Eric Altenburg

Software Engineer

Hey, nice to meet you!

I'm a Software Development Engineer with 4+ years of experience at Amazon, building distributed systems and cloud infrastructure. I hold a B.S. in Computer Science with a Minor in Mathematics from Stevens Institute of Technology.

Most recently, I've worked on ad attribution systems for Amazon Live, AI-powered developer tools for AWS Q Developer (Kiro), and mission-critical API infrastructure for AWS CodeCatalyst serving millions of developers globally.

When I'm not writing code, you'll find me chasing the elusive powder day on the East Coast or staying up way too late reading a sci-fi/fantasy novel.

Skills

  • Languages

    TypeScript, JavaScript, Java, Python, GraphQL, SQL

  • AWS

    Lambda, DynamoDB, S3, SQS, Step Functions, CDK, CloudFormation, CloudWatch, Kinesis, Redshift, Athena, API Gateway, CloudFront, Route53, WAF

  • Technologies

    React, Node.js, Git, CI/CD, Distributed Systems

Experience

  • Amazon

    September 2021 - January 2026

    Software Development Engineer II

    • Amazon Live Creator Tools - Built ad attribution systems, event-driven data pipelines, and compliance infrastructure

    • AWS Kiro (Q Developer) - Optimized ML training data pipelines for AI-powered developer tools

    • AWS CodeCatalyst - Architected API infrastructure across 7+ AWS regions with 99.95% availability

  • Stevens Institute of Technology

    August 2019 - May 2021

    Computer Science Department | Algorithms and Programming Languages Course Assistant

    • Challenged with developing different approaches for explaining topics such as sorting, dynamic programming, greedy algorithms, and analyzing code complexity for students one-on-one

    • Led weekly programming labs and held office hours to provide extra help for struggling students

    • Graded exams and assignments on a weekly basis and communicated with students to resolve any questions about grading

  • Texas State University

    June 2019 - August 2019

    Pre-Flight Battery Consumption Model for UAV missions | Undergraduate Research Assistant

    • Collaborated on a team to produce a machine learning model capable of predicting drone battery consumption pre-flight

    • Build a classified decision tree with Python's scikit-learn to analyze raw flight data and produce a prediction for all the maneuvers a drone will perform during its flight

    • Evaluated the risk analysis of using different machine learning models such as random forests, boosted trees, neural networks, and decision trees to ensure the accuracy of the model while making sure the learning curve does not derail the project's timeline

Projects

  • RustGrad

    WORK IN PROGRESS

    Building a neural network training framework from scratch in Rust, implementing automatic differentiation and backpropagation to understand ML internals and systems programming.

    Rust Machine Learning Autograd Git

  • Shifts

    Built a full-stack stock discussion platform with real-time WebSocket price streaming and multi-threaded web scraping. Created with a team of four to facilitate discussions about stocks with live price updates.

    Source Node.js React GraphQL Firebase Socket.io WebSockets Git

  • Pre-Flight Battery Consumption Model for UAV missions

    Under the direction of Dr. Quijun Gu at Texas State University, a machine learning model was created with the aim to predict a given drone's maneuvers. From here, it was mapped to voltage curves in order to determine how much power would be consumed over the course of performing a maneuver. Thus, giving an estimate of the power to be consumed pre-flight. The findings were compiled into a paper which can be found below.

    Source Paper Python Classifier Decision Trees scikit-learn LaTeX Git

  • Lighthouse

    With a team of 4, we built a web application that allows students to better communicate with each other and professors. This is a redesigned variant of Piazza, Canvas, and Blackboard.

    Source Demo HTML CSS JS Node.js MongoDB Atlas Git

  • Modified Project Euler 43

    This differs from the original Project Euler 43 by allowing an input to be a pandigital number of any length. With a team of 3, we developed a solution which builds from the bottom up constructing a solution without having to generate all of the permutations. This method results in a fast execution time of less than 4 ms.

    Source Java Git

  • projectMASS

    MASS: MAc Settings Setup

    Script(s) used to configure a new Mac for development (Xcode, Homebrew, settings, applications).

    Source Bash Homebrew Xcode Git

  • projectME

    ME: Meet Eric

    What you're looking at! It's a little portfolio website put together to get to know me better.

    HTML Javascript Git

Resume

  • Eric Altenburg © 2026