora for financial reporting.
Architected microservices deployed via Docker/Kubernetes and automated CI/CD using Jenkins & GitHub Actions.
Created and optimized batch jobs with Spring Batch for large data ingestion pipelines.
Automated builds using Maven/Gradle, integrating with Jenkins for CI/CD pipelines.
Containerized Java applications with Docker and deployed on Kubernetes clusters for scalability.
Designed and implemented multi-threaded applications for high-volume transaction processing, ensuring thread-safety and reducing processing time.
Configured AWS infrastructure including EC2 instances, ECS clusters, IAM roles, and networking policies to ensure secure, scalable, and resilient deployments across multiple environments.
Implemented data engineering pipelines using AWS EMR, leveraging PySpark and Scala to build multi-step ETL workflows. These workflows extracted high-volume datasets from S3, transformed them using Spark SQL, and delivered outputs into Aurora RDS for structured reporting and downstream analytics.
Developed AWS Lambda functions for serverless, event-driven workloads such as file ingestion, data validation, and automated error handling.
Contributed to proof-of-concept projects evaluating serverless architectures with Lambda, SQS, and S3 for cost optimization.