I build data platforms, and the AI agents that run on them.

AT&T · Automated Platforms Development · Plano, TX

• Designed and shipped the INLAP AI Platform, a natural-language console over AT&T's Snowflake address dataset: users query by address or GLID and get a record view with DA/PSA boundary maps (MapLibre), Street View, and live serviceability metrics.
• Built a production agent chat layer with LangChain/LangGraph backed by a self-hosted small language model on Databricks, keeping inference and agent orchestration inside the governed platform boundary.
• Own and architect AT&T's Snowflake data platform behind nationwide U.S. address and serviceability pipelines that feed fiber build-out and network expansion planning.
• Reduced ETL runtime from 36 hours to under 20 minutes (97%) by re-architecting VM-based JavaScript workloads onto Databricks around partitioned distributed processing.
• Built ingestion for 300GB+ datasets via Snowpipe and Databricks–Snowflake connectors, with dbt models and Snowpark logic for anomaly detection, missing-data analysis, and national-scale address classification.
• Enforced governance across business units with Snowflake RBAC, column-level security, masking, and row access policies, the access controls that AI tools over enterprise data must respect.
• Leading a production migration from Snowflake native tables to Azure-backed Iceberg tables, moving enterprise storage to an open table format while validating ELT parity.
• Scoped ambiguous business requirements into shipped tools, and delivered architecture readouts and Streamlit dashboards translating platform tradeoffs into business value for executive stakeholders.

AT&T · Fraud ML · Plano, TX

• Owned and deployed the production automotive fraud detection pipeline on Databricks and PySpark end to end, flagging high-similarity credit applications at enterprise scale and cutting processing time 72% versus the legacy system, while working through messy application data with inconsistent formatting and incomplete records.
• Won an internal AT&T hackathon by building a low-latency connected-car telemetry pipeline integrating Palantir, Databricks, and Power BI for live anomaly detection, demoed to senior leadership.

AT&T · Advanced Analytics & Automation · Plano, TX

• Replaced manual executive reporting by building Python ETL pipelines that integrate Excel, Palantir, and enterprise data into Snowflake, cutting pipeline execution time 83%.
• Built time-series forecasting for Net Adds (92% R²) and regression models identifying fiber expansion opportunities; insights supported a penetration increase from 29% to 54% across the Greater Lakes region and informed capital allocation.