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deannos/README.md

Amish Jha

Software Engineer | Backend & Systems Architecture | Cloud/DevOps

I build reliable backends, scalable event-driven pipelines, and developer-friendly tools. Focused on clean architecture, performance, observability, and production readiness.

LinkedIn • GitHub • LeetCode • CodeChef • Email

About

  • Backend engineering, systems, and cloud-native deployments
  • Strong focus on performance, API design, observability, and developer experience
  • Comfortable owning work end-to-end: design → implementation → deployment → monitoring
  • Problem-solving mindset: algorithmic rigor with real-world tradeoffs

What I Bring to a Team

Backend and Systems Engineering

  • API design: REST, pagination/filtering, versioning, predictable contracts
  • Performance: batching, pooling, concurrency (workers/channels), profiling
  • Reliability: timeouts, retries/backoff, idempotency, DLQs, backpressure
  • Data layer: schema design, indexing, query tuning, transactions, migrations
  • Event-driven systems: Kafka ingestion patterns, producer/consumer fundamentals

Production and DevOps Ownership

  • Production mindset: structured logs, metrics-first thinking, SLO awareness
  • Observability: Prometheus metrics, actionable instrumentation, debugging support
  • Cloud-native workflow: Dockerized services, env-based config, local reproducibility
  • Deployment discipline: CI checks, automated builds, safe rollouts, health checks
  • Operations: graceful shutdowns, resource limits, incident-ready execution

Code Quality and Architecture

  • Separation of concerns: domain/service/repo/handler boundaries
  • Testing: unit + integration, contract checks where useful, pragmatic TDD
  • Maintainability: composable modules, consistent naming, clean structure
  • Security basics: input validation, least privilege, safe defaults, rate limiting

Collaboration and Execution

  • Clear written communication: concise docs, design notes, tradeoff clarity
  • Execution: scoped milestones, incremental delivery, feedback loops
  • Ownership: from development to production monitoring and iteration
  • Pragmatism: simplest solution that meets reliability and scale needs

Technology

Languages


JavaScript
TypeScript
Python
Go
C++
Rust

Backend and Databases


Django
Node.js
FastAPI
MySQL
PostgreSQL
Redis

DevOps and Cloud


Docker
Kubernetes
AWS
GCP
Nginx
Linux

Engineering Principles

  • Clarity beats cleverness
  • Measure before optimizing
  • Build for failure: timeouts, retries, idempotency, graceful degradation
  • Small composable modules, strong boundaries
  • Observability is a production requirement

Featured Work (Systems/Scale → Tools/APIs)

Injection Go Pipeline — High-Throughput Kafka Ingestion (Go)

HTTP → Kafka ingestion pipeline designed for high throughput with bounded backpressure.

Highlights

  • Batching, retries, DLQ strategy for reliability under load
  • Worker/channel pipeline for controlled concurrency and throughput
  • Includes load generation and throughput validation

Tech: Go • Kafka • Backpressure • Batching • Docker Compose
Repo: https://github.com/deannos/injection-go-pipeline.git

Kafka Injector — HTTP to Kafka with Prometheus Metrics (Go)

Low-latency HTTP → Kafka injector focused on safe ingestion under burst traffic.

Highlights

  • Prometheus metrics endpoint for operational visibility
  • Local stack via docker-compose for reproducible runs
  • Batching and safety-first design for sustained throughput

Tech: Go • Kafka • Prometheus • Docker Compose
Repo: https://github.com/deannos/kafka-injector.git

Email Checker Tool — DNS Email Configuration Auditor (Go)

CLI to audit domain email DNS configuration (MX, SPF, DMARC) at scale.

Highlights

  • Concurrent worker pool with global rate limiting
  • Streaming CSV processing for large datasets
  • Timeout-safe DNS lookups and graceful shutdown behavior

Tech: Go • DNS • CLI • Concurrency • CSV
Repo: https://github.com/deannos/email-checker-tool.git

Salary Management API — Clean Architecture API (Go)

HTTP API for employee salary workflows with clean layering and testing discipline.

Highlights

  • Domain/service/repository/handler separation
  • SQLite persistence with clear boundaries
  • CRUD and salary-related endpoints with predictable contracts

Tech: Go • net/http • SQLite • Testing
Repo: https://github.com/deannos/salary-management-api-go.git

GitHub Stats

GitHub Stats

Top Languages

Problem Solving

  • I practice DSA to sharpen reasoning, edge-case handling, and implementation speed.

Currently Exploring

  • Distributed systems fundamentals (queues, caching strategies, consistency)
  • Backend performance tuning and profiling
  • Better observability patterns (structured logs, metrics-first)

Contact

Pinned Loading

  1. email-checker-tool email-checker-tool Public

    A high-performance, open-source CLI tool to verify DNS configurations (MX, SPF, DMARC). Built in Go with concurrent workers, rate limiting, and CSV streaming support for large-scale domain analysis.

    Go 1

  2. salary-management-api-go salary-management-api-go Public

    This application manages employee data, calculates salaries based on country-specific tax rules, and provides salary-related metrics. The project prioritizes software craftsmanship, leveraging a cl…

    Go

  3. deannosTech-website deannosTech-website Public

    A modern, responsive website built with React, Vite, and Tailwind CSS, featuring smooth animations and a professional design.

    JavaScript

  4. injection-go-pipeline injection-go-pipeline Public

    High-throughput Kafka ingestion service built in Go, designed to handle millions of events per minute with bounded backpressure, batching, retries, and DLQ support. Includes a custom load-generatio…

    Go