Melbourne, Australia · Open to opportunities

Krishnakanth Mohanraj · Data Engineer | Cloud | AI

Building Modern
Cloud Data Platforms

Designing scalable AWS-powered data platforms, intelligent ETL pipelines, and AI-enabled backend systems that transform raw data into actionable business insights.

Download résumé View projects Contact me

PythonSQLAWSApache SparkDockerKubernetesFastAPIREST APIsPandasGit

ETL / ELT data platforms

2× AWS certified

2+Years Industry Experience

20+Technical Projects

AWSCloud Solutions

MonashUniversity

Analytics& Database Background

01 / ABOUT

Engineering Data Platforms
for Scalable Intelligence.

I build cloud-native data platforms that enable organisations to ingest, process, store, and analyse data efficiently.

My work combines backend engineering, cloud architecture, distributed systems, and artificial intelligence to create scalable and reliable data solutions. I'm currently completing a Master of Information Technology at Monash University.

2+Years in data

2×AWS certified

30%Faster reporting

01Data Engineering

02Cloud Computing

03Data Pipelines

04Distributed Systems

05AI Integration

06Backend Engineering

07Data Platforms

08ML Infrastructure

02 / CORE CAPABILITIES

Engineering capabilities
across the data lifecycle.

Data Engineering

Design scalable ETL and ELT pipelines with clear contracts, validation, and observability.

Python · SQL · Spark

Cloud Architecture

Develop secure cloud-native systems using managed and serverless AWS services.

AWS · Lambda · S3

Backend Engineering

Build reliable APIs and event-driven services that connect data products.

FastAPI · REST · Auth

Data Platforms

Create maintainable infrastructure for storage, transformation, and analytics.

Warehousing · Modelling

AI Integration

Integrate machine learning and generative AI where they add measurable value.

Bedrock · PyTorch · YOLO

Distributed Systems

Design resilient services that scale across containers and cloud resources.

Docker · Kubernetes

03 / EXPERIENCE

Building reliable data
systems and workflows.

Nov 2022 — Jun 2024

Analyst

Merkle DGS · Dentsu Global Services · Client: MarketCast

Built complex SQL and Python workflows to extract, clean, validate, and transform enterprise datasets. Automated recurring transformation pipelines, developed Power BI reporting on AWS Redshift, and documented shared data definitions—reducing report turnaround by approximately 30% and manual reporting requests by approximately 20%.

SQLPythonETL PipelinesAWS RedshiftPower BIData Quality

Jun 2022 — Oct 2022

Associate Analyst

Ugam Solutions Pvt. Ltd. · Client: Hall & Partners

Prepared historical retail-sales data through cleaning, preprocessing, feature engineering, and exploratory analysis. Structured optimised SQL tables for repeatable analytical workflows, contributed to a Python forecasting model, and developed JavaScript REST API endpoints for downstream reporting applications.

PythonSQLData ProcessingFeature EngineeringRREST APIs

Melbourne, Australia

Coding Instructor

Code Camp Australia

Teach programming and computational thinking through practical Python, JavaScript, Scratch, and game-development projects, translating technical concepts into clear learning experiences.

PythonJavaScriptScratchGame DevelopmentTechnical Communication

EDUCATION · 2024—2026

Master of Information Technology

Monash University · Melbourne, Australia

Artificial IntelligenceCloud ComputingData ScienceSoftware Engineering

04 / ENGINEERING WORKFLOW

Technologies for modern
cloud data platforms.

Programming

PythonSQLJavaJavaScriptTypeScriptBash

Cloud

AWSLambdaS3API GatewayEC2IAMCloudWatchCognitoDynamoDB

Data Engineering

Apache SparkPandasNumPyETL / ELTData PipelinesData WarehousingData ModellingData Profiling

Backend Systems

FastAPIREST APIsAuthenticationMicroservicesLinuxGitCI/CD

Containers & Infrastructure

DockerKubernetesServerlessDistributed ProcessingObservabilityMLOps

AI Integration

Machine LearningComputer VisionYOLOPyTorchTensorFlowGenerative AIPrompt Engineering

05 / ARCHITECTURE SHOWCASE

How I Build Modern
Data Platforms.

Data Sources

APIs · Files · Events

Ingestion

Lambda · API Gateway

Processing

Spark · Python · ETL

Storage

S3 · Warehouse · DynamoDB

Analytics

SQL · Power BI · APIs

Artificial Intelligence

Bedrock · ML · YOLO

Business Insights

Decisions · Automation

06 / FEATURED PROJECTS

Architecture before
features.

Each project explains the problem, system design, engineering challenge, and outcome.

Data Platform

PROJECT / 01

Smart Data Profiling Platform

Enterprise cloud platform for automated profiling, quality assessment, and intelligent remediation.

Problem

Manual profiling makes quality issues slow to detect and difficult to explain.

Solution

A FastAPI service orchestrates profiling and AWS Bedrock-assisted remediation behind stable REST contracts.

Engineering challenge

Designing explainable AI outputs without weakening deterministic quality checks.

Key outcome

A reusable architecture for faster, governed data-quality assessment.

AWSPythonFastAPIBedrockREST APIsData Quality

GitHub Case study

Event-Driven Cloud

PROJECT / 02

BirdTag

Cloud-native, event-driven platform for scalable bird-image processing and classification.

Problem

Media uploads and inference workloads require decoupled processing that can scale independently.

Solution

S3 events trigger Lambda workflows, YOLO inference produces structured results, and DynamoDB stores query-ready metadata.

Engineering challenge

Coordinating asynchronous events, secure access, and predictable failure recovery.

Key outcome

A serverless reference architecture with low operational overhead.

AWS LambdaS3API GatewayDynamoDBYOLOServerless

GitHub Case study

Data Pipelines

PROJECT / 03

Tennis Match Analytics Platform

ATP analytics pipeline supporting feature engineering, prediction, statistics, and visualisation.

Problem

Raw historical match records need consistent transformations before modelling can be trusted.

Solution

A repeatable Python pipeline validates records, engineers predictive features, and feeds XGBoost and statistical analysis.

Engineering challenge

Preventing leakage while handling incomplete and time-sensitive match features.

Key outcome

A reproducible path from raw records to analysis-ready datasets.

PythonXGBoostRFeature EngineeringData Pipelines

GitHub Case study

ML Infrastructure

PROJECT / 04

Pose Detection Platform

Containerised computer-vision backend designed around scalable inference APIs.

Problem

Real-time pose inference requires reliable APIs and portable deployment across environments.

Solution

FastAPI exposes model inference through containerised services designed for Kubernetes orchestration.

Engineering challenge

Balancing inference latency, container size, and horizontal scalability.

Key outcome

A deployment-ready backend pattern for computer-vision workloads.

FastAPIDockerKubernetesYOLOREST APIs

GitHub Case study

07 / ENGINEERING PHILOSOPHY

Data infrastructure
designed to endure.

I approach data platforms as long-lived products: observable, secure, cost-aware, and easy for downstream teams to trust. Architecture decisions should reduce operational friction and make data more useful—not merely add technology.

Discuss a data platform

Scalable Pipelines

Data Quality

Cloud-Native Architecture

Distributed Processing

Observability

AI-Powered Data Solutions

08 / ENGINEERING PROCESS

From business need
to dependable system.

Discover

Clarify the business objective, users, data sources, constraints, and success measures.

Design

Define contracts, architecture, security boundaries, failure modes, and observability.

Build

Implement small, testable pipeline and service components with automated validation.

Deploy

Package repeatable releases and provision infrastructure with operational safeguards.

Monitor

Track freshness, quality, latency, cost, failures, and downstream service health.

Optimize

Use evidence to improve performance, reliability, maintainability, and cloud spend.

09 / RESUME & CREDENTIALS

Experience backed by
cloud credentials.

2+ yearsIndustry data experience

Monash UniversityMaster of Information Technology

SQL · Python · AWSCore engineering toolkit

2× AWSActive certifications

Download résumé View LinkedIn

AWS · 2026

Solutions Architect — Associate

Validated expertise in designing secure, resilient, high-performing AWS architectures.

AWS · 2026

Cloud Practitioner

Foundational understanding of AWS cloud concepts, services, security, and economics.

10 / OPEN SOURCE / GITHUB

Data engineering in practice.

Public work spanning TypeScript and JavaScript applications, frontend systems, and ongoing engineering experiments.

5Public repositories

2Primary languages

2025GitHub since

looptaskflow-miniFIT5120-OnboardingFIT5225_Frontend

View profile & activity

platform/pipeline.py

class DataPlatform:
  pillars = ["Scale", "Quality", "Trust"]
  cloud_native = True

  def transform(self, raw):
    return raw.to_business_value()

11 / CONTACT

Let's build a
better data platform.

Data Engineer focused on cloud-native platforms, reliable pipelines, backend services, and pragmatic AI integration. Available for conversations with teams building modern data systems.

krishnakanthmohanraj21@gmail.com

GitHub LinkedIn

PROFESSIONAL DETAILS

Location: Melbourne, Australia
Focus: Data Engineering · Cloud · AI
Education: Master of Information Technology, Monash University

Current availability

Open to graduate roles

Open to full-time opportunities

Open to remote work

Start a conversation

Building ModernCloud Data Platforms

Engineering Data Platformsfor Scalable Intelligence.

Engineering capabilitiesacross the data lifecycle.

Data Engineering

Cloud Architecture

Backend Engineering

Data Platforms

AI Integration

Distributed Systems

Building reliable datasystems and workflows.

Analyst

Associate Analyst

Coding Instructor

Master of Information Technology

Technologies for moderncloud data platforms.

Programming

Cloud

Data Engineering

Backend Systems

Containers & Infrastructure

AI Integration

How I Build ModernData Platforms.

Data Sources

Ingestion

Processing

Storage

Analytics

Artificial Intelligence

Business Insights

Architecture beforefeatures.

Smart Data Profiling Platform

BirdTag

Tennis Match Analytics Platform

Pose Detection Platform

Data infrastructuredesigned to endure.

Scalable Pipelines

Data Quality

Cloud-Native Architecture

Distributed Processing

Observability

AI-Powered Data Solutions

From business needto dependable system.

Discover

Design

Build

Deploy

Monitor

Optimize

Experience backed bycloud credentials.

Solutions Architect — Associate

Cloud Practitioner

Data engineering in practice.

Let's build abetter data platform.

Current availability

Building Modern
Cloud Data Platforms

Engineering Data Platforms
for Scalable Intelligence.

Engineering capabilities
across the data lifecycle.

Building reliable data
systems and workflows.

Technologies for modern
cloud data platforms.

How I Build Modern
Data Platforms.

Architecture before
features.

Data infrastructure
designed to endure.

From business need
to dependable system.

Experience backed by
cloud credentials.

Let's build a
better data platform.