Richard Diaz-Cool | Analytics Engineer

About

Astronomer turned analytics engineer.

I specialize in the full stack of analytics—from Spark/PySpark data engineering to React-based tools and predictive models. Recently, I've built systems processing 3B+ daily records, real‑time alerting that protects millions of customers, and forecasting models that drive strategic decisions years ahead.

Experience

Senior Analytics Engineer

Netflix, Los Gatos, CA

9+ years

Powered Netflix's data infrastructure evolution through innovative analytics solutions, device ecosystem forecasting, and strategic engineering insights. Delivered end-to-end data pipelines, monitoring systems, and ML-powered quality models that inform strategic planning and protect millions of customers through early issue detection.

•Created innovative data solutions allowing complex custom cardinality calculations in order to understand the reach of potential UI features based on their underlying technical requirements and logged device capabilities
•Shipped end-to-end analytics: Spark-powered ETL pipelines fronted by responsive dashboards and tooling using React and Tableau for high-dimensional data exploration
•Developed forecasting for device ecosystem to inform strategic planning, retirements, and engineering investments
•Built ML models to tag device quality alerts; reducing false positives and saving ~0.5 FTE annually
•Designed monitoring systems for partner firmware rollouts; surfaced critical issues early, protecting millions of members

Staff Scientist

MMT Observatory, Tucson, AZ

3 years

Engineered critical systems for world-class astronomical facility, developing automation and ML-powered monitoring solutions that optimized telescope operations and prevented equipment failures.

•Automated nightly image processing and quality metrics; enabled real-time oversight and reduced manual effort.
•Implemented clustering-based performance monitoring; prevented critical equipment failures.
•Optimized observing schedule with logistic regression; ~20% reduction in observation overhead.

Postdoctoral Research Fellow

Carnegie Observatories & Princeton University, Pasadena, CA / Princeton, NJ

4 years

Led large-scale astronomical data analysis initiatives, building automated processing pipelines and research tooling that accelerated scientific discovery and improved reproducibility across the field.

•Analyzed millions of galaxies using statistical and ML methods; led large-scale data processing initiatives.
•Built archives and tooling that accelerated research access and reproducibility.
•Produced peer-reviewed results grounded in rigorous statistical methods.

Projects

Tooling to Understand Potential Feature Reach

The CE device ecosystem is a complex system with many moving parts. I build tooling to help understand the reach of potential new features by measuring the cardinality of an arbitrary set of technical requirements and logged device capabilities. Data volume exceeds 3 trillion rows of data per day. The dashboard is fronted by a custom React UI and powered through ThetaSketch in Druid. Measured reach values are augmented with ML forecasts into the future to help prioritize feature development.

PySpark

React

Druid

Prophet

Partner Firmware Monitoring

Build an early detection and alerting framework to monitor the in-field performance of devices running new firmware versions. This allowed the device reliability team to identify and address issues before they became critical saving impact on millions of customers.

Netflix Tech Blog

Statistical Analysis

Metaflow

Python

Community Impact

Helpline Operations & Reporting

Coordinated scheduling and onboarding for a community recovery helpline. Built monthly engagement reporting and training materials to improve coverage and service quality.

Membership Survey Analytics

Led survey design and data processing; created dashboards to analyze trends and inform program priorities while preserving participant anonymity.

Resume & Publications