SQL, PostgreSQL, graph & application data engineering
I treat the data model as part of the product design. Invoices, tenant credit, patient context, payment ledgers, cargo assignments, journal entries, local mobile state, and recommendation history all become easier to reason about when the database expresses the real business relationships clearly.
What this looks like in practice
Accounting-oriented invoice, payment, credit, lease, property, unit, and tenant relationships in RentPayor.
Patient, encounter, facility, billing, orders, movement, and receipt state in CliviQue HMIS.
Operational load, reservation, driver, fleet, payout, and immutable ledger state in Macsim Cargo.
SQLite/local state and cloud-backed application data patterns in mobile products.
Each project below links to a deeper case study covering product scope, engineering ownership, supporting systems, and production evidence.
Enterprise · Case study
RentPayor
Rent collection and reconciliation software for landlords and property managers. RentPayor creates rent invoices, lets tenants pay KES rent through an invoice-linked M-Pesa flow, automatically reconciles confirmed payments, and keeps partial balances, carried-forward credits, receipts, leases, units, tenants, and manual payment records in one rent ledger.
Cargo and logistics operations platform spanning mobile field workflows and back-office administration. Macsim handles loads, reservations, driver assignments, trip tracking, documents, notifications, fleet operations and finance, with a guarded M-Pesa/Daraja flow for funding a collection-account ledger and paying drivers in full or partial load installments.
Voice-first journaling app that preserves the original recording, transcribes spoken entries, and uses AI to generate useful titles, summaries, and categories. Entries can be searched and revisited by date, linked to goals, supported by reminders and prompts, and protected with account, deletion, and optional biometric controls.
Mobile wardrobe-management and outfit-recommendation product. Users create wardrobes, upload clothing photos for AI-assisted category, tag, and description analysis, and generate one-time, daily, or weekly outfit suggestions that can use precise local weather; recommendation history is stored locally in SQLite and designed to sync with the cloud.