Bhagya Sunkara

About

Backend/data engineering candidate with Netflix experience designing and migrating analytics platforms from batch to real-time streaming (Kafka/Flink) across AWS and GCP. Delivered measurable improvements (40% lower data delay, 99.9% accuracy) using phased rollouts, automated data validation (Great Expectations), and strong observability (Prometheus/Grafana), and proactively hardened pipelines with idempotency to prevent duplicate Kafka processing.

Experience

Education

Key Strengths

• Designed real-time streaming analytics backend (Node/Express + Flink) on AWS/GCP
• Reduced data delays by 40%
• Improved data accuracy to 99.9%
• Led batch-to-streaming migration using phased parallel rollout to manage risk
• Strong data integrity practices (Great Expectations validation, mismatch scripts, backups/versioning)
• Production observability and incident readiness (Prometheus/Grafana monitoring, alerting, rollback)
• API reliability and security in FastAPI (pytest testing, JWT/OAuth2, Pydantic validation, CORS)
• Identified Kafka consumer duplicate-processing failure mode; implemented idempotency to prevent double counting
• End-to-end full-stack delivery (React frontend, Node/Express APIs, AWS deployment/ops)
• Production observability and incident/performance monitoring (Prometheus/Grafana, CloudWatch)
• Designing scalable web app architecture with Postgres, migrations (Flyway), and performance tuning (indexing, pooling, caching)
• High-ambiguity execution: shipped real-time collaboration using WebSockets and JWT without detailed specs
• Reliability patterns for integrations/pipelines (retries/exponential backoff, backfills, reconciliation, alerting)
• Maintainability improvements via modular React components and backend abstractions; reduced bugs and improved onboarding speed