Finance → Data → Systems → Decisions
I build systems that turn messy financial and operational data into reconciled state, reusable data infrastructure, and decision-ready analytics.
I started with the web, moved into full-stack products, found Python through analytics, and kept following the data until the side quests became engines. Somewhere along the way, Chartered Accountancy and software engineering stopped being separate tracks. 😄
flowchart LR
E1["I · Product Engineering<br/>Vue · React · TypeScript<br/>Node · Express · PostgreSQL"]
E2["II · Analytics Engineering<br/>Excel · Power Query · Power BI<br/>Python automation"]
E3["III · Data & Semantic Systems<br/>Polars · SQL · ETL<br/>PyQuery · RAG · Local AI"]
E4["IV · Finance × Systems<br/>Control Plane · DuckDB<br/>Tax · XIRR · Monte Carlo"]
E1 --> E2 --> E3 --> E4
CA["Chartered Accountancy<br/>Accounting · Tax · Controls"] --> X["Finance × Engineering"]
E2 --> X
E3 --> X
X --> E4
Build products → engineer data → model semantics → reconstruct financial truth.
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A local-first financial data platform. Reconstructs fragmented financial evidence into reconciled household and investment state, then carries that state through analytics, tax, planning, and BI. Data architecture Finance & quant Reliability & product |
⚡ PyQueryPython-native data execution systems. What began as Power Query-style transformation tooling evolved into reusable local-first data infrastructure. Current Core Platform evolution What it represents Explore: HQ · Core · Current Engine · Legacy · Platform Evolution |
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Power BI semantics → reusable data infrastructure Extracts a Power BI semantic model, reconstructs it in relational SQL, and layers local semantic/RAG capabilities over that state.
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Python engineering inside real Excel workflows A modular Python × Excel × VBA framework for UDFs, query helpers, Power Query functions, and reusable analytics utilities.
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Analytics engineering turned into a product An interactive synthetic-data workbench for finance and operations POCs, dashboard testing, modelling, and prototyping without exposing real client data.
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A local systems-engineering side quest An open-source, memory-conscious take on Windows Recall built around local multimedia processing.
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Also in the analytics tooling lane: xl-pq-handler, built around managing Power Query assets as maintainable engineering artifacts.
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A Vue-based Google Drive indexing product with backend services, authentication, role-based access control, MongoDB, media handling, and deployment tooling.
250+ stars · 230+ forks Open-source product engineering before data systems became the main plot. |
A full-stack personal-finance application built across 400+ commits in separate frontend and backend repositories. Frontend Backend Where finance and product engineering first started sharing the same codebase. |
| Capability | Toolkit / concepts |
|---|---|
| Data engineering | Python · Polars · DuckDB · SQLite · SQL · ETL · data contracts |
| Analytics & BI | Power BI · Power Query · Excel · DAX · semantic modelling · analytical marts |
| Software & product engineering | Pydantic · APIs · CLI · desktop apps · TypeScript · React · Node.js |
| Finance & quant | Accounting · reconciliation · tax · FIFO · XIRR · portfolio analytics · Monte Carlo |
| AI & semantic systems | LangChain · RAG · Ollama · local LLM workflows · semantic metadata |
Chartered Accountancy is the domain lens, not a decorative credential. It shows up in how I think about evidence, reconciliation, controls, tax, grain, and financial semantics.
Stats are fun. Projects are the receipts.
Finance gave me the questions. Data gave me the structure. Software engineering let me build the answers.