Who am I?
A little about me
I am a data engineer with a foundation in data science and a focus on distributed systems. I design production platforms, streaming and batch pipelines, databases, and analytical tools that make large-scale data easier to trust and use.
At Tesla, I have led projects from proof of concept through production - improving query latency, automating engineering workflows, and enabling real-time observability for hardware test programs.
Skills
- Languages:
- Python, SQL, Java
- Databases:
- Iceberg, ClickHouse, PostgreSQL, MySQL
- Libraries:
- PySpark, Pandas, Polars, FastAPI, Plotly, Dash, Scikit-learn, XGBoost, TensorFlow, PyTorch
- Platforms & Tools:
- Dagster, Airflow, Spark, Flink, Kafka, Docker, Kubernetes, Grafana, Git, Power BI, Tableau
Experience
Data Engineer
Tesla - Palo Alto, CA
January 2025 - Present
- Eliminated 20+ hours of manual work each week by designing and owning a production Dagster orchestration platform on Kubernetes, delivering 30+ automated ETL, anomaly detection, and real-time alerting pipelines.
- Reduced time-series query latency by 20x by leading the migration from Apache Iceberg to ClickHouse, scaling the platform to 100B+ rows and accelerating analysis for test and design engineering teams.
- Cut end-to-end data latency from 30 seconds to under 500ms with a PyFlink and Kafka pipeline processing 3M+ messages per minute across 100+ hardware test devices.
- Centralized traceability for 50K+ vehicle parts by designing a MySQL metadata model and FastAPI service that replaced manual tracking workflows across 100+ devices.
- Built and maintained 20+ production Airflow and Spark pipelines processing petabytes of vehicle test data, integrating Iceberg, MySQL, PostgreSQL, and other sources.
- Migrated Dagster, FastAPI, and Flink services to Kubernetes with GitHub Actions CI/CD, then implemented Prometheus and Grafana observability across the platform.
Data Science Intern
Tesla - Palo Alto, CA
June 2024 - December 2024
- Built an internal Plotly Dash application on Kubernetes for hardware-test device usage, KPI monitoring, and statistical analysis.
- Improved oil-pump reliability by 50% by deploying an Isolation Forest model that identified manufacturing defects and supplier variability.
- Analyzed 10B+ fleet records to isolate vehicle fault root causes, then built a self-service Bokeh Panel and Trino dashboard for engineering teams.
Data Engineer Intern
EverCharge - Palo Alto, CA
June 2023 - September 2023
- Identified previously unknown hardware, software, and installation issues affecting 5-10% of products through time-series analysis, statistical inference, and anomaly detection in Python and SQL.
- Built Retool dashboards for device-performance KPIs and improved their responsiveness through PostgreSQL query optimization.
- Published three stakeholder-facing data science reports and developed a proof-of-concept anomaly detection model for abnormal device behavior.
Data Science Intern
HM Electronics - Carlsbad, CA
June 2022 - September 2022
- Analyzed customer-device telemetry to surface production-quality issues and strengthen customer support.
- Extracted semi-structured console logs from 100+ devices into Azure Blob Storage and automated ETL across 30M+ records with PySpark on Databricks.
- Built classification and clustering models to predict device failure and isolate indicators for technical support teams.
- Presented findings built with Python, SQL, and Power BI during Agile sprints, informing product and software engineering decisions.
Education
Columbia University
January 2026 - Present
M.S. Computer Science · Distributed Systems Specialization · Remote, part-time
University of California, San Diego
September 2021 - March 2025
B.S. Data Science
Projects
Mitigating Gender Bias In Coronary Heart Disease Prediction
(Capstone Project)
December 2024
- Designed and implemented a feed-forward neural network to predict Coronary Heart Disease (CHD) using CDC’s NHANES dataset with over 37,000 patient records and 35 key features.
- Integrated an adversarial debiasing framework to mitigate gender bias by penalizing correlations between predictions and gender.
- Improved balanced accuracy scores from 0.71 to 0.77 and reduced equal opportunity difference by 95.6%.
- Contributed a reusable machine learning framework for bias mitigation applicable to broader healthcare AI models.
E-commerce Recommender System
December 2023
- Developed a machine learning model catered for an e-commerce recommender system using Sci-kit Learn, H2o AutoML, and tensorflow
- Performed exploratory data analysis, feature engineering, model training and validating, and hyperparameter tuning in order to obtain a model with an accuracy, F1-score, and ROC-AUC of 0.71, 0.72, 0.79 respectively.
- Deployed the finalized Collaborative Filtering Tensorflow model using Flask and HTML to emulate a real-world recommender system.
Real-Time Weather Data Dashboard
April 2023
- Constructed dynamic dashboards visualizing real-time weather updates using Tableau, MongoDB, and weather APIs.
- Highlighted temperature, humidity, and other attributes using Apache Airflow to build an ETL batch processing pipeline that ingested, transformed, and loaded the data while performing various quality checks.
Popular Youtube Videos Title Generator
March 2023
- Utilized Youtube APIs to analyze recent top 50 trending videos and predict a new possible trending video title.
- Established an ETL pipeline to populate an SQLite database after processing words using Pandas and NLTK.
- Generated possible trending video titles using sentence linguistics, unigram NLP models, and N-gram NLP models.
Recipe Ratings Analysis and Predictor
February 2023
- Analyzed 234,429 different recipe reviews given on Food.com to discover underlying associations and trends regarding rating.
- Concluded statistically significant results using hypothesis testing and after performing exploratory data analysis.
- Developed a Decision Tree model using CV grid search after testing models using SKLearn pipelines.
Best Companies to Work For
July 2021
- Showcased the top 500 best companies to work for in 2021 according to Forbes.com using Python and SQL.
- Extracted and transformed data using web-scraping and data processing to store into an SQLite3 database.
- Showcased the top 500 best companies to work for by utilizing embedded-SQL queries and Matplotlib.
A Programmer's Pay Analysis
June 2021
- Performed EDA using Python to investigate a programmer’s salary and contributing factors
- Assembled Pandas, NumPy, and Matplotlib to clean, query, and visualize over 64461 data entries.
- Concluded positive associations between pay and hobbyist coding, language, education level, and experience.
