Data an AI can actually use

Selected work demonstrating this service.

• Made a geographical data pipeline 50x faster

  Accelerated geographical data pipeline performance by 50x by improving SQL programming and data modeling across PostgreSQL, MS SQL, and Google Cloud BigQuery.

• Designed and ran 6 ETL/ELT pipelines

  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.

• Modeled 348 tables across 34 Postgres schemas

  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.

• 1,022 migrations behind a schema drift gate

  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.

• Generated 495 pages from a SQLite export

  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.

• Replaced 963 metadata blocks with one graph

  Replaced 963 anonymous structured‑data blocks that restated the company 1,671 times with one linked graph of 16 types minted from stable origin identifiers.