Led enterprise application integration and modern data stack architecture, connecting legacy infrastructure to cloud-native systems on BigQuery and Snowflake, with event-driven backbones on Kafka and Camel.
Seven years across three roles: cloud migration leadership, real-time IoT ingestion pipelines, and technical evangelism for AWS's newest services presented directly to enterprise leadership.
Network engineering and data center migrations early on, then two and a half years in Bangalore standing up and leading a 30+ person offshore operations team, then enterprise technical sales.
Ran technical deployment and inventory tracking for a nationwide Point-of-Sale hardware refresh across field sites.
AWS Lookout for Equipment is a managed ML service that ingests sensor and time-series data from industrial equipment and learns what normal operation looks like, so it can flag abnormal behavior early without requiring in-house ML expertise to build or maintain a model.
Illustrative data placeholder for real Lookout for Equipment output.
Geospatial Data Analytics on AWS distills patterns pressure-tested across engagements with some of the largest utilities and energy majors in North America not theory first.
A selection of technical blogs and projects, most written for AWS, that map directly onto energy-sector problems: spatial data at utility scale, real-time sensor and SCADA integration, and turning raw infrastructure data into something a team can actually query.
Combining a spatial engine with a data warehouse the same pattern behind joining asset location data to cost and consumption data at utility scale.
A low-cost, serverless pattern for publishing spatial datasets the same shape as serving public energy and climate data without standing up a GIS server.
Grid sensor (SCADA) data fused with geospatial asset data in one live dashboard written directly for a Power & Utilities audience.
Streaming IoT sensor data straight into spatial analysis the same real-time ingestion problem field equipment and smart-meter data pose.
Big-data spatial analytics across huge datasets the scale a national transmission or generation footprint actually needs.
A partitioning strategy for large geospatial datasets in a data lake or warehouse directly applicable to asset and outage data queried by region.
Sourcing public datasets (weather, climate, demographic) to enrich internal energy and load-forecasting models.
Climate-impact geospatial modeling for The Nature Conservancy the same "data into a decision" pipeline energy sustainability reporting needs.
Running an open-source LLM locally relevant wherever OT and utility networks can't call out to a cloud API.
An earlier, serverless version of the same retrieval-augmented approach behind the infrastructure RAG work above.