Data Analyst | Banking, Treasury, Accounting, Product & SaaS Analytics
I work with SQL, PostgreSQL and Python to build analytical data models, investigate business performance, validate data quality, and evaluate product and operational changes through controlled experiments.
My background in banking, treasury and independent analytics helps me connect technical analysis with commercial, financial, operational, product, and recurring-revenue decisions.
- Synthetic Banking Analytics Platform
- Synthetic Accounting Analytics Platform
- Synthetic Learning Platform Analytics
- Synthetic B2B SaaS Platform Analytics
Together, these platforms demonstrate the full analytical workflow — from generating source data and building relational models to business analysis, data-quality validation, and controlled experimentation.
| Module | Repository | Focus |
|---|---|---|
| 01 | Synthetic Banking Data Generator | Configurable generation of consistent synthetic banking data |
| 02 | Synthetic Banking SQL | PostgreSQL data modelling, DDL, ETL, and analytics-ready structures |
| 03 | Synthetic Banking Analytics | 63 SQL business cases, executive reporting, and commercial insights |
| 04 | Synthetic Banking Data Quality | SQL-first validation of completeness, consistency, integrity, and business rules |
| 05 | Synthetic Banking A/B Testing | Experiment design, power analysis, CUPED, bootstrap inference, and decision rules |
| Module | Repository | Focus |
|---|---|---|
| 01 | Synthetic Accounting Data Generator | Reproducible generation of 35 relational accounting CSV tables with controlled data-quality scenarios |
| 02 | Synthetic Accounting SQL | PostgreSQL landing, core, analytics, quality, and metadata layers over a 15.7M-row dataset |
| 03 | Synthetic Accounting Analytics | 72 SQL business cases covering clients, services, revenue, bookkeeping, cash, tax, operations, and executive reporting |
| 04 | Synthetic Accounting Data Quality | 64 SQL controls with profiling, scoring, expected-issue reconciliation, and regression monitoring |
| 05 | Synthetic Accounting A/B Testing | Seven controlled experiments using continuous, binary, count, survival, non-inferiority, cluster, and factorial designs |
| Module | Repository | Focus |
|---|---|---|
| Integrated Platform | Synthetic Learning Platform Analytics | End-to-end product analytics platform for a synthetic online learning marketplace integrating configurable data generation, PostgreSQL modelling, analytical SQL, governed metrics, product analytics, experimentation, data-quality validation, and executive reporting. |
| Module | Repository | Focus |
|---|---|---|
| Integrated Platform | Synthetic B2B SaaS Platform Analytics | End-to-end analytics platform for a European B2B field-service SaaS business, integrating CRM, contracts, subscriptions, recurring billing, multi-currency revenue, product adoption, customer success, support, renewals, churn, experimentation, data-quality controls, and executive reporting |
Synthetic data generation
↓
PostgreSQL modelling and ETL
↓
Business analytics
↓
Data quality validation
↓
A/B testing and experimentation
SQL · PostgreSQL · Python · pandas · NumPy · statsmodels · Jupyter · Git
Banking · Treasury · Accounting · B2B SaaS · Subscription Economics · Product Analytics · Financial Operations · Online Learning · Customer Behaviour · Revenue Performance · Retention · Customer Success · Experimentation · Data Quality
All portfolio data is synthetic. The projects reproduce realistic banking, accounting, marketplace, and B2B SaaS structures and analytical workflows without exposing confidential, proprietary, or client-identifying information.