Released 500 electricity and GIS data analysis reports, utilizing deep research and troubleshooting to ensure accurate geographic and time series big data insights.

Situation. The team managed vast datasets generated by smart‑meters installed across multiple geographic regions. These smart‑meters produced granular, time‑series electrical consumption data used by energy analysts, engineers, and regional planners for operational and strategic decision‑making.

Task. The responsibility was to produce high‑quality, transparent, and reproducible analytical reports that could uncover patterns in energy consumption, detect anomalies, and identify regional usage trends, while ensuring non‑technical stakeholders could easily interpret and reuse the findings.

Action. 500+ in‑depth data analysis reports were created and delivered, using pure SQL to perform all data extraction, transformation, and analysis tasks, working directly within cloud‑based environments such as PostgreSQL and BigQuery. The data included geolocation coordinates, meter IDs, timestamped energy usage, and environmental metadata. The SQL scripts featured CTEs, window functions, subqueries, and geospatial joins, allowing for scalable and efficient processing.

Each report included annotated SQL code, enabling colleagues and collaborators to fully reproduce and audit the research, which significantly reduced the time needed for follow‑up analysis. Troubleshooting notes were also added and common data quality issues documented, such as missing GPS coordinates or corrupted meter values, with recommended handling procedures.

Result. The reports became a standard reference across departments, aiding in regional load balancing, energy efficiency planning, and anomaly detection. By ensuring full transparency and reproducibility, the work helped improve stakeholder trust in the data and contributed to more accurate forecasting models and a 10–15% improvement in operational planning efficiency.