William Gonzalez.

Leading GCP modernization across data, ML, and internal platforms — metadata-driven systems internal teams run themselves.

• Lead enterprise GCP modernization — data infrastructure, ML systems, and internal tooling across the company.Enterprise-scale cloud platform ownership
• Standardized metadata-driven architectures — business and ops teams change workflow logic without engineering tickets.Self-service ops for non-engineers
• Centralized React + Python apps on App Engine + BigQuery — pipeline monitoring, log/error analysis, data validation, metadata control, reporting.One UI replaces a wall of dashboards

Cloud architecture and automation for enterprise data and cloud-native deployments.

• Scalable GCP architectures for enterprise data operations and cloud-native deployments.Production-grade cloud-native platforms
• Infra automation and engineering standards — predictable releases, less drift between environments.Predictable, repeatable releases
• Cloud platform optimization — scalability, observability, production reliability.Stronger SLOs and operational visibility

Led the company's migration from legacy Alteryx / MySQL to a cloud-native GCP stack — data engineering, ML, reporting, internal apps.

• Led company-wide migration from Alteryx / MySQL to cloud- native GCP — Python, BigQuery, Airflow, PySpark, Terraform, Dataproc. POC scaled to production architecture.$100M+ org adopted the proposed stack
• Rebuilt ROI reporting end-to-end on Django + BigQuery + Python — on-demand, formatted, no human in the loop.30 min → 20 s per report · 8 h → 2 min company-wide
• Automated mover modeling on PySpark + Airflow — parallel training, retraining, and scoring driven by metadata, with imbalance correction built in.100+ client campaigns in parallel · terabytes / week

Statistical and predictive models for investment research.

• Python financial models estimating corporate performance across multiple sectors.Models drove sector-level investment calls
• Quant research using Bloomberg, FactSet, SEC filings, and alternative data sources.
• Equity research reports backed by proprietary models and statistical insight.

ML research for quantitative finance and deep hedging.

• Deep Hedging models with recurrent neural networks to hedge Delta.Novel RNN-based hedging research
• ML applications for quantitative finance and hedging strategies.
• Predictive modeling on structured + unstructured financial data.

Financial risk systems and reporting automation.

• Automated Reuters data retrieval — Python + Jupyter + SQL Server with Slack alerts, replacing manual workflows.~€60K saved annually
• Risk calculation systems in Java, Excel VBA, Wolfram Mathematica, Python — robust, scalable, audited.Production risk engine
• Eliminated repetitive financial ops — faster reporting cycles, lower error rate, more analyst time on analysis.Hours of manual work / week recovered

Banking infrastructure and reporting operations.

• Automated reporting cycle with VBA tooling for a major banking client.3 weeks → 1 day
• Project manager on the daily GTR project — primary liaison between bank managers and the IT team, weekly reporting to Executive Board.Owned client comms for largest GTR account
• Coordinated 8 bank departments to standardize income data from 14 sources into a unified ETL format.14 source systems unified