Summary
Data analyst with nearly four years of hands-on experience monitoring, analyzing, and reporting on a
42-site, ~250 MW solar portfolio for internal and external stakeholders. Builds custom analysis tooling
when off-the-shelf methods fall short — including a sun-position-adjusted shortfall analysis that
separates true underperformance from expected daily variation, and an ops-reporting dashboard that cut
administrative staffing for work-order tracking by 66%. Owns the full reporting lifecycle: exploratory
analysis, KPI design, and standardized customer-facing deliverables. Holds an MBA in Quantitative
Methods of Business, deepening the link between analysis and business decision-making.
Core Skills
Analysis & Reporting
Exploratory data analysis, KPI design, performance/shortfall analysis, monthly & quarterly customer reporting, standardized data visualization templates
Domain Expertise
Utility-scale solar performance monitoring, weather-adjusted production analysis, O&M operations support
Tools & Languages
Python (pandas, astral, scikit-learn), SQL, Excel (Advanced), Google Sheets, R / RStudio, VBA
Data & Databases
SQL Server / SSMS, Microsoft Access, Google Sheets API, Gmail API, REST API data sources
Communication
Stakeholder-facing deliverables, standardized reporting templates for internal and external audiences
Experience
PV Performance Analyst
Charlotte, NC
- Monitor solar farm production from the Control Center, identifying and escalating performance anomalies across a portfolio of 42 sites (~250 MW, anchored by one 70 MW site alongside 41 smaller 1–2 MW community sites).
- Analyze near real-time solar production data from a Python-integrated Also Energy monitoring platform (SQL Server backend), cutting daily Control Center analysis labor from 6 hours to 1 hour, an ~83% reduction.
- Designed the reporting logic behind an ops-management dashboard covering the company's ~10,000 annual work orders, giving administration and middle managers a searchable, chart-based view of work-order status without manual compilation — cut the administrative headcount needed for WO tracking from 3 people to 1, a 66% reduction.
- Built a sun-position-adjusted production shortfall analysis tool (Python, pandas, astral) that calculates expected sunrise/sunset and solar position per site coordinates to isolate genuine underperformance from normal daily variation.
- Author a recurring lineup of 7 combined performance/work-order reports and 25 work-order-only reports delivered to customers and internal stakeholders, translating raw production data into standardized, decision-ready deliverables.
- Migrated the legacy Excel/VBA performance analysis workflow to Python, improving analytical consistency and cutting the full multi-site reporting cycle from 8–16 hours to 30–60 minutes.
- Built a real-time wind-conditions monitoring tool, later extended into a weather-aware daily scheduling planner for 6 field technicians — cut scheduling-check time from about an hour to under 5 minutes and eliminated weather-related mis-scheduling entirely over 3 months of use.
- Designed standardized data visualization templates for O&M deliverables used by both internal teams and external customers.
General Laborer (promoted to Project Lead)
Charlotte, NC
- Promoted to lead a warehouse decommissioning project, coordinating work completion and punch-list management; role ended when the company dissolved and sister company NARENCO made a direct hire offer.
Selected Projects (github.com/jlang99)
Solar-Performance-Analysis
Analysis and reporting scripts behind monthly/quarterly customer performance reports, including sun-position-adjusted shortfall analysis and PVsyst expected-production comparisons.
Wind-Monitoring-App
Desktop tool tracking site-specific wind conditions for the O&M team, supporting rapid operational decisions during weather events.
EPC-Vegetation-Analysis
Exploratory pixel-color classification approach to detecting vegetation coverage on construction sites from drone imagery.
eMaint Ops-Reporting Dashboard (internal tool — not public)
Reporting logic and analysis behind a searchable, chart-based work-order dashboard used by administration and middle managers; reduced required admin staffing for WO tracking from 3 people to 1.
Education
Queen’s University of Charlotte
Master of Business Administration (MBA) — Quantitative Methods of Business
Aug 2024 — May 2026
Completed
Wingate University
Bachelor of Science — Psychology
Aug 2018 — May 2021