Data Science & Computer Science Student, Data Engineering

Thomas Thomassen

Tinton Falls, NJ 551-***-**** **.*********@******.***

https://www.linkedin.com/in/thomas-thomassen-24114428a/ https://github.com/TJ5864 Education

Rutgers University — B.S. in Computer Science & Data Science New Brunswick, NJ, 08/2023– 12/2026, GPA: 3.6

Relevant Coursework: Data Structures, Design and Analysis of Computer Algorithms, Introduction to AI, Intro to Deep Learning, Applied Statistics, Software Methodology, Data Management for Data Science, and Relational Database Systems. Experience

Valley Bank Morristown, New Jersey

Data Engineering Intern June, 2026 – August, 2026

-Gained hands-on experience with Azure Data Factory, Databricks notebooks and workflows, SQL stored procedures, and Snowflake to automate ETL processes and data ingestion pipelines across the bank; worked with metadata management to optimize data workflows and streamline engineering processes.

-Automated Bing Ads and Meta Ads data ingestion pipelines using REST APIs and Azure Data Factory with parameterized scripts for flexible date windows; implemented error handling and retry logic in Databricks (PySpark) to ensure reliability; deployed Snowflake views in production supporting marketing dashboard analytics.

-Built an automated ETL pipeline loading Hootsuite social media data in weekly batches; used API calls to extract Instagram, Facebook, and YouTube post and profile data, stored in Snowflake, enabling marketing teams to create dashboards tracking cross-platform campaign performance.

-Evaluated View vs. Dynamic Table performance in Snowflake to optimize data mart query speed and pipeline efficiency. Insights used to improve system-wide performance. Little Red Riding Hood Inc Remote

Rutgers Master of Business and Science Externship Project Lead May, 2025 – August, 2025

-Led a 3-person data team analyzing hospital budget and transportation datasets to identify market opportunities for an elderly mobility startup, delivering findings directly to company leadership

-Built automated ETL pipelines in Python to scrape, clean, and process large-scale healthcare and transportation datasets, reducing manual data prep time and accelerating the team's ability to surface market opportunities for leadership review. Projects

Fantasy Football Draft Assistant— Python, Streamlit, OpenAI - 07/2025

-Built a Python-based Fantasy Football Draft Assistant that scrapes CBS Sports projections, cleans data with pandas, and provides an interactive draft interface with real-time, PPR-optimized recommendations.

-Developed modular data pipelines, integrated AI-powered suggestion logic, and used Git/GitHub for version control, deployed as a fully functional Streamlit web application.

-Implemented data-cleaning and feature-selection logic to rank players using PPR-adjusted scoring heuristics and position-based value algorithms, dynamically updating recommendations in real time as draft picks were made. Relational Database — SQL, MySQL - (03/2026 - 05/2026)

-Designed and implemented a relational database schema with 10-15 tables modeling a loan application system, defining entity relationships through primary and foreign keys connecting applicants, co-applicants, and properties.

-Applied normalization principles to eliminate redundancy and optimize query performance across joined tables. and wrote complex SQL queries to extract and analyze loan application data across related entities.

-Integrated a locally hosted Ollama LLM with the application backend to translate English prompts into SQL, execute queries against the database, and return formatted results to the user.

Breast Cancer Symptom Checker Using Bayesian Networks— Python, Tensor Flow, Scikit-Learn - 12/2025

-Co-developed a Bayesian network classification model using the UCI Breast Cancer Wisconsin dataset, engineering 30 diagnostic tumor features to predict malignant vs. benign diagnoses with TensorFlow, Scikit-Learn, and Pgmpy.

-Visualized model performance and diagnostic feature distributions using Matplotlib and NumPy to evaluate classification accuracy across the dataset. Skills

Languages: Python, Java, SQL, R, C, HTML, MATLAB

Cloud & Tools: AWS (Certified Cloud Practitioner), Git/GitHub, Power BI, Tableau, Snowflake, Odoo, Excel

Frameworks & Libraries: TensorFlow, Scikit-Learn, Pandas, NumPy, Streamlit, OpenAI API Concepts: ETL/ELT Pipelines, Data Modeling, Data Cleaning, Web Scraping, REST APIs, OOP, Bayesian Networks, Data Wrangling, Cloud Computing Certifications

AWS Certified Cloud Practitioner

-Amazon Web Services August 22, 2024 - August 22, 2027

-Knowledge of core AWS services, cloud architecture, and security fundamentals

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