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.
Portfolio
Experience
- C++ - 15 years
- Linux - 15 years
- Bash - 15 years
- R - 15 years
- Python - 15 years
- MySQL - 10 years
- Snowflake - 2 years
- Apache Airflow - 2 years
Availability
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
EDO
- 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.
Associate Director, Risk Office
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.
Experience
Web API Reliability Design Example
https://github.com/nealchau/coalescenceFor 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.
Education
PhD in Mathematics
University of California, Los Angeles - Los Angeles, CA, USA
Master's Degree in Operations Research
University of California, Los Angeles - Los Angeles, CA, USA
Bachelor's Degree in Applied Math
California Institute of Technology - Pasadena, CA, USA
Skills
Libraries/APIs
Flask-RESTful
Tools
Apache Airflow, Pytest
Languages
R, Python, C++, Bash, SQL, Snowflake, Python 3
Platforms
Linux
Storage
MySQL
Frameworks
Flask
Other
Amazon RDS, Finance, Simulations, Monte Carlo Simulations, Derivatives, Operations Research, Signal Processing, Programming
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