Data Engineer – Sports Data & Pipelines

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Mercor is recruiting Data Engineers for long-term projects focused on collecting, structuring, and validating historical and live sports data across **MLB, NFL, NBA, NHL, NCAAF, and NCAAB** for Blitz. Blitz’s mission is to democratize access to sports insights and content that are usually locked behind analysts, code, or expensive tooling. Generic AI tools today struggle in sports because the data is fragmented and contains a lot of nuances. Today, we power a ChatGPT-style sports interface and automated, low-cost previews, recaps, and custom editorials built on our own structured sports, betting, and prediction-market datasets. These projects involve building robust data ingestion systems, web scraping at scale, designing clean relational schemas, and ensuring data quality for complex sports and betting datasets. You are a good fit if you: - Proven ability to **design, clean, and maintain structured relational databases** and build **automated data pipelines** - Hands-on experience with **AWS data infrastructure** (S3, Aurora PostgreSQL, Lambda, Glue, ETL workflows) - **Sports domain knowledge** with experience validating sports/betting data, writing tests for edge cases, and using **GenAI coding tools (Claude Code, Cursor)** to accelerate development - Strong experience with **web scraping at scale**, including anti-bot mitigation (VPN/proxy rotation, rate limiting, fingerprinting avoidance) Here are more details about the role: - You will work on systems that ingest, normalize, and validate live and historical sports data across multiple leagues - We expect a **minimum commitment of 20 hours per week**, with the ability to work up to 40 hours per week depending on performance and project needs - Successful contributions could lead to a FT role at Blitz.

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