Data • AI • Cloud • Architecture

I turn complex business problems into intelligent technology systems.

I'm Sadurshan Punithasegaram — a technology professional working across data engineering, solutions architecture, cloud platforms, automation and enterprise systems.

From data pipelines and APIs to cloud architecture and AI-enabled workflows, I connect business requirements with scalable technical solutions.

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Currently exploring: Data Engineering • AI Platforms • Solutions Architecture

System flow

live

The short version

Technology is only valuable when it solves the right problem.

I work at the intersection of technology, data and business.

My experience spans enterprise application platforms, data workflows, backend development, cloud environments, automation, technical business analysis and solution design.

Working within insurance technology has given me exposure to complex operational workflows, compliance-sensitive data, integrations and regional business requirements.

My focus today is building scalable data and technology platforms that transform fragmented information into reliable systems and useful intelligence.

Business ProblemArchitectureDataAutomationIntelligence

0+

Years across technology & enterprise systems

0%

Operational efficiency improvement

0%

Manual workload reduction through automation

$0.0M

Business impact from enterprise solutions

0

Regional markets supported

0.0%

Revenue improvement through data-driven solutions

Capability map

How I Think About Technology

Six connected domains. Select one to see the technologies behind it and where it shows up in real business work.

Data Engineering

Turning fragmented enterprise information into reliable, queryable sources of truth.

Connected technologies

PythonSQLData ModellingData PipelinesData QualityData GovernanceSnowflakePostgreSQLMSSQLMongoDBMySQL

Example business applications

  • Structuring platform data into governed, reportable datasets
  • Transformation workflows feeding operational reporting
  • Data integrity checks across regional applications

Selected work

Selected Systems & Solutions

A closer look at how I approach real-world technology problems — problem, architecture, implementation, impact.

Problem

Enterprise insurance data can become fragmented across applications, regional operations and legacy systems, making reporting, integrations and operational decisions difficult.

Approach

Designed and maintained Python and database workflows while helping structure fragmented platform data into reliable, queryable sources of truth.

Architecture

01Regional Applications
02APIs / Integrations
03Data Transformation
04Database Layer
05Governed Data
06Reporting / Business Decisions

Technologies

PythonSQLMSSQLPostgreSQLMongoDBSnowflakeAPIsAzure

Impact

  • Reliable data flow
  • Improved data integrity
  • Improved cross-functional visibility
  • Structured governance

Problem

Manual operational processes created unnecessary workload, inconsistency and operational risk.

Approach

Designed automation workflows and integrated middleware systems to replace manual processes with reliable digital workflows.

Architecture

Manual

01Emails
02Spreadsheets
03Human Processing
04Delays
05Errors

Automated

01Trigger
02API
03Workflow
04Validation
05Database
06Business Outcome

Technologies

PythonSQLUiPathAutomation AnywhereAPIsMiddleware

Impact

  • 68% reduction in manual workload
  • $1.8M business impact

Problem

Established platforms need a path to the cloud that protects availability and security while improving delivery speed.

Approach

Approach modernization as a sequence: assess the existing platform, design architecture and integrations, migrate, align security, automate delivery and keep improving.

Architecture

01Existing Platform
02Technical Assessment
03Architecture & Integration Design
04Cloud Migration
05Security Alignment
06CI/CD
07Monitoring & Continuous Improvement

Technologies

AzureAWSDockerTerraformAzure DevOpsCI/CD

Impact

  • Scalability
  • Availability
  • Security
  • Maintainability
  • Operational resilience

Problem

Warehouse operations carry avoidable cost when decisions are made without data-informed optimization.

Approach

Academic research project — AI-Powered Warehouse Optimization System — exploring data ingestion, optimization models and AI-assisted decision making. This is research, not production AI experience.

Architecture

01Data Ingestion
02Optimization Model
03AI-Assisted Decisions
04Operational Efficiency
05Cost Reduction

Technologies

PythonSQLAI/ML ConceptsData Modelling

Impact

  • Research foundation in AI system design
  • Optimization-led thinking applied to operations

Live builds

Shipped and running in production

Work that exists outside a case study — real products people use today.

Corporate web platformlive

RSAF General Trading LLC

rsafllc.com

A Dubai-based trading company platform presenting import, export and distribution services across multiple industries, with a focus on quality, compliance and trade credibility.

  • Designed and developed the full website end to end
  • Structured content and navigation around real business services and markets
  • Built for performance, responsive layouts and search visibility
Web DevelopmentResponsive UISEOHosting & Deployment
Visit live site

Architecture Lab

Patterns I design with

Select a pattern to render the architecture. These are the shapes most enterprise problems eventually take.

Sources
API / Ingestion
Validation
Transformation
Data Warehouse
Semantic Layer
BI / AI

A governed path from raw source systems to decisions people actually trust.

Track record

Experience

Six years across enterprise systems — from software development and business analysis to data workflows and cloud environments.

2023 – Present

Howden Insurance Brokers

Senior Support Engineer – Business Applications

Dubai, UAE

Promoted from Application Developer

  • Python and database workflows
  • Data integrity and governance
  • Enterprise integrations
  • Azure environments
  • CI/CD
  • Insurance technology
  • Regional stakeholder collaboration
  • Technical solution discussions
  • Operational optimization

Arimac Lanka

Technical Business Analyst & Project Manager

  • Technical solution design
  • Data-driven models
  • Stakeholder workshops
  • KPIs
  • Requirements
  • Implementation roadmaps
  • Revenue-focused solutions

Logicare

Software Developer

  • Backend applications
  • Python / SQL
  • Middleware integration
  • Automation
  • Digital transformation
  • Enterprise systems

Boost Hotel Software Solutions

Intern Software Support Engineer

  • Requirements
  • QA automation
  • Monitoring
  • Production risk identification

Positioning

Why I'm different

01

I understand the business.

Not just the technology. I understand requirements, workflows, stakeholders, KPIs and operational constraints.

02

I understand the architecture.

I think beyond individual applications and consider integrations, scalability, cloud, security and maintainability.

03

I understand the data.

I work with databases, data modelling, pipelines, validation, governance and business intelligence.

04

I build for outcomes.

Technology should improve efficiency, reduce risk, increase reliability or create measurable business value.

Business thinking + Engineering + Architecture + Data

Domain context

Enterprise Technology, with Insurance Domain Context

Working inside insurance technology means building in a complex, regulated business environment — where data, workflows and integrations carry real operational and compliance weight.

This is technology experience inside a regulated industry — not actuarial or underwriting expertise.

Exposure across

Client dataPolicy dataClaims workflowsComplianceFinanceRegional operationsEnterprise applicationsData governanceIntegration requirements

Technology

The stack I build with

Tools, not trophies — no ratings, no percentages.

Languages

  • Python
  • C#
  • Java
  • SQL

Data

  • MSSQL
  • PostgreSQL
  • MongoDB
  • Snowflake
  • MySQL

Cloud

  • Azure
  • AWS

DevOps

  • Docker
  • Terraform
  • Azure DevOps
  • CI/CD
  • Git

Automation

  • UiPath
  • Automation Anywhere

Analytics

  • Power BI
  • Tableau

Delivery

  • Jira
  • Confluence
  • Productboard
  • Miro

Method

My approach

A pipeline, not a checklist. Each step feeds the next.

  1. 01

    Understand

    Understand the business problem.

  2. 02

    Model

    Understand data, workflows and dependencies.

  3. 03

    Architect

    Design the right technical architecture.

  4. 04

    Build

    Develop integrations, pipelines, automation and systems.

  5. 05

    Validate

    Test data quality, reliability, security and performance.

  6. 06

    Improve

    Measure outcomes and continuously optimize.

Education

Foundations

BSc Business Information Systems

University of Wolverhampton

Academic research: AI-Powered Warehouse Optimization System — Enhancing Efficiency and Reducing Operational Costs

Higher Diploma in Computing & Software Engineering

ICBT Campus / Cardiff Metropolitan University

Diploma in Information Technology

Esoft Metro Campus

Contact

Have a difficult technology problem?

Let's turn it into something scalable.

Emailsadurshan.sps@gmail.comLinkedInlinkedin.com/in/sadurshansp
Based inDubai, UAE
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SADURSHAN PUNITHASEGARAN

Data Engineer × Solutions Architect × Technology Strategist — Dubai, UAE