Jeanine M.Private profile: Upwork shows its earnings only to signed-in members, so we check it every few weeks, not daily. MRR here is a rough estimate (Upwork's six-month earnings ÷ months active). Last read Nov 5, 2026.

From my time at a telecom company, I have been able to provide a deeper understanding of data pipelines and the process behind moving data from the source to a customer-facing data platform, while also developing data quality monitoring tools using orchestration tools like Prefect. During my first six months, my team and I migrated all of our legacy Hive data processes over to Snowflake using technologies like Talend. I have also acted as a liaison between the data engineering team and other teams within the organization regarding our data platform, while handling dashboard creation and administration for internal customers on our Sisense instance. Recently, I have transitioned into a remote role with an analytics company, where I have spent most of my time building out a database logging infrastructure that includes work with Python, MySQL, and Amazon S3. The program is run through a set of Kubernetes pods that spin up and down depending on the live and upcoming sports games, so it was important to create a controller and deployment environment that would allow this code to run successfully. I have also spent time working on GitHub workflows and Kubernetes deployment configurations. From an analytical standpoint, my graduate degree provided me with many opportunities to work directly with large datasets and create predictive models. My capstone project consisted of scraping online data to build out a database with labor statistics and job descriptions, in which we used natural language processing and modeling techniques like regression and XGBoost to make predictions for salaries of individuals based on their specified job category. I was also provided the opportunity to participate in the inaugural Big Data Bowl mentorship program, in which I built out multiple models using 10 weeks' worth of NFL player tracking data. I also have been fortunate to learn and apply many different data-collecting technologies with my positions at a number of baseball organizations. From seeing pitchers throw in the pitching lab to generate biomechanical data, to personally tracking pitch data using Rapsodo on-field with top high school prospects, I have experienced first-hand how valuable this data can be for gaining insights on player development and scouting. With one organization, I spent the majority of my time creating data visualizations and doing data analysis using R, and I also spent time at anotheroffice working directly with ball-tracking data inside of an Amazon Redshift database to find data anomalies and improve the performance and underlying data that is used all throughout baseball.