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

4Years 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

Data Engineer

Merkle DGS · Coimbatore, India

Automated deployment of AWS Glue jobs, Lambda functions, and Redshift schema updates using AWS CDK and GitHub Actions, cutting deployment time by approximately 80%.
Built Python-based AWS Lambda functions orchestrated with AWS Step Functions to decrypt PGP-encrypted files, validate schema contracts, and trigger downstream AWS Glue ETL workflows, eliminating a manual daily verification process.
Diagnosed slow reporting queries using EXPLAIN plans to pinpoint inefficient scan patterns across large Redshift tables.
Optimised Redshift sort keys based on those findings, reducing the slowest recurring report from approximately 4 minutes to under 90 seconds.
Implemented CloudWatch monitoring and SNS-based Slack notifications for Glue and Lambda failures, cutting production issue detection from the next business day to same-day.
Developed AWS Glue (PySpark) jobs across a three-tier Amazon S3 data lake (L0 Raw → L1 Validated → L2 Curated), enforcing automated schema validation before promotion to reduce data quality issues reaching reporting layers.
Processed approximately 100–200 GB of batch data daily from SFTP and REST API sources feeding the L0/L1/L2 pipeline.
Contributed to CDC-based incremental loading into Amazon Redshift using AWS Database Migration Service (AWS DMS), keeping reporting tables closely in sync with source systems.
Partnered with reporting teams to align Redshift schema changes with Power BI requirements and resolve production data discrepancies.
Supported ingestion and transformation workflows within the AWS-based data platform underpinning the Hall & Partners Alcohol Sales reporting platform.

AWS GluePySparkLambdaStep FunctionsRedshiftAWS CDKGitHub ActionsS3CloudWatchAWS DMS

Jun 2021 — Oct 2022

Analyst

Ugam Solutions Pvt. Ltd. · Coimbatore, India

Automated a manual daily file validation process using AWS Lambda, reducing a one-hour operational task to only a few minutes.
Developed AWS pipeline components using Amazon S3, AWS Lambda, and AWS Glue under senior engineering guidance for a client reporting platform.
Built AWS Glue ETL jobs to clean, validate, and transform data across raw and processed Amazon S3 layers.
Migrated manual deployment processes into GitHub Actions CI/CD pipelines using AWS CDK templates.
Troubleshot production ETL pipeline failures and contributed to broader data quality initiatives.

AWS GlueAWS LambdaAmazon S3AWS CDKGitHub ActionsETLData Quality

Jun 2020 — Jun 2021

Associate Analyst

Ugam Solutions Pvt. Ltd. · Coimbatore, India

Developed and optimised SQL queries and stored procedures supporting enterprise operational reporting.
Built SQL validation rules that detected data quality issues before recurring reports reached business stakeholders.
Developed REST API integrations and JavaScript automation to replace manual CSV import/export workflows.
Investigated production data discrepancies by tracing source-system mapping issues and collaborating with business users to implement permanent fixes.
Gathered reporting requirements directly from stakeholders and translated business needs into SQL-based reporting solutions.

SQLStored ProceduresREST APIsJavaScriptData QualityOperational Reporting

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.

Cloud-Native Media Data

PROJECT / 01

SentinelVision

End-to-end image and video data platform with automated OpenCV metadata extraction, quality validation, and analytics-ready datasets.

Problem

Unstructured media needs consistent metadata and quality checks before it can reliably support analytics and machine-learning workflows.

Solution

React requests presigned URLs through API Gateway and Lambda for direct-to-S3 uploads. S3 events trigger a Docker-based OpenCV Lambda to write processed metadata; local Python ETL supports PostgreSQL analysis.

Engineering challenge

Decoupling ingestion from processing, packaging OpenCV in an ECR-hosted container, and separating raw and processed data to prevent recursive triggers.

Key outcome

Automated image dimensions, brightness and blur checks, plus video FPS and duration metadata. Supporting Pandas validation, SQL analysis, and idempotent loading prepare datasets for downstream use.

PythonOpenCVAWS LambdaAPI GatewayS3ECRDockerReactPostgreSQLPandas

GitHub Live demo

Data Quality Intelligence

PROJECT / 02

TESSA

Cloud-based data profiling platform that turns uploaded CSV files into interactive quality insights and downloadable PDF reports.

Problem

Understanding missing values, duplicate records, data types, and outliers typically requires manual analysis or specialist tooling.

Solution

A Next.js dashboard sends CSV datasets through API Gateway to a containerised FastAPI service on AWS Lambda for Pandas-based profiling and ReportLab exports.

Engineering challenge

Packaging a data-processing API for serverless execution while managing image size, CORS, response handling, and production observability.

Key outcome

A production-deployed, no-code workflow for assessing dataset quality, exploring column-level statistics, and sharing professional reports.

Next.jsTypeScriptFastAPIPandasAWS LambdaAPI GatewayDockerReportLab

Live application

Event-Driven Cloud

PROJECT / 03

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 / 04

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 / 05

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.

4 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.

Data Engineer

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.

SentinelVision

TESSA

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.