Hello, I’m
Data Analytics Professional — Business Automation & Solar Energy
Self-taught programmer and data analyst with nearly four years turning raw operational data into automated tools, actionable reports, and reliable pipelines — primarily in the renewable energy sector.
I am a data analytics professional and self-taught programmer based in Charlotte, NC. My work sits at the intersection of data analysis and practical software development: I design the tools that make data usable — custom monitoring applications, automated reporting pipelines, and API integrations — and I use that data to surface the insights that drive smarter decisions.
Currently working as a PV Performance Analyst at National Renewable Energy Corporation, I monitor a 42-site, ~250 MW solar portfolio, build Python-based tooling for our O&M team, and continuously look for ways to automate repetitive processes and improve data visibility across the organisation.
After the eMaint work-order dashboard I built proved its value and our team’s Ops Manager departed, I absorbed that role’s core responsibilities alongside my analyst duties — I’m now the primary point of contact for O&M customers and field technicians, schedule daily technician tasks, and own the work-order system directly.
I hold an MBA in Quantitative Methods of Business from Queen’s University of Charlotte, strengthening the bridge between technical data work and business strategy.
4 yr
Self-taught Development
MBA
Queen’s University ’26
PV
Solar Performance Analyst
Ops Mgr
O&M Point of Contact
Turning operational data into clear, actionable intelligence. I design reporting frameworks, KPI dashboards, and analysis tools that allow teams to monitor performance and make faster decisions. Hands-on experience analysing solar farm production data at scale.
Identifying manual, repetitive workflows and replacing them with reliable automated systems. From migrating Excel/VBA tools to Python to scheduling live data syncs between databases and cloud spreadsheets — I build automation that saves time and reduces human error.
Designing and querying relational databases to support real-time monitoring applications and operational reporting. Experience integrating SQL Server databases with Python applications and bridging legacy Access databases with modern tooling.
Primary point of contact for O&M customers and field technicians, owning the company’s work-order system end-to-end and setting daily technician task schedules — the operator’s side of the tools I build.
National Renewable Energy Corporation (NARENCO)
A full-featured Python desktop application built for CCC over a roughly three-month freelance engagement (Dec 2025–Feb 2026), replacing a fragmented, Excel-based manual workflow — project managers previously assembled purchase orders in Excel and re-typed the same data into a separate Excel sheet for cost tracking, then edited every document by hand off a template. The platform consolidates that into a single entry point that generates documents directly from the app. Scoped and designed directly with CCC staff, then handed off via a train-the-trainer rollout. Now used daily by 3–6 project managers processing an estimated 3–10 documents each per day, and still in active use and maintenance as of mid-2026.
A Python application integrating the Also Energies API with an SQL Server database to provide real-time performance monitoring and automated, contract-specific notifications for O&M field teams — cutting daily Control Center analysis labor from 6 hours to 1 hour.
Rewrote the company’s legacy Excel/VBA performance analysis tool in Python, connecting directly to Google Sheets for live collaborative reporting — cutting the full multi-site reporting cycle from 8–16 hours to 30–60 minutes.
A desktop GUI application built with tkinter that pulls and displays live wind data from weather APIs for solar farm sites. Later extended into a weather-aware daily scheduling planner for field technicians — cutting scheduling-check time from about an hour to under 5 minutes.
A Python bridge that keeps a legacy Microsoft Access database synchronised with a Google Sheet, giving a customer a live, always-current view of their data and fully automating a Control Center task that previously took 30 minutes per update.
An automated pipeline for the company’s eMaint work-order system: a Gmail API integration retrieves eMaint’s scheduled Excel export, Python re-analyzes and re-saves the data, and a scheduled job statically regenerates a searchable HTML dashboard with embedded charts — covering roughly 10,000 work orders/year and cutting administrative staffing for WO tracking from 3 people to 1, a 66% reduction. Its impact led to my taking on the team’s vacated Ops Manager responsibilities — I’m now the tool’s primary daily user as well as its builder.
A config-driven Python system (dataclasses defining per-plant parameters) covering 7 solar sites, pulling from an Access database via pyodbc and generating branded Excel forecast reports distributed by automated email.
A secure, fully static portfolio site — zero client-side JavaScript — deployed to Cloudflare’s global edge network via Cloudflare Workers. A small Worker script handles clean-URL routing (/data, /resume, etc.) and attaches hardened security headers (CSP, X-Frame-Options, and more) on every response.
Browse all of my personal tools, experiments, and automation scripts on GitHub.
View Profile →Queen’s University of Charlotte
Business Administration & Management — Quantitative Methods
Aug 2024 — May 2026 Completed
Wingate University
Analytical thinking and understanding human behaviour applied to data communication and stakeholder engagement.
Aug 2018 — May 2021
Three versions, same experience, different framing — pick whichever matches the role you’re looking at.
Open to opportunities, collaborations, and conversations about data, automation, or renewable energy analytics.