Experience

A chronological selector of my software engineering and data science career.

Data Analyst @ ASUITECH Solutions

Irving, TX (Remote/Onsite) Nov 2025 — Jun 2026
SQL Python Power BI Tableau Snowflake MySQL AWS Lambda MongoDB PowerShell
  • Developed interactive business intelligence dashboards and operational visual reports using Power BI and Tableau.
  • Wrote, tuned, and optimized SQL queries to query Snowflake databases, boosting report retrieval times.
  • Collaborated closely with cross-functional business stakeholders to translate specifications into visual metrics.
  • Identified key data anomalies, patterns, and trends, supplying reports to support executive planning.
  • Automated operational report compilation and ingestion workflows utilizing Python and PowerShell scripting.
  • Created strict validation rules to guarantee data format accuracy and consistency.

Software Developer @ DATA Bricks

Ellicott City, MD Mar 2025 — Nov 2025
Java Python SQL Spring Boot Hibernate Junit Git/GitHub Jira Agile CI/CD
  • Wrote, tested, and shipped clean backend code using Java, Spring Boot, and SQL databases.
  • Diagnosed, troubleshot, and patched complex application and environment bugs to maintain high availability.
  • Managed code deployments and version control branches inside Git/GitHub environments.
  • Produced thorough unit testing logic using JUnit to ensure stability prior to branch merge.
  • Participated actively in daily Scrum stand-ups, code reviews, and Agile project tracking via Jira.
  • Authored clear system design documents, setup guides, and REST API endpoints specifications.

Research Assistant @ University of Memphis

Memphis, TN (Data Science Dept.) Jan 2023 — Dec 2024
Python Pandas/NumPy Scikit-learn TensorFlow Apache Spark Docker AWS EC2/S3 MATLAB SciPy
  • Engineered and trained predictive ML models on large-scale datasets (up to 500GB) using Python (Pandas, Scikit-learn).
  • Pioneered extensive feature engineering and data validation pipelines, boosting model AUC scores by 15%.
  • Spun up, monitored, and optimized AWS EC2 compute instances and S3 storage configurations.
  • Architected Deep Learning models (TensorFlow/Keras) for complex time-series anomaly detection, achieving 92%+ accuracy.
  • Processed big data pipelines utilizing Apache Spark (PySpark) across distributed clusters.
  • Containerized ML environments with Docker, ensuring reproducible deployments from local hosts to cloud endpoints.
  • Conducted statistical significance analysis and hypothesis testing using SciPy.