I am a backend software engineer building production-grade Python systems across ERP and product configurators. My work sits at the intersection of engineering, software systems, and data workflows, with a foundation in engineering automation through the .NET ecosystem. This foundation and engineering background has shaped how I think about constraints, reproducibility, and the practical design of systems.
Sean Marandure / Backend Software Engineer
Open to backend, data, automation, and ML-adjacent roles
Engineering logic into reliable software systems.
I build production-grade Python systems for ERP and product configurator environments, with a focus on backend architecture, automation, and data workflows.
My path runs from CAD automation and rule-based engineering tooling into scalable software systems, and is now extending through an MSc in Data Science & Advanced Computing with a long-term interest in machine learning systems, optimisation, and advanced decision environments.
Core focus
Backend systems, automation layers, and structured data workflows.
Engineering roots
C#, .NET, rule-driven product logic, and Automation, Autodesk Inventor iLogic.
Current chapter
MSc Data Science & Advanced Computing at the University of Reading.
/ About
Backend engineering with an automation-first mindset.
My ethos is to translate engineering constraints into dependable software. That means building control layers, automating rule-based systems, working with relational data, and designing tools that stay useful inside real operational environments.
Production Python systems
My focus is on backend architecture, data, version-controlled tooling, automation pipelines, and dependable workflow execution.
Engineering to software
I'm comfortable moving between physical product rules, relational data structures, and application control layers.
Data and ML trajectory
My current MSc work is deepening the expertise in machine learning systems, optimisation, and intelligent systems.
/ Experience
From engineering constraints to software systems.
My portfolio is a systems journey comprising of an engineering foundation, automation roots, backend transition, and an expanding data and ML-focused chapter.
Engineering first
Architectural Environment Engineering built the systems lens: modelling, constraints, environmental performance, physics, mathematics and the built environment, cultivating the discipline of solving technical problems with traceable logic.
CAD logic and rule-based configurators
Early software work centered on Autodesk Inventor iLogic and .NET, building rule-based parametric behavior for configurable products and translating engineering rules into repeatable digital controls.
Version control, databases, and backend tooling
That work evolved into software engineering focused on Python applications, relational databases, reproducible automation pipelines, and the backend control surfaces needed by enterprise systems, ETL pipelines and visualisations for reporting.
Backend, data, and distributed systems thinking
My current work sits around ERP and product configurator environments, alongside projects in MapReduce-style processing and cloud incident simulation that sharpen distributed thinking, observability, and data-intensive architecture. I'm also building healthcare platforms and software applications for large domicilliary healthcare providers with regional offices around the UK, expanding my domain expertise across multiple industries. My goal is to solve real-world constraints through software-based solutions.
MSc direction
The MSc in Data Science & Advanced Computing at the University of Reading is extending that backend foundation toward machine learning systems, optimisation, and advanced computing environments.
/ Projects
Selected work across backend tooling, automation, cloud, and applied ML.
Each project reflects a different slice of the same interest: robust systems, structured data, and practical automation under real constraints.
Version Control Application
SystemsArchitected a modular Python CLI application designed to version-control enterprise systems. The project involved parsing raw data structures, managing relational database state, and enabling reproducible automation pipelines within a legacy environment.
MapReduce System (Ongoing)
DistributedCurrently developing a MapReduce-style distributed processing framework to coordinate parallel map and reduce workers over partitioned datasets. The system implements task scheduling, intermediate key-value shuffling, and process communication to simulate large-scale data processing workflows.
View GitHubAWS GameDay - Cloud Incident Simulation
CloudParticipated in a timed AWS simulation focused on diagnosing and resolving distributed system failures across IAM, EC2, S3, RDS, and CloudWatch. The exercise strengthened cloud architecture, observability, and infrastructure-level problem solving under pressure.
SolveMyMatrix
UtilityAn educational CLI tool for solving linear systems using matrix transformations, including Gaussian elimination and inverse computation, built while completing Coursera's Mathematics for Machine Learning.
View GitHubBEng Final Year Project - CFD-ML Urban Microclimate Optimisation
ResearchUsed CFD-generated data to train an ANN that interpolated wind velocity and predicted building geometries for pedestrian comfort, with simulation outputs validated against real weather data.
Inventor iLogic - Acoustic Door Configurator
AutomationBuilt a rule-based parametric model that clamps width and height, conditionally suppresses features, and writes iProperties used for BOM and drawing outputs.
TaskHive (Previously PTL-Manager)
ProductivityA lightweight Flask MVP that started as a workplace task-management tool and evolved into a broader productivity application vision, emphasizing clean API design, database integration, and scalable feature development.
/ Skills
A stack built around systems reliability, automation, and analysis.
The tooling spans backend engineering, data work, and engineering-oriented automation, with an emphasis on building dependable systems rather than one-off demos.
Backend & automation
Core application and automation work for configurable systems and service-oriented tooling.
Python
Flask
SQL Server
.NET
Data & modelling
Numerical computing, experimentation, and applied modelling workflows across analysis and ML-adjacent work.
NumPy
TensorFlow
MATLAB
R
Power BI
Tooling & delivery
Environment, workflow, and delivery tooling used to build, explore, and maintain technical systems.
Git
VS Code
Jupyter
Microsoft Azure / DevOps
/ Education
Formal training across engineering and advanced computing.
The academic path mirrors the practical work: strong engineering fundamentals first, followed by a deeper move into data science, AI/ML, and advanced computing.
MSc Data Science & Advanced Computing
University of Reading
BEng (Hons) Architectural Environment Engineering
University of Nottingham