Databases

• Engineered hourly electricity consumption aggregation pipeline in Python / SQL / Bash + Jq, achieving 180ms for 30‑day datasets across heterogeneous JSONL sources.
• 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.
• Designed, deployed, and maintained 10 PostgreSQL and MS SQL servers on Ubuntu Linux VPS, ensuring optimal server performance and reliability.
• Enhanced data security by implementing 1,000 RBAC rules for developers, application instances, PostgreSQL, MS SQL, and other Linux servers, preventing unauthorized access; documented with Ansible automation.
• Accelerated PostgreSQL performance by 10x via strategic indexing, partitioning, and query optimization, enhancing database efficiency for user, tenant, geospatial, and time‑series electrical data.
• Automated 100 critical data backups using Barman, Google Cloud, Bash, and Python, ensuring data integrity across databases.
• 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.
• Automated data processing tasks using Shell scripting, PL/pgSQL, Python, and Transact‑SQL, increasing productivity and efficiency.
• Engineered 600 PL/pgSQL‑based ETL/ELT pipelines to streamline complex data processing workflows across multiple PostgreSQL development and production environments.
• 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.
• Adopted UUID v7 time‑ordered identifiers (PostgreSQL 18) as entity keys to reduce B‑tree index fragmentation and speed up queries.
• 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.
• Configured database backup retention as infrastructure‑as‑code, then audited the recovery position and documented the restore procedure — naming the remaining gaps rather than leaving them to be found during an incident.
• 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.
• Kept the schema honest across 1,022 migrations with a CI gate that builds the database both ways — a fresh install, and an install plus every migration — and fails when the two disagree.
• Built first‑party product analytics in PostgreSQL — 47 functions over partitioned event tables that prune themselves — pseudonymised behind a rotating salt and gated on the visitor's consent.
• 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.
• Wrote tests for the checkers themselves after establishing that a checker fed only clean input will one day report clean because it read nothing — planting a misspelling to confirm the spell‑check finds it, and taking an id range from the database rather than from a number in the test.

This work is part of what we offer as Backend & API Development, Database Design & Modeling, Data Pipeline Development (ETL/ELT), Database Administration (DBA), Data Governance & Quality, Full‑Stack Product Development, GIS & Geospatial Solutions and Database Performance Tuning.

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