Python

• Engineered hourly electricity consumption aggregation pipeline in Python / SQL / Bash + Jq, achieving 180ms for 30‑day datasets across heterogeneous JSONL sources.
• 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.
• Delivered 8 Power BI projects with comprehensive manuals, integrating Microsoft Power BI tools with NodeJS API and Python FastAPI for effective data analytics and visualization.
• Automated GIS SaaS application deployment, data processing, and reporting system using GitHub Actions CI/CD, Python, Bash, and SQL.
• Automated 100 critical data backups using Barman, Google Cloud, Bash, and Python, ensuring data integrity across databases.
• Led the development, deployment, and support of over 30 GIS projects, demonstrating expertise in PostgreSQL, Bash, Python, JavaScript, GDAL, ArcGIS, PostGIS, and Mapbox technologies.
• Streamlined data analysis and software development processes, saving 4,000 hours by introducing GitHub, GitLab, Bash, and Python CI/CD practices.
• Developed a Data Analytics reporting system, increasing quarterly software revenue by 400% through Python‑based PDF reports.
• Automated data processing tasks using Shell scripting, PL/pgSQL, Python, and Transact‑SQL, increasing productivity and efficiency.
• 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.
• 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.
• 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.
• Moved every user‑facing string out of Python into a content tree of 874 files across three languages, after finding dead translations nobody could see were dead and a check silently grading a third of the achievements.
• Built a cross‑language content check that fails when a translation drops a figure the English states, and when a language uses notation it does not use — finding two Danish descriptions missing a metric and sixteen Ukrainian spans quoting in the English style.

This work is part of what we offer as Data Pipeline Development (ETL/ELT), Backend & API Development, DevOps & CI/CD Automation, Data Analytics & BI Dashboards, Internationalization & Localization, Data Governance & Quality, Database Administration (DBA) and Database Design & Modeling.

Building something that needs this? Get in touch.