Data Engineer, Sports Data & Pipelines

Data Engineer, Sports Data & Pipelines We’re looking for a strong Data Engineer to help expand the data infrastructure for a sports analytics company. The main focus is building and maintaining reliable data pipelines, but we also need someone who is comfortable working directly with the data - cleaning it, structuring it, combining sources, and creating high-quality datasets for analysis and modeling. ### What You’ll Do - Build and maintain Python data pipelines - Connect to APIs, databases, feeds, and other data sources - Collect historical and real-time sports data - Clean, normalize, validate, and structure raw data - Combine multiple data sources into usable datasets - Create research-ready datasets for our team - Investigate missing, inconsistent, or incorrect data - Monitor pipelines and fix issues when feeds break or change - Add new sports, leagues, and data sources over time ### What We’re Looking For - Strong Python and SQL - Experience building production data pipelines - Strong data cleaning and data-wrangling skills - Experience with ETL pipelines - Comfortable working with APIs, JSON, WebSockets, and databases - Experience with Airflow or similar workflow tools - Strong Pandas skills - Able to turn messy raw data into clean, usable datasets - Comfortable with basic data analysis and exploratory work - Able to work independently and take ownership of projects Experience working with sports data is a strong plus. Experience with basketball data is especially valuable, including NBA or college basketball data such as play-by-play, box scores, lineups, player data, and real-time game feeds. ### Location & Collaboration We are primarily looking for candidates based in **North America or Europe**. Strong professional English is required, along with regular overlap with North American working hours for team communication and collaboration. ### When Applying Please keep your response short and answer: 1. Tell us about one data pipeline you personally built. 2. Give an example of a messy dataset you cleaned and structured for analysis. 3. Have you worked with sports or basketball data before? 4. Have you worked with real-time APIs, WebSockets, or live data feeds? Please include links to relevant work or GitHub if available.