Enterprise DLP Platform | Backend, SRE & Data Processing

I contribute to the ongoing development of an enterprise Data Loss Prevention (DLP) platform across backend engineering, distributed systems reliability, content processing, and observability. This case study intentionally omits employer, customer, and internal implementation details.

Contribute to system design for backend services, including service boundaries, Redis caching, and scalable content-processing pipelines across Java and Go microservices.

Build and maintain Java/Spring Boot and Go services supporting content processing, detection, compliance, and platform capabilities.

Design end-to-end observability using OpenTelemetry, OpenTelemetry Collector, Prometheus, Grafana, Jaeger, and Tempo.

Implement metrics, distributed tracing, RED metrics, dashboards, and SRE alerting for service health and production behavior.

Establish trace correlation across services with W3C Trace Context and traceparent propagation.

Investigate production issues using logs, metrics, traces, and application behavior; tune performance across latency, CPU, memory, throughput, error rates, and downstream dependencies.

Evaluate telemetry sampling approaches to retain critical error and slow traces while managing ingestion volume, memory use, and storage needs.

Earlier work on the same product included designing a detection-engine content-processing pipeline with AWS S3, Redis, and multi-parser orchestration. I integrated Apache Tika, KeyView, and Tesseract OCR to extract content and metadata from complex file types, and supported data classification and compliance workflows.

Current exploration also includes Generative AI and RAG capabilities with Spring AI.

Java, Spring Boot, Go, Kafka, Redis, AWS, Apache Tika, KeyView, Tesseract OCR, OpenTelemetry, Prometheus, Grafana, Jaeger, Tempo, JMeter, New Relic, and REST APIs.