# Travis Theall ## Email travis@tntheall.com ## Profile Travis Theall is an experienced Data Engineer with a primary focus on Python Development on the AWS cloud. He is trained in Data Analytics, Data Science, Data Visualization, and Process Analytics and has worked on many multi-site research studies. He has worked on all aspects of the software development lifecycle, from product planning to product release. He is proficient in Python, JavaScript, SQL, and NoSQL languages. ## Education - B.S. in Kinesiology, Louisiana State University, US, 2017 - B.S. in Dietetics, Louisiana State University, US, 2017 ## Digital Credentials - SnowPro Core Certification – 2026 - AWS Certified Cloud Practitioner – 2025 - AWS Certified Solutions Architect – Associate – 2025 - AWS Certified Data Analytics – Specialty – 2023 - Data Science Professional Certificate – 2021 - Databases and SQL for Data Science – 2020 ## Key skills Amazon Web Services (AWS), Data Analysis, Git, Gitlab CI/CD, Jenkins, Linux, Node.js (JavaScript), NoSQL, Python, RESTful APIs, and SQL ## Languages English – Fluent ## Work experience ### Data Engineer > IBM, United States | Nov 2021 – Present At IBM, I have primarily worked as a back end data engineer using python and some web frameworks like Django, Flask, or FastAPI. I am proficient at writing python and knowledgeable about the entire python ecosystem. I typically use an object relational mapper (ORM) to interact with databases like the Django ORM or sqlalchemy. However, I do use custom SQL when required. I have worked on deployment pipelines using Jenkins and Gitlab CI/CD. The primary cloud provider I work with is AWS; however, much of my client work has focused on using hybrid deployments to both cloud and on-premise. I have worked on dependency update projects from full stack web development projects to data analysis projects. I have worked at all levels of software maturity from starting a project from scratch to deploying to production at Morgan Stanley to maintaining a legacy monolith at Doordash. ### Research Specialist > Pennington Biomedical Research Center, United States | Nov 2016–Aug 2021 I started my career as a Student worker doing data entry and conducting study visits at PBRC. My passion for dietetics research was clear, and I was offered a full-time position as a Research Specialist after graduating from LSU in December 2017. While working at Pennington, I helped manage multiple studies with various universities across the globe. My lab specifically worked with the Remote Food Photography Method (RFPM). Essentially, participants would undergo various dietary protocols and would send my lab pictures of their food to monitor dietary adhesion to the protocol. It was my job to estimate participant intake based on their pictures and their descriptions of their food. I was fascinated with how we were quantifying dietary intake using the Food and Nutrient Database for Dietary Studies (FNDDS). I had always tracked my own nutrient intake with various tracker apps like MyFitnessPal, but the FNDDS was different. With the FNDDS I could see how every ingredient came together to form the whole food. They mapped every micro-nutrient and macro-nutrient to various studies and methodologies used to derive those values. I wanted to share my understanding of how all the various ingredients came together to form whole foods, and how we could more efficiently track participant meal intake with my coworkers. I started to learn web technologies so that I could host and share my app. This lead me to getting more involved in the actual software development lifecycle (SDLC) of the RFPM application and our new Data Capture and Analysis platform. My participation in the DCAP was the catalyst That drove me to getting certified in Data Analytics and transitioning to work for IBM. ## Publications - Electronic Cigarette Nicotine Flux, Nicotine Yield, and Particulate Matter Emissions: Impact of Device and Liquid Heterogeneity, 2022 - Electronic cigarette heterogeneity, nicotine flux, and total particulate matter emissions: Development of a smartphone app to measure device and liquid characteristics., 2021 ## Assignment history ### Senior Application Developer – Experience Fullstack > Elevance Health Inc, United States | Dec 2025 – Present > Project name: Hub for Experience Analytics (HEXA) Creating a dashboard for to display analysis of various survey statistics. #### Tech stack Python, FastAPI, Snowflake #### Contribution - Added survey metrics for tracking digital adoption. - Added stoplight visualization for various KPIs. ### Python Application Developer – AWS Cloud Modernization > FANNIE MAE, United States | May 2024–Nov 2025 As part of ongoing cloud modernization, this project is setting up scripts and infrastructure to automate configuration deploys nodes, located both on premises and in the cloud. This project primarily focuses on Synthetic Monitoring, Real User Monitoring, and Internet / Application performance Monitoring. The goal is to have a seamless pipeline that 1. Takes in MyServices request tickets. 2. Updates corporate infrastructure when it is allowed. 3. Contacts the necessary teams to update infrastructure when it is not allowed. 4. Synchronize application ID password rotations with SAAS vendor and internal password vaults. Detect and deactivate monitors when an application ID becomes out of sync. 5. Generate monitoring scripts from arbitrary HTML, JSON, or XML based on the type of application / service being monitored. 6. Schedules script runs per application team specifications and application maintenance windows. 7. Sets up alert groups, levels, severity, and conditions. 8. Removes all previous set-ups when a monitor is off boarded. #### Primary Technologies Used MyServices, Jira, Catchpoint, AWS (EC2, S3, ECS, SNS, SQS, RDS, Lambda), Python, Node.js, GitLab CI/CD, Jenkins CI/CD & Terraform #### Contribution - Creating an automated on-boarding pipeline that gets requirement information from application teams and generates monitoring scripts in either vanilla JavaScript or using the JavaScript Playwright package. - Helped write a Selenium to Playwright conversion tool for automating conversion of 1000+ Selenium scripts. - Created GitLab / Jenkins pipeline for automated unit testing and code deployment to 30 different web servers with varying Linux operating systems, both in the cloud and on premise. - Automated proxy error detection and proxy updates for monitoring internal services. - Automating firewall error detection and Network Team MyServices ticket requests for monitoring internal services. - Automated creating REST API client and data models from swagger API specification documents for working with vendor APIs. ### Full Stack Web Developer > Morgan Stanley | Sep 2023 - May 2024 We are modernizing three legacy Perl applications that monitor commands issued on mainframe hardware consoles. The applications' primary focus is audit tracking approvals/rejections. #### Contribution - Updated the legacy Perl Common Gateway Interface web application into a Flask (Python) backend API and an Angular (TypeScript) frontend. - Creation of reusable Python/Node.js packages for use throughout the updated applications. - Creation of batch-processing scripts that analyze the various XML/raw text console logs. - Email notification of relevant personnel for missing/late reports. - Data validation on extracted log content. - Creation of CI/CD pipelines for automated code deployments. ### Python Django Developer > IBM – Internal, United States | Apr 2023–Sep 2023 > Project name: Internal Resource Tracker The project is a dockerized Python web backend using django and django rest framework as a RESTful API with PostgreSQL as a backend database. It aims to allow for ease of conducting bench report meetings. #### Contribution - Report file uploads (Excel, CSV) - API endpoint creation. - CRUD uploaded file data - Unit test code - Documentation ### Python Django Developer – Contractor > DoorDash | Jan 2022–Apr 2023 Brief: Update legacy Docker Containers in a monolith Django application with the latest security-patched Python and Debian packages. The project was mainly a Python web backend using Django and Django Rest Framework as a RESTful API, PostgreSQL as a backend database, and Redis as the in-memory cache. The application also used GRPC with protocol buffers for data serialization. The project was containerized Docker in a Debian Linux container. Docker-compose was used for local multi-container management. Application logs were streamed to an Elasticsearch Container for analysis by Apache Kafka. Order transactions and tasks were queued using RabbitMQ. CI/CD was orchestrated using Jenkins. Finally, the project was managed using Agile. The project had five environments: Production, Staging, Sandbox, Testing, and Local. The local environment was for initial testing of the updated Python packages. Once initial testing for a package was completed, pushing the code into GitHub would trigger the test environment to spin up and run in Jenkins. Additional testing could be done in the cloud using the Sandbox or Staging environments. Sandbox environments were similar to the local environment and consisted of 11 docker containers. However, sandbox data was closer to actual production data. The staging environment was a closer replica of the production environment with ~50 containers and used the same data as the sandbox environment. Production, staging, and sandbox environments used Amazon Elastic Container Service (Amazon ECS) for container orchestration. Testing and local environments used docker-compose for container management. #### Tech stack Docker, Jenkins, AWS, Linux, Redis, Elasticsearch, RabbitMQ, Kafka, PostgreSQL, NGINX, Python, Java/Groovy #### Contribution - Led a 3-person team with fellow Python developers, Linux sysadmin, and DevOps engineer. - Created a Python, bash, and docker project that – reads Python requirement files – creates the necessary file changes within the client Django sites – and pushes the changes into a Jenkins CI server. - We used the results to help us understand how much work needed to be done, which allowed us to prioritize and group Python packages for updates. #### Duties - Updated existing code to be compatible with the latest versions of ~400 Python packages. - Tested and updated REST API endpoints. - Wrote Groovy/Java code for Jenkins CI/CD pipelines. - Followed Test Driven Development principles. - Updated client test cases for new package versions. - Updated client documentation to match new package versions. - Mapped the package's old API to the respective package's new API. - Python and shell scripting for automating processes where appropriate. - Created docker-compose files to create environments to run package tests automatically for various packages. - Worked with Amazon Elastic Container Registry (Amazon ECR) to push and pull client containers. - Removed dead code and unneeded additional packages. - Maintained JIRA board. - Documented client code ### ELT Developer / Project Manager – Research Specialist > East Carolina University & Virginia Commonwealth University, United States | Jan 2020–Aug 2021 > Project name: SmartVape / eCigTracker The effect of electronic cigarettes (eCig) has not been largely studied. The lack of concrete measures for nicotine intake is a major area lacking in eCig vaping research. It is a result of the vast differences in eCig devices that range from a 200-watt box mod device to an 8.5-watt Juul. Compounding this issue is the ability for vapers to choose between a huge variety of electronic cigarette liquids(e-liquid) from 60 mg/ml salt nicotine to 3 mg/ml freebase nicotine. This project aimed to create a methodology that would allow researchers to get qualitative data on the device, e-liquid, and vaping habits in an attempt to receive concrete data for intervention resources. In short, vapers would take pictures of their device and e-liquids on the first day of the intervention. Then, on later days, the vapers would take pictures of their device and liquids so that we could see how much and at what wattage e-liquids were vaped. #### Contribution ##### ELT Pipeline: Extract/Load - Web scraped e-commerce websites using Python's Selenium package. - Hosted on Amazon Elastic Compute Cloud (AWS EC2) - Stored scraped JSON information in AWS DynamoDB. - Stored scraped image data in Amazon Simple Storage Service (AWS S3.) ##### Transform - Normalization of device and e-liquid data. - Creation of an SQL database using Amazon Relational Database Services (AWS RDS) from web-scraped data for vapers to easily look up their eCig and e-liquid devices. ##### Project Manager - Requirements planning with Primary Investigators. - UX/UI design of eCigTracker iOS application. - Developed methodology for image analysis. - Developed intervention materials and trained staff on intervention requirements. - Handled interactions and document submissions with the Institutional Review Board (IRB). ### Data Analyst – Research Specialist > Universities of Birmingham, Bristol, and Glasgow, United States | Jan 2020–Aug 2021 > Project name: NAPSACC The use of the Remote Food Photography Method (RFPM) to determine the impact of exercise-based interventions on preschool-aged children's dietary intakes in the United Kingdom. Project Discontinued due to COVID restrictions. #### Contribution - Mapped disparate data sources from the United Kingdom's National Diet and Nutrition Survey (NDNS) to the United States Food and Nutrient Database for Dietary Studies (FNDDS). - Stored data using Amazon's Relational Database Service (AWS RDS) - Used linear regression machine learning algorithm in Python for outlier detection. - Automation of food photo transfers. - Requirement Planning with Primary Investigators. - Creating a food lookup tool for the National Diet and Nutrition Survey using Excel. ### Full Stack Web Developer – Research Specialist > Pennington Biomedical Research Center, United States | Dec 2019–Aug 2021 > Project name: Data Capture and Analysis Platform (DCAP) Our legacy in-house Food Photography analysis application needed to be revamped as it was error-prone and buggy. The DCAP arose from an increased need for collaborators to have easier access to data and help with data transfers. This personal passion project I worked on outside my work hours was made into an official PBRC project. The initial project grew in complexity to meet growing demands from a simple Django application using Django template tags to a Django Rest Framework REST API with a React JavaScript front end to an express Javascript-based API with a React with Redux front end, and finally to a production environment. The final project used a Microsoft SQL Server database on Amazon Relational Database Service (AWS RDS.) It used Swagger to create a REST API for the front to interact with. The front end was developed using React JS and Redux JS with a Bootstrap JS component library. #### Contribution - Data Modeling using Microsoft SQL Server Management Studio. - Hosted site on Amazon Elastic Compute Cloud (AWS EC2) instances. - Creating queries for REST API endpoints using Python's Django Rest Framework package. - Developed test cases for the Django application using Python's unit test package. - Mapped disparate data sources Food and Nutrient Database for Dietary Studies to their respective counterparts in the Standard Reference Database using Python and stored results in Amazon Relational Database Service (AWS RDS.) - Project Requirements Planning - UX/UI design of the application. - Wireframe creation for UX/UI of the project. - Creation of documentation for the project. ### Research Specialist > Jaeb Center for Health Research, United States | Nov 2019–Aug 2021 > Project name: T1Dexi People with type 1 diabetes estimate meal carbohydrate content to accurately dose insulin, yet the protein and fat content of meals also influences postprandial glycemia. We examined the accuracy of macronutrient content estimation via a novel phone app. Participant estimates were compared with expert nutrition analyses performed via the Remote Food Photography Method (RFPM). #### Contribution - Detecting and removing 10,000+ duplicate meal photos using Python's OpenCV package and meal descriptions using Python to reduce manual staffing burden. - Automated file transfers. - Creating a data analysis monitoring tool using Python's Pandas package to ensure timelines are met. - Used linear regression machine learning algorithm in Python for outlier detection. ### Data Analyst – Research Specialist > Ohio State University, United States | Dec 2018–Nov 2020 > Project name: FoodImage The FoodImage smartphone app transmits users’ photographs of food selection and food waste to researchers and includes user-tagged information about waste reasons and destinations. Twenty-four participants were trained to record food waste using FoodImage, food waste diaries requiring visual estimation of waste quantities, and diaries requiring scale weights. In a randomized crossover trial, participants used each method during three staged food-waste scenarios (food preparation, eating, and clean-out). #### Contribution - Disparate data source mapping. Linked Standard Reference 28 (SR28) with the Food and Nutrient Database for Dietary Studies (FNDDS) to get accurate food waste estimations using Amazon Relational Database Service (AWS RDS.) - Displayed results using a Django-based website. Hosted on Amazon Elastic Compute Cloud Instances (AWS EC2). - Used linear regression machine learning algorithm in Python for outlier detection.