
Regulatory Applications Analysis
Python case study on regulatory application data, SLA risk, delays and early-warning indicators.
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Database · Applications · Analytics · Software
Work across enterprise applications, databases, reporting and software development. A record of what I have built, learned and occasionally had to fix.
See what I've been working onFEATURED CASE STUDIES

Python case study on regulatory application data, SLA risk, delays and early-warning indicators.
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Spring Boot API modelling advisors, portfolios, products, appointments and transactions.
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Python pipeline that cleans service ticket data and produces reporting-ready outputs.
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Portfolio in progress. Naturally.
ABOUT
I am a database and applications professional with experience across enterprise systems, SQL databases, reporting, service management and operational support.
I am expanding that foundation through practical projects in Python, data analytics, software engineering, automation and data engineering.
My work sits between technology and operations, with a focus on how systems support people, processes and decision-making.
I value clear documentation, maintainable solutions and technology that remains useful beyond the initial implementation.
THINGS I'VE BUILT
01
Python portfolio case study analysing synthetic regulatory application data to identify SLA breaches, processing delays, department risk, document issues and early-warning indicators. Includes saved chart outputs, business recommendations, model notes and a companion SQL analytics project.
02
A PostgreSQL analytics database for synthetic regulatory applications data, including table design, CSV import, SLA reporting queries, reusable views and documentation.
03
A Spring Boot backend API modelling advisors, customers, portfolios, investment products, appointments, financial goals, risk profiles and transactions. Includes JPA entities, repositories, H2 seed data, REST endpoints, DTO responses, automated tests and release documentation.
04
Python data engineering project that processes raw service ticket data, validates required fields, cleans operational records, generates data quality checks and produces reporting-ready summary outputs through a one-command pipeline.