I’m a Senior Software Engineer focused on AWS-based data platforms and ML infrastructure. My career has taken me from data science and analysis into backend software engineering, where I now spend most of my time building the systems that make data and ML work reliably at scale.

Currently, I’m a Senior Developer at InfoBate, where I established the team’s AWS CDK codebase in Python from scratch, built and automated multiple data ingestion and reporting pipelines, and work directly with clients to design and deliver data features end-to-end. I’ve worked across services including S3, Glue, Athena, SageMaker, RDS, Step Functions, Lambda, and CloudWatch, translating messy business requirements into backend data views and React-based UIs.

I’m also the founder and sole developer of Sprig, an independent mobile app project I’m building from the ground up. Sprig encourages people to notice and photograph natural details in the world around them, with engagement mechanics like streaks and badges. I’m architecting the cloud infrastructure on AWS (RDS PostgreSQL, S3, DynamoDB) and building across the full stack — backend, infrastructure, and mobile.

Before InfoBate, I was a Software Development Engineer at Amazon, where I optimized an AWS-based data pipeline for AmazonGo handling 2M+ data points daily and designed a resumable Step Functions workflow that saved ~13 hours of compute per execution. Prior to Amazon, I was a Decision Scientist at Ibotta, where I automated data workflows using Airflow and refactored legacy PySpark pipelines.

I started my path in data science during undergrad, then spent a year teaching English in Vietnam before returning to the US and earning my Master of Information and Data Science at UC Berkeley. My graduate ML coursework is showcased in the portfolio below.

Outside of work, I’m an adventurer — I rock climb, ski, and mountaineer. I also converted a Dodge ProMaster into a campervan, which was one of the most rewarding projects I’ve taken on: learn, plan, build, repeat.

Skills & Technologies

Cloud & Infrastructure AWS CDK (Python), Lambda, Step Functions, S3, Glue, Athena, SageMaker, RDS, DynamoDB, CloudWatch, SES, API Gateway · Infrastructure-as-code · CI/CD pipelines

Languages Python · TypeScript · JavaScript · SQL

Data & ML PostgreSQL · PySpark · Airflow · data pipeline design · ML infrastructure

Frontend React · react-admin

Portfolio

The projects below are from my graduate work at UC Berkeley. For information about my more recent professional work, feel free to reach out at pattidegner@gmail.com or connect on LinkedIn.

Machine Learning Projects (Python)

Mountain Project Sentiment Analysis using Transfer Learning and DistilBERT

An end-to-end deep learning project for sentiment analysis of natural language. The goal is to label sentiment of Mountain Project forum posts to determine what gear climbers consider best. Covers webscraping, data cleaning, model selection, and final analysis. Solo project.

Audio Super-Resolution — Thesis Project

Uses GANs and Autoencoders to upsample low-quality audio in real time. Team project.

Car Damage Detection Part 1 — Image Classification

Uses traditional ML (KNN, Naive Bayes) and CNNs (with and without dropout) to classify images of cars as damaged or whole. Solo project.

Car Damage Detection Part 2 — Damage Localization

Uses Mask R-CNN to identify the location of damage on a car. Team project.