SQL
19 achievements

Photo: 422737 / Pixabay 
Photo: AnnasPhotography / Pixabay
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A month of meter readings in 180 milliseconds
Engineered hourly electricity consumption aggregation pipeline in Python / SQL / Bash + Jq, achieving 180ms for 30‑day datasets across heterogeneous JSONL sources.
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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.
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Improved GIS map performance 10x on PostgreSQL
Improved geographical map application performance by 10x through strategic database transition from MSSQL to PostgreSQL, including the data cutover, optimizing processing and data security.
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Resolved 1,000 issues in geographical data
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.
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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.
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Released 500 electricity and GIS data reports
Released 500 electricity and GIS data analysis reports, utilizing deep research and troubleshooting to ensure accurate geographic and time series big data insights.
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Ran 100 PostgreSQL, MS SQL, BigQuery warehouses
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.
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Accelerated PostgreSQL performance by 10x
Accelerated PostgreSQL performance by 10x via strategic indexing, partitioning, and query optimization, enhancing database efficiency for user, tenant, geospatial, and time‑series electrical data.
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Automated GIS SaaS deployment with GitHub Actions
Automated GIS SaaS application deployment, data processing, and reporting system using GitHub Actions CI/CD, Python, Bash, and SQL.
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Led 30+ GIS projects from build to support
Led the development, deployment, and support of over 30 GIS projects, demonstrating expertise in PostgreSQL, Bash, Python, JavaScript, GDAL, ArcGIS, PostGIS, and Mapbox technologies.
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Automated data processing in Python and PL/pgSQL
Automated data processing tasks using Shell scripting, PL/pgSQL, Python, and Transact‑SQL, increasing productivity and efficiency.
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Engineered 600 PL/pgSQL ETL/ELT pipelines
Engineered 600 PL/pgSQL‑based ETL/ELT pipelines to streamline complex data processing workflows across multiple PostgreSQL development and production environments.
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Designed a JSON passthrough from Postgres to API
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.
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Designed a function‑first PostgreSQL data layer
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.
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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.
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Built first‑party analytics inside PostgreSQL
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.
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Built a loyalty wallet, leagues and badges
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.
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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.
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Built a database‑driven CV generator
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.
If this is the work you need done, the next step is a conversation.
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This work is part of what we offer as Data Pipeline Development (ETL/ELT), Database Design & Modeling, GIS & Geospatial Solutions, Backend & API Development, Data Governance & Quality, Database Performance Tuning, AI Data Readiness and Database Administration (DBA).
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