Shubhendu Backend API

Engineering excellence, documented.

Welcome to the official API documentation for Shubhendu Sangam Dash — a Senior Java Backend Engineer with 9+ years of experience building scalable, high-performance enterprise applications.

Base URL

https://shubhendu.dev/api/v9

Production Ready v9.0 9+ Years Uptime

Quick Links

About Experience Projects Contact

GET

/about

Returns comprehensive information about the engineer.

Response 200 OK

{
  "name": "Shubhendu Sangam Dash",
  "title": "Senior Java Backend Engineer",
  "experience": "9+ years",
  "location": "Bhubaneswar, India",
  "status": "Available for opportunities",
  "summary": "Senior Java Backend Engineer with 9+ years of
    experience in designing and implementing scalable,
    high-performance enterprise applications.",
  "specializations": [
    "Distributed Systems",
    "Microservices Architecture",
    "Search & Data Processing",
    "Performance Optimization"
  ],
  "links": {
    "email": "shubhendu.sangam@gmail.com",
    "linkedin": "linkedin.com/in/shubhendusangam",
    "github": "github.com/shubhendusangam"
  }
}

Core Values

Clean Code: Writing maintainable, readable, and testable code
Scalability: Designing systems that grow with business needs
Performance: Optimizing for speed and efficiency
Collaboration: Effective communication across teams

GET

/experience

Returns work history with quantifiable achievements.

Response 200 OK

{
  "total_experience": "9+ years",
  "positions": [
    {
      "company": "Syndigo LLC",
      "role": "Senior Java Backend Engineer",
      "period": "2020 – Present",
      "location": "Remote",
      "status": "current"
    },
    {
      "company": "Hexagon Global IT Services",
      "role": "Software Engineer",
      "period": "2017 – 2020",
      "location": "Bangalore, India"
    }
  ]
}

Current

Senior Java Backend Engineer

Syndigo LLC

2020 – Present · Remote

Key Achievements

40% Processing speed improvement through optimized algorithms
50% Elasticsearch accuracy increase via query optimization
30% ML deployment speed improvement with pipeline automation
100K+ Record batch processing capability implemented
35% Data standardization accuracy improvement

Java RESTful APIs Azure Cloud Elasticsearch Kubernetes Kafka

Software Engineer

Hexagon Global IT Services

2017 – 2020 · Bangalore, India

Key Achievements

Built RESTful APIs serving millions of requests daily
Implemented microservices architecture migration
Led code review processes and mentored junior developers
Designed and implemented CI/CD pipelines

Java Spring Hibernate PostgreSQL JavaScript RESTful APIs

GET

/projects

Returns featured projects with impact metrics.

Response 200 OK

{
  "total_projects": 5,
  "featured": [
    {
      "name": "High-Performance Batch Processing System",
      "impact": "40% processing speed improvement"
    },
    {
      "name": "Elasticsearch Query Optimizer",
      "impact": "50% accuracy improvement"
    }
  ]
}

High-Performance Batch Processing

Designed and implemented a scalable batch processing system capable of handling 100K+ records with optimized memory management.

Impact: 40% processing speed improvement

Java Kafka Batch Processing Elasticsearch RESTful APIs

Elasticsearch Query Optimizer

Built an intelligent query optimization layer that improved search relevance and accuracy across the product catalog.

Impact: 50% accuracy improvement

Elasticsearch Java RESTful APIs

ML Pipeline Automation

Automated ML model deployment pipeline reducing time-to-production for data science teams.

Impact: 30% faster deployments

Java Elasticsearch RESTful APIs

Data Standardization Engine

Created a rule-based data standardization engine improving data quality across enterprise systems.

Impact: 35% accuracy improvement

Java Elasticsearch Data Processing RESTful APIs

GET

/projects/football-predictor

Returns project details for the Football Predictor system.

Overview

A data-driven football match outcome prediction system built with backend engineering principles. The system ingests historical match data, processes feature vectors, and generates probabilistic predictions for match outcomes.

This project demonstrates the application of software engineering discipline to sports analytics—focusing on clean architecture, testable code, and production-ready design patterns rather than experimental ML scripts.

Architecture application/json

{
  "system": "Football Match Predictor",
  "architecture": "Layered / Clean Architecture",
  "pipeline": {
    "input": "Historical match data (leagues, teams, scores)",
    "processing": "Feature extraction and statistical modeling",
    "prediction": "Match outcome probability distribution",
    "output": "Home Win / Draw / Away Win prediction"
  },
  "tech_stack": [
    "Java",
    "Data Processing (Java Streams)",
    "Statistical Modeling",
    "Batch Analysis Pipeline",
    "Clean Code Principles"
  ]
}

Key Engineering Highlights

Data Ingestion: Structured pipeline for importing and validating historical match datasets with schema enforcement
Feature Engineering: Extraction of predictive signals: form, head-to-head records, home/away performance, goal differentials
Model Evaluation: Cross-validation with accuracy, precision, and log-loss metrics to measure prediction quality
Clean Architecture: Separation of data access, business logic, and prediction layers for testability and maintainability
Batch Processing: Designed for efficient bulk prediction across multiple leagues and seasons

Response 200 OK

{
  "project_name": "Football Predictor",
  "type": "Personal Project",
  "status": "Active Development",
  "domain": "Sports Analytics / Machine Learning",
  "focus": [
    "Data modeling and feature engineering",
    "Prediction algorithms",
    "Performance optimization",
    "Backend system design"
  ],
  "repository": {
    "platform": "GitHub",
    "url": "https://github.com/shubhendusangam/football-predictor",
    "visibility": "Public"
  }
}

System Design Considerations

Scaling this system for production use with millions of users requires careful architectural decisions:

Caching Strategy: Pre-computed predictions cached in Redis with TTL based on match schedule; cache invalidation on new data arrival
Batch vs Real-time: Batch predictions generated nightly for upcoming matches; real-time adjustments only for last-minute team changes
Model Retraining: Scheduled retraining pipeline triggered weekly with new match results; A/B testing for model version comparison
Data Storage: Time-series optimized storage for historical data; read replicas for high-throughput prediction queries
API Design: RESTful endpoints with rate limiting, pagination, and webhook support for real-time prediction updates

GET /projects/football-predictor/system-design 200 OK

{
  "objective": "Predict football match outcomes with historical match data using a backend service",
  "data_ingestion": {
    "source": "CSV or structured datasets containing historical match statistics",
    "preprocessing": [
      "Feature extraction (goals, shots, possession, team rating, etc.)",
      "Cleaning missing values",
      "Normalization / scaling"
    ],
    "storage": "H2 database for structured data access"
  },
  "model_training": {
    "framework": "Weka",
    "training_type": "Batch offline training",
    "output": "Serialized predictive model artifact (e.g., .model file)",
    "evaluation_metrics": [
        "Accuracy",
        "Precision",
        "Recall",
        "Log-loss"
    ]
  },
  "backend_service": {
    "language": "Java backend",
    "API_framework": "Spring Boot",
    "prediction_api": "/predict",
    "input_schema": {
      "home_team_stats": "...",
      "away_team_stats": "..."
    },
    "response_schema": {
      "home_win_prob": "float",
      "draw_prob": "float",
      "away_win_prob": "float"
    }
  },
  "inference_logic": {
    "load_model": "On service startup, load the trained model into memory",
    "latency_target": "<200ms per prediction",
    "precomputed_features": "Compute static features ahead of prediction requests"
  },
  "storage_and_caching": {
    "model_storage": "Local disk or configured artifact store",
    "cache": "Optional in-memory cache for repeated queries",
    "database": "H2 for structured dataset and results"
  },
  "monitoring_and_logging": {
    "logging": "Structured API request/response logs",
    "metrics": "Optional time-to-predict, error tracking"
  },
  "security": {
    "input_validation": "Ensure only valid match data is accepted",
    "authentication": "If exposed publicly, add API key or basic auth"
  },
  "future_scaling_plan": {
    "containerization": "Using Docker to package backend + model",
    "load_balancer": "Add reverse proxy/load balancer ahead of multiple replicas",
    "caching": "Introduce Redis for high-traffic caching",
    "async_batch_training": "Offline retraining pipeline using scheduled jobs",
    "observability": "Add Prometheus + Grafana for metrics"
  }
}

Notes

This project applies production engineering practices to sports analytics. The focus is on building maintainable, testable systems—not just achieving prediction accuracy. View the source code and documentation on GitHub.

GET

/skills

Returns technical skills with proficiency levels.

Response 200 OK

{
  "categories": [
    {
      "name": "Languages & Core",
      "skills": ["Java (80%)", "Python(30%)"]
    },
    {
      "name": "Frameworks",
      "skills": ["Spring Boot (70%)", "Hibernate/JPA (70%)", "Microservices(80%)", "Restful APIs(90%)"]
    }
  ]
}

Languages & Core

Java 80%

Python 30%

Frameworks

Spring Boot 70%

Hibernate/JPA 70%

REST APIs 90%

Data & Search

Elasticsearch 70%

Kafka 50%

Redis 50%

DevOps & Tools

Maven 80%

Docker 50%

Kubernetes 50%

GET

/metrics

Returns performance metrics and career statistics.

Response 200 OK

{
  "career_metrics": {
    "years_experience": 9,
    "projects_delivered": "15+",
    "code_reviews": "500+",
    "uptime": "99.9%"
  },
  "performance_improvements": {
    "processing_speed": "+40%",
    "search_accuracy": "+50%",
    "deployment_time": "-30%"
  }
}

9+

Years Experience

8+

Projects Delivered

100+

Code Reviews

Performance Improvements Delivered

Processing Speed

+40%

Search Accuracy

+50%

Deployment Time

-30%

Data Quality

+35%

GET

/system-design

Returns system architecture and design philosophy.

Response 200 OK

{
  "architecture_style": "Microservices",
  "patterns": [
    "Event-Driven Architecture",
    "Saga Pattern",
    "Circuit Breaker"
  ],
  "principles": [
    "SOLID",
    "DRY",
    "KISS"
  ]
}

Architecture Patterns

Event-Driven Architecture
CQRS (Command Query Responsibility Segregation)
Saga Pattern for Distributed Transactions
Circuit Breaker for Fault Tolerance
API Gateway Pattern

Design Principles

SOLID - Object-oriented design principles
DRY - Don't Repeat Yourself
KISS - Keep It Simple, Stupid
TDD/BDD - Test-driven development

Technology Stack Architecture

Client Layer

Web Apps Mobile Apps Third-party APIs

API Gateway

Load Balancer Rate Limiting Authentication

Service Layer

Spring Boot Microservices Kafka Event Bus Redis Cache

Data Layer

PostgreSQL Elasticsearch

GET

/certifications

Returns professional certifications and continuous learning records.

Response 200 OK

{
  "certifications": [
    {
      "title": "Java EE: RESTful Service with JAX-RS 2.0",
      "focus_area": "REST API Development",
      "status": "Completed"
    },
    {
      "title": "Responsible AI: Principles and Practical Applications",
      "focus_area": "AI Governance",
      "status": "Completed"
    },
    {
      "title": "Introduction to Spring AI",
      "focus_area": "AI + Spring Ecosystem",
      "status": "Completed"
    },
    {
      "title": "Learn Apache Kafka for Beginners",
      "focus_area": "Event Streaming",
      "status": "Completed"
    },
    {
      "title": "What Is Generative AI?",
      "focus_area": "Generative Models",
      "status": "Completed"
    }
  ]
}

Metadata

Last Updated: 2026-03
Learning Strategy: Continuous backend and distributed systems improvement
Primary Domains: Backend Systems, Microservices, AI Integration

Notes

Certifications complement production experience. Primary validation comes from real-world system implementation.

POST

/contact

Send a message to initiate collaboration.

Request Body application/json

{
  "name": "Hiring Manager / Recruiter",
  "email": "shubhendu.sangam@gmail.com",
  "subject": "Backend Engineering Role",
  "message": "I specialize in building scalable, high-throughput backend systems with measurable performance impact. I’m open to discussing backend engineering opportunities."
}

Direct Contact Methods

Email shubhendu.sangam@gmail.com

LinkedIn linkedin.com/in/shubhendusangam/

GitHub github.com/shubhendusangam

Success Response 201 Created

{
  "status": "success",
  "message": "Thank you for reaching out! I'll respond within 24-48 hours.",
  "timestamp": "2026-03-1T10:30:00Z"
}

Quick Links

Core Values

Key Achievements

Key Achievements

High-Performance Batch Processing

Elasticsearch Query Optimizer

ML Pipeline Automation

Data Standardization Engine

Overview

Key Engineering Highlights

System Design Considerations

Notes

💻 Languages & Core

🏗️ Frameworks

🔍 Data & Search

🔧 DevOps & Tools

Performance Improvements Delivered

Architecture Patterns

Design Principles

Technology Stack Architecture

Metadata

Notes

Direct Contact Methods

Languages & Core

Frameworks

Data & Search

DevOps & Tools