Electric Utilities
Built data pipeline that processes over 1.5 billion meter data points on a regular basis using Azure Databricks with PySpark. This involved a wide variety of data wrangling – eliminating bad data, deduplication, anomaly detection, load multiplication, meter isolation, and more. Data was stored in Parquet format for efficient downstream access.
Developed general purpose peak detection capabilities for meter data that enables discovery of peak loads during coincident load periods, potential loads from electric vehicles, and more.
Leveraged Azure Data Factory to integrate on premise data from NISC iVUE with the cloud.
Developed ingestion tooling using WebEx APIs, processed data, and reported on key customer service call metrics.
Built a variety of Power BI reports involving outage information, rate changes, line loss, transformer overloading, blinks, and more.
Built Power BI reports showing overall status of fiber installations as well current states of key workflow steps.
Developed the data back end for a public facing rate change calculator that members can use to see the impact of rate changes. This used data read from NISC iVUE and processed using Azure Databricks.
Dynamics ecosystem
Created multi-variate forecasting proof of concept for demand planning using Azure ML Auto ML. This project used a mix of R and Python.
Provided guidance on applying market basket analysis to a Dynamics ISV.
Created and delivered a training program – Data and Dynamics Bootcamp. This training involved 16 hours of instructor led training for 10 students at a Microsoft partner. The training provided an overall background on the fundamentals of data engineering and described how the Microsoft strategies of Fabric and Synapse Link fit into these fundamentals.
Technical management consulting
Provided hiring support from job description development through interviewing and selecting candidates.
Provided guidance on development process.
Provided guidance on product management.
Consulting in Engineering Service Provider in the Utilities Industry
Developed evaluation tooling to assess feasibility of AI projects. Applied tooling through two rounds of idea generation, prioritization, and project selection.
Supported a reinforcement learning project by developing synthetic data, exporting appropriate simulation data, and built Power BI reports to compare training runs.