Summary
Analytics-focused engineer with nearly four years building the full path from raw data to decision-ready
output across a 42-site, ~250 MW solar portfolio: API ingestion, relational database design, transformation
logic, and automated delivery. Designed and maintain a config-driven, multi-site reporting pipeline, an
automated ops-reporting pipeline for a 10,000-work-order/year maintenance system, and a shared internal
Python utilities library used across a company's automation tooling. Comfortable owning a data system
end-to-end — from the database schema it reads from to the report a stakeholder opens. Holds
an MBA in Quantitative Methods of Business.
Technical Skills
Languages
Python, SQL, VBA, JavaScript (beginner), AutoHotkey
Data Pipelines & APIs
REST API integration, multithreaded data ingestion, scheduled/triggered jobs, credential & session-refresh handling, Google Sheets API, Gmail API
Databases
SQL Server / SSMS, Microsoft Access (schema design, table creation, pyodbc integration), SQLite, database-to-spreadsheet sync
Python Libraries
pandas, pyodbc, openpyxl, requests, BeautifulSoup, googleapiclient, scikit-learn, astral, tkinter
Engineering Practices
Config-driven system design (dataclasses), shared internal utility packages, legacy-to-modern migration (Excel/VBA → Python), Git/GitHub version control
Infrastructure
Linux, Nginx, Proxmox, Docker, Cloudflare
Experience
PV Performance Analyst
Charlotte, NC
- Built an automated ops-reporting pipeline for the company's eMaint work-order system: a Gmail API integration retrieves eMaint's scheduled Excel export on arrival, a Python script 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 (drawing on a database of 50,000+ historical records) and cutting the administrative staffing required for WO status tracking from 3 people to 1, a 66% reduction. In production since mid-2026.
- Designed and maintain a config-driven, multi-site solar forecasting pipeline (Python dataclasses defining per-plant parameters) that reads from an Access database via pyodbc and generates styled, branded Excel deliverables distributed by automated email — currently covering 7 solar sites. Also designed and manage the underlying Access database schema, including new-site onboarding and safe data reset/truncation scripts.
- Migrated the company's legacy Excel/VBA performance analysis workflow to a Python + Google Sheets API pipeline, removing manual export/refresh cycles and cutting the full multi-site reporting cycle from 8–16 hours to 30–60 minutes.
- Built a multithreaded API integration with the Also Energy monitoring platform and a SQL Server backend to ingest near real-time solar production data across a 42-site, ~250 MW portfolio, including automatic recovery logic for expired API credentials — cutting daily Control Center analysis labor from 6 hours to 1 hour, an ~83% reduction.
- Extended a real-time wind-monitoring tool into a weather-aware scheduling planner (Python + Google Sheets) used for daily technician scheduling — cut scheduling-check time from about an hour to under 5 minutes and eliminated weather-related mis-scheduling entirely over 3 months of use (previously ~10% of scheduled site visits).
- Built and maintain a shared internal Python utilities package (centralized credentials and email configuration, reusable data-handling functions) consumed by multiple internal automation tools.
- Engineered a live sync bridge between Microsoft Access and Google Sheets, fully automating a Control Center task that previously took 30 minutes per update.
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)
Also-Energy-Data-Real-Time-Data-Analysis
Multithreaded Python pull of live device data from a third-party solar monitoring API into a SQL Server database, with GUI front-ends for local and remote use.
M-Access-DB-Management
Schema and table-creation scripts, new-site onboarding, and safe truncation utilities for the Access database backing the monitoring platform.
NCC-Automations
Collection of production automation tools including the config-driven multi-plant forecasting system, technician data delivery, and a shared internal Python tools package.
eMaint Ops-Reporting Dashboard (internal tool — not public)
Gmail API pipeline that retrieves and re-analyzes eMaint's scheduled work-order Excel exports and statically regenerates a searchable HTML dashboard; cut 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