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Hema Tungala
Mid-Level Full-Stack Software Engineer specializing in FinTech and cloud-native microservices
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About
Full-stack engineer with fintech/trading domain experience (Fidelity) and startup SaaS CRM/billing platform work (Zoho), building real-time portfolio analytics and trade-processing systems. Strong in microservices, event-driven architectures (Kafka/WebSockets), and AWS/Kubernetes operations with measurable performance gains (~34–35% latency reduction) and maintainability improvements (~40% faster deployments). Targeting a founding full-stack engineer role in NYC with meaningful equity.
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Key Strengths
- Owned end-to-end real-time portfolio analytics dashboard (React/TS + Spring Boot microservices) from build through deployment/ops
- Real-time systems expertise (WebSockets + Kafka event-driven updates) for live trade/portfolio data
- Performance optimization with measurable impact (Redis caching + Postgres tuning/indexing; ~34–35% faster data retrieval/API response)
- Production observability and reliability practices (Prometheus/Grafana, CloudWatch, ELK; monitoring latency/error rates/throughput)
- Zero-downtime delivery and environment parity (blue-green deployments, Terraform IaC, dev/staging/prod)
- Scalable architecture and maintainability improvements (refactored tightly coupled system into domain microservices; standardized DTOs/contracts; ~40% faster deployments)
- Data pipeline correctness under flaky upstreams (idempotency via transaction IDs/offset logic, retries, backfills, reconciliation jobs, alerting)
- High-ambiguity startup execution (shipped MVP in ~3 weeks by designing extensible plugin-based billing module)
- Owned and shipped real-time portfolio performance dashboard end-to-end (design → build → deploy → iterate)
- Designed streaming architecture with Kafka + Redis caching to balance real-time accuracy vs infrastructure cost
- Performance optimization based on user feedback (composite indexes, query tuning, lazy loading) reducing load time ~35%
- Built production RAG system for portfolio insights using embeddings + Pinecone + GPT-4 with citations and confidence scoring
- Implemented LLM guardrails (context validation, prompt sanitization, fallback responses, content moderation, audit logging)
- Designed LLM evaluation loop with human review dashboard; improved agreement with human graders ~18% via rubric-focused retrieval/prompting
- Built scalable ingestion pipeline for messy multi-format trade data (CSV/JSON/Kafka) with canonical Avro schema + schema registry
- Implemented reliability/observability for data pipelines (exactly-once/idempotency, Prometheus/Grafana, ELK); reduced AWS compute cost ~24% while keeping latency <2s
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