Chikal Padukawan

Industrial Software & Automation Engineer (Edge-to-Cloud / Data Systems / OEE)

I design and deliver reliable data acquisition and analytics platforms for industrial and healthcare environments. My focus: low-latency data pipelines, KPI computation, and integration resilience under real-world factory conditions. Turning sensor data into operational decisions that improve flow, uptime, and product quality.

Avatar
Available for relocation / hybrid roles in Ireland or EU
7+ yrs
Industry experience (manufacturing, medtech, energy)
11 plants
Active deployments
<2s
Sensor→dashboard latency
>99%
Core availability*
*Ingestion + KPI compute + dashboards (rolling 12-month internal monitoring)
Client logo 1 Client logo 2 Client logo 3 Client logo 4 Client logo 5 Client logo 6 Client logo 7 Client logo 8 Client logo 9 Client logo 10

Projects / Products

Industrial DAQ Platform
Role: Designed the system architecture, implemented ingestion and storage pipelines, and led deployment across multiple manufacturing sites.

Real-time acquisition, normalisation, and storage of machine and sensor data.

Outcome:
  • Provided real-time OEE dashboards and downtime analytics that became integral to daily operations. (Achieved end-to-end latency < 2 seconds. Successfully onboarded 11 plants with sustained usage.)
  • Key Action: Developed edge data collectors with protocol support (Modbus, FINS, MC Protocol, OPC UA, Siemens S7, Profinet, MQTT). Implemented buffering, schema mapping, and retention policies for scalable data storage. Deployed monitoring, alerting, and health checks to ensure robust data flow. Led rollouts and trained on-site teams for smooth adoption.
  • Context: Factories relied on siloed PLC data and manual log sheets, limiting visibility into machine performance and production efficiency.
Stack: DAQ, Python, Edge Computing
OEE Analytics
Role: Led full-stack development and designed the OEE metric model used for cross-plant performance benchmarking.

Manufacturing performance dashboards and standardised KPIs.

Outcome:
  • Provided actionable, reliable OEE insights that enabled management to focus on key bottlenecks and optimise production flow. (Achieved +12% OEE improvement on the primary bottleneck line within the first quarter.)
  • Key Action: Modelled production event states and downtime categories. Implemented a high-performance calculation engine with result caching to minimise latency. Developed web dashboards for real-time and historical OEE tracking using Django. Collaborated with production managers to validate formulas and ensure adoption.
  • Context: Management teams lacked a single source of truth for OEE metrics. Calculations varied across plants, causing inconsistent reporting and poor decision-making.
  • Link Demo: https://lerix.org
  • Username Demo: admin
  • Password Demo: chikal#oee
Stack: OEE, Django, Analytics

Experience

Founder & Tech Lead – Lerix Karya Nusantara
2021 – Present
S: Noticed that multiple manufacturing clients struggled with fragmented data collection systems, leading to poor production visibility and delayed decision-making. Manual reporting consumed valuable engineering time and hindered timely executive insights.
T: Set out to design and deliver a scalable, low-latency industrial data acquisition (DAQ) and OEE platform capable of integrating diverse PLC protocols and delivering real-time analytics across multiple plants.
A: Architected a modular DAQ framework supporting Modbus, FINS, MC Protocol, OPC UA, Siemens S7, Profinet, MQTT, and other industrial protocols. Built ETL pipelines for aggregating, cleansing, and transforming shop-floor data. Developed real-time dashboards and automated alerting systems to monitor production KPIs. Implemented CI/CD and infrastructure-as-code for rapid and reliable deployment. Collaborated closely with plant engineers to ensure seamless adoption and usability.
R: Successfully deployed the solution across 11 plants, achieving consistent usage and executive-level adoption. The platform became a central source of truth for production monitoring and decision-making. (Reduced manual reporting time by 50%. Maintained service availability under production loads. Enabled cross-plant performance benchmarking, driving measurable improvements in OEE.)
Software Developer – Food Opera Express
2020 – 2021
S: Rapid growth in delivery and dine‑in orders required better internal tools and a seamless online ordering experience.
T: Build and maintain internal and customer‑facing web apps to support restaurant operations and ordering at scale.
A: Developed menu management, order tracking and payment integration features. Optimised frontend and API performance, reducing page load times and smoothing user journeys.
R: Enabled smoother day‑to‑day operations and a more reliable ordering flow, improving usability and reducing support noise. (Faster page loads and improved UX)
Software Developer – Edulogy
2019 – 2020
S: The learning platform needed engaging features and a backend that could scale with growing cohorts.
T: Design and implement interactive education functionality and a clean API and data model to support it.
A: Delivered quizzes, progress tracking and content management. Designed backend APIs and database architecture for scalable delivery. Improved reliability by reducing crashes and tidying critical paths.
R: Lifted learner engagement and platform stability with clearer progression and fewer interruptions. (Stability and engagement improvements)
IT Support Officer – Four Points by Sheraton Bandung
2018 – 2019
S: Hotel operations depended on reliable network, POS systems and guest Wi‑Fi while meeting Marriott IT standards.
T: Maintain core IT infrastructure, streamline ticket resolution and ensure compliance with brand security policies.
A: Monitored and maintained network and POS systems, optimised helpdesk workflows to remove bottlenecks, and applied required security controls and updates.
R: Reduced downtime across departments and supported smooth guest experiences while passing compliance checks. (Lower downtime and compliant operations)
References & detailed metrics available on request.

Relocation & Work Eligibility

Ireland
  • Eligible to apply for Critical Skills Employment Permit or General Employment Permit.
  • Prepared documentation for work visa sponsorship under Irish Department of Enterprise, Trade and Employment.
  • Experience aligned with Ireland’s key sectors: manufacturing automation, pharma, healthcare data systems.
  • Immediate actions:
  • Engage with sponsoring employers for permit processing.
  • Provide verifiable references and technical portfolios for skill assessment.
Clarifications

Skills

Architecture & Data Flow
  • Edge→Cloud ingestion
  • Event/state modelling
  • OEE KPI modelling
  • Latency optimisation
Backend & Services
  • Python / Django
  • Scheduling & pipelines
  • API design
  • Data normalisation
Industrial & Protocols
  • Modbus
  • FINS
  • MC Protocol
  • OPC-UA
  • Siemens S7
  • Profinet
  • MQTT
  • Sensor integration
  • Edge deployment
Data & Storage
  • PostgreSQL
  • Caching patterns
  • Aggregation design
  • Data integrity
Reliability & Ops
  • Observability
  • Incident review
  • CI/CD & Docker
  • Deployment automation
Integration & Security
  • Auth & tokens
  • Audit logging
  • Healthcare APIs
  • Data compliance mindset

Contact

Get in touch

Open to roles based in Ireland or EU (remote, hybrid, or on-site). Particularly interested in automation, data systems, or reliability engineering within industrial or medtech environments.

Value Propositions
  • End-to-end ownership: Edge → Cloud → Analytics → Reliability.
  • Proven delivery in OEE uplift, reduced downtime, and stable production data pipelines.
  • Strong understanding of regulated environments (GMP, healthcare data compliance).
  • Automation-first mindset: Infrastructure as Code, traceability, and measurable reliability targets.