About the Company
GE Vernova builds wind turbines and hybrid energy platforms globally. I worked in the Wind Resource Assessment (WRA) engineering team, which handles meteorological data modeling (SODAR, met-masts) for site selection and turbine performance.
My Journey There
I was responsible for maintaining PyWRA, GE's internal Python library for wind data calculations, along with its custom Plotly visualization wrapper. A big part of the work was routine engineering hygiene: fixing CVE security flaws, tracking regressions, and keeping core mathematical modules stable.
I led the migration of core services from Python 3.7/3.9 to 3.11 and pandas 1 to 2. Upgrading pandas isn't a simple version bump — behavioral changes break subtle logic. During the audit, I caught and fixed an elusive bug in wind rose plot rendering that had been quietly slipping past earlier releases.
On the infrastructure side, I modularized Airflow on GPU Kubernetes, split Celery into soft and heavy Redis queues to prevent long calculations from blocking fast APIs, built a date parser for messy field uploads, and set up Grafana/Loki/Prometheus to capture real telemetry before and after migrations.
Key Deliverables & Architectures
- [01]PyWRA Scientific Library: Maintained PyWRA (GE's internal Python/NumPy/pandas scientific computing engine) and its custom Plotly charting wrapper. Handled CVE security patches, regression tracking, and performance fixes in core math modules.
- [02]Python 3.11 & pandas 2 Migration: Led service migrations from Python 3.7/3.9 to 3.11 and pandas 1 to 2. Discovered and fixed an elusive, long-standing rendering bug in wind rose charts that previously caused silent visual inaccuracies.
- [03]Celery Queue & API Partitioning: Re-architected Celery workers by isolating soft vs. heavy tasks into dedicated Redis queues and distinct API endpoints with parallel execution, eliminating queue starvation.
- [04]Resilient Datetime Normalization: Built a deterministic datetime parser to handle wild field formats from disparate engineering tools, guaranteeing zero null gaps in time-series data.
- [05]Empirical Telemetry (Grafana Stack): Deployed Grafana, Loki, and Prometheus on GPU Kubernetes clusters to record empirical telemetry baselines before and after migration, replacing guesswork with verifiable metrics.