Databases

• Accelerated geographical data pipeline performance by 50x by improving SQL programming and data modeling across PostgreSQL, MS SQL, and Google Cloud BigQuery.
• Improved geographical map application performance by 10x through strategic database transition from MSSQL to PostgreSQL, optimizing processing and data security.
• Resolved 1,000 issues in geographical data and time‑series data, using GDAL, ArcGIS, PostGIS, Mapbox, QGIS, SQL (PL/pgSQL, Transact‑SQL), Bash, ensuring high‑quality big data processing.
• Designed, implemented, and administered 6 ETL/ELT pipelines, utilizing Google BigQuery, MSSQL, PostgreSQL, Shell scripting, PL/pgSQL, and Transact‑SQL, integrating data for efficient Python API processing.
• Released 500 electricity and GIS data analysis reports, utilizing deep research and troubleshooting to ensure accurate geographic and time series big data insights.
• Architected, created, and managed 100 PostgreSQL, MS SQL, and Google BigQuery data warehouse databases with primarily GIS and time‑series data, optimizing performance and scalability.
• Directed full‑stack GIS map development, overseeing PostgreSQL, Mapbox, ReactJS, and NodeJS to deliver an integrated solution.
• Led the development, deployment, and support of over 30 GIS projects, demonstrating expertise in PostgreSQL, Bash, Python, JavaScript, GDAL, ArcGIS, PostGIS, and Mapbox technologies.
• Architected a layered maritime platform separating a Next.js PWA frontend, a Go (Huma/Fiber) API, and a PostgreSQL function layer, keeping all business logic in the database.
• Designed a JSON passthrough architecture where PostgreSQL functions return complete JSON forwarded verbatim by the Go API, eliminating intermediate unmarshalling and decoupling the frontend from schema changes.
• Designed a PostgreSQL function‑first data layer — 1,275 stored functions across 34 schemas — so every read and write goes through a function the database can grant, rather than through a table.
• Implemented a catalogue‑driven deep‑merge for stored JSON preferences, preventing missing‑key crashes as the schema evolves.
• Built the Python vessel‑data scrapers (MarineTraffic, Maritime‑Database) and a repeatable import that seeds the platform's reference data — 184,197 rows, including 698 companies and 56,149 vessels.
• Modeled the maritime domain into 348 normalized tables across 34 PostgreSQL schemas — professionals, companies, ships, jobs, reviews and the rest — with SMALLINT lookups and UUID v7 keys.
• Hardened the application with nonce‑based CSP, HSTS, SameSite cookies, least‑privilege database roles and server‑side entitlement re‑checks.
• Set the platform's founding decisions in the weeks after the repository opened in November 2025 — the layering, database‑first data access and the zero‑warnings bar — and they still hold nine months on.
• Built the payments and entitlements layer — Stripe alongside Apple and Google in‑app purchase — gating the directory, search and export through an 11‑table access model checked on the server.
• Moved slow work off the request path onto a River job queue — 15 worker modules, 8 scheduled tasks and 20 pg_cron jobs — so a request returns while the work behind it carries on.
• Built the loyalty and reputation system — 67 functions over a 31‑table ledger, with leagues, badges and a redemption shop — taking a row lock on the balance to close the double‑spend window.
• Built the platform's social layer — posts, feed, groups, mentions, a follower graph and an occasions digest — on the same function‑first data layer as the rest of the product.
• Built the hiring marketplace and the seafarer career workspace — 151 stored functions across 48 tables — covering vacancies, applications, certificates, rank progression and verified sea time.
• Generated 495 achievement pages across three languages from a read‑only SQLite export, with the page address authored as data so that correcting a sentence no longer moved the page and broke the link.
• Built a database‑driven CV, references, portfolio and cover‑letter generator in Python — 41 modules, 10,580 lines — rendering six output formats from one 23‑table SQLite source assembled by a 19‑step idempotent pipeline.
• Held the generator to 981 test cases at a 92% branch‑coverage floor with warnings treated as failures, and asserted idempotence by running the whole build pipeline twice from an empty file and requiring the second pass to change nothing.

This work is part of what we offer as Backend & API Development, Database Design & Modeling, Full‑Stack Product Development, GIS & Geospatial Solutions, Data Pipeline Development (ETL/ELT), Platform & Solution Architecture, Data Governance & Quality and Product Strategy & Requirements.

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