Neal Chaudhary, PhD, Developer in Los Angeles, United States
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Neal Chaudhary, PhD

Verified Expert  in Engineering

Data Engineer and Developer

Los Angeles, United States
Toptal Member Since
April 4, 2023

Neal is a seasoned data engineer with strong quantitative skills and experience over the software lifecycle, including data pipelines, big data, investment risk, software architecture, and tech debt management. He is proficient in Python, SQL, AWS, Snowflake, Airflow, R, and C++ and skilled in data visualization using Apache Superset, AWS QuickSight, ggplot2, and Shiny. Neal holds a PhD in mathematics, an MS in operations research from UCLA, and a BS in applied math from Caltech.


Apache Airflow, Python 3, Snowflake, Amazon RDS, MySQL
First Quadrant
Python, R, Bash, MySQL, Linux




Preferred Environment

Linux, Bash, R, Python, C++, SQL

The most amazing...

...project I've worked on refactored a legacy Python and MySQL code, significantly reducing LOC and increasing reliability and flexibility.

Work Experience

Senior Back-end Engineer

2021 - 2023
  • Participated in the development and code review of an ETL data pipeline using AWS, Snowflake, Airflow, S3, and RDS.
  • Developed data quality monitoring functionality using Python, RDS MySQL, Snowflake, and EC2.
  • Built the orchestration setup, checklists, and documentation for Airflow (MWAA), AWS SecretsManager, EC2, and RDS MySQL deployment.
Technologies: Apache Airflow, Python 3, Snowflake, Amazon RDS, MySQL

Associate Director, Risk Office

2013 - 2021
First Quadrant
  • Refactored the risk codebase to increase observability, reliability, and flexibility using Python, XML, and MySQL.
  • Developed a framework for investment risk compliance monitoring with Python.
  • Performed exploratory data analysis, visualization, and research on investor sentiment.
Technologies: Python, R, Bash, MySQL, Linux

Web API Reliability Design Example
This project showcases a web API that can coalesce numbers from many upstream APIs to summarize health insurance plan information. This API gains both flexibility and reliability through software design. Specifically, dependency inversion is the software design principle of separating higher-level business logic from lower-level implementation details. Here the process of querying upstream APIs is separated from the higher-level logic of coalescing plan data.

For example, the API can flexibly switch between the requests or urllib libraries through provided adaptors; still, other libraries or methods could also be added by simply creating a new adaptor that implements the required interface, so the API can easily be updated or customized to use new or different APIs without requiring changes to the higher-level business logic.

Furthermore, this separation of concerns also enables the testing framework to provide simulated results without modification, allowing for easier testing and more robust code. In a production environment, the adapter choice could be defaulted or wrapped with a particular preference for end-users, providing even more flexibility and adaptability to the API.
1998 - 2004

PhD in Mathematics

University of California, Los Angeles - Los Angeles, CA, USA

1996 - 1997

Master's Degree in Operations Research

University of California, Los Angeles - Los Angeles, CA, USA

1990 - 1994

Bachelor's Degree in Applied Math

California Institute of Technology - Pasadena, CA, USA




Apache Airflow, Pytest


R, Python, C++, Bash, SQL, Snowflake, Python 3








Amazon RDS, Finance, Simulations, Monte Carlo Simulations, Derivatives, Operations Research, Signal Processing, Programming

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